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Data analyst practice test number 1.

  • Post published: July 28, 2022

This is an Excel Data Analyst exam, you will be challenged to solve various data analysis issues that Excel Data Analysts face in their everyday work!

You will be using functions such as:

And more…

You can view the answers in the Solution tab! 🙂

Exam level – Intermediate-Advanced

If you prefer to work on this exam using regular Excel – click here to download the Data Analyst Practice exam no. 1

Looking to be a pro? Check Out Coursera’s Google Data Analytics Professional Certificate Here

Having an issue with the formulas' language? check out this post

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Free assessment tests to get you prepared, data analyst: excel interview and assessment test questions.


Data Analyst Job

A data analyst plays a crucial role in the world of data-driven decision-making. Their primary responsibility is to gather, clean, analyze, and interpret data to help organizations make informed decisions, identify patterns, trends, and insights, and solve complex problems. Data analysts work with large datasets, using various analytical tools and techniques to extract meaningful information and create visualizations to present their findings to stakeholders.

Key Responsibilities:

The Data Analyst is responsible for collecting, cleaning, and analyzing large datasets to derive valuable insights and trends. Using statistical methods and data visualization tools, they present their findings to support data-driven decision-making within the organization. The Data Analyst collaborates with stakeholders, interprets complex data, and provides actionable recommendations to enhance business processes and drive strategic initiatives. Key responsibilities of data analysts include:

  • Data Collection: Gather data from various sources, including databases, spreadsheets, and APIs, ensuring data accuracy and reliability.
  • Data Cleaning and Preprocessing: Clean and preprocess the data to remove errors, inconsistencies, and outliers that could affect the analysis.
  • Data Analysis: Utilize statistical methods, data mining techniques, and machine learning algorithms to analyze data and identify trends, correlations, and patterns.
  • Data Visualization: Create visually appealing and easy-to-understand charts, graphs, and dashboards to present the insights effectively.
  • Reporting: Prepare comprehensive reports and presentations to communicate findings to management and other stakeholders.
  • Business Insights: Translate data analysis into actionable business insights and recommendations to support decision-making processes.
  • Database Management: Maintain and manage databases, ensuring data integrity and security.
  • Continuous Learning: Stay updated with the latest trends, tools, and technologies in data analysis to enhance skills and knowledge.

Getting Hired

By preparing yourself with the right skills, education, and experiences, you can increase your chances of landing a data analyst job and thrive in the industry.

To increase your chances of getting hired as a data analyst, consider the following steps:

  • Complete Required Education: Obtain a bachelor's degree in a related field, such as computer science, statistics, mathematics, or data science. Pursuing higher education or relevant certifications can also be beneficial.
  • Gain Experience: Look for internships, freelance projects, or entry-level positions to gain practical experience and build a portfolio showcasing your data analysis projects.
  • Build Technical Skills: Master data manipulation, programming languages, and data visualization tools commonly used in data analysis.
  • Showcase Projects: Create a portfolio of data analysis projects, demonstrating your skills and problem-solving abilities.
  • Networking: Connect with professionals in the industry through networking events, conferences, and online platforms like LinkedIn.
  • Continuous Learning: Stay up-to-date with industry trends, attend workshops, and complete online courses to expand your knowledge.
  • Tailor Your Applications: Customize your resume and cover letter to highlight relevant skills and experiences for each job application.

Required Skills and Qualifications

The Data Analyst position requires strong analytical skills and the ability to think critically to solve complex problems. Proficiency in data manipulation using tools like SQL, Excel, Python, or R is essential, along with knowledge of statistical concepts and techniques for data analysis. Excellent communication skills are necessary to convey findings effectively to non-technical stakeholders, and attention to detail is crucial to ensure data accuracy and reliability.

Typical required skills to get hired as data analyst are:

  • Analytical Skills: A strong aptitude for analyzing data and the ability to think critically and solve problems.
  • Technical Skills: Proficiency in data manipulation using tools like SQL, Excel, Python, R, or other statistical software.
  • Data Visualization: Familiarity with data visualization tools such as Tableau, Power BI, or matplotlib for presenting findings.
  • Mathematics and Statistics: Knowledge of statistical concepts and techniques, including hypothesis testing, regression analysis, and data sampling.
  • Communication: Excellent verbal and written communication skills to convey complex findings to non-technical stakeholders.
  • Attention to Detail: Thoroughness in data cleaning and validation to ensure accuracy and reliability of analysis.
  • Domain Knowledge: Familiarity with the industry or business domain in which the data analyst will be working is advantageous.

There are a lot of different categories of companies that hire data analysts . This is why job seekers should consider their interests, preferred industry, and long-term career goals when targeting employers. Each employer offers unique opportunities and challenges, and joining a company that aligns with one's aspirations can lead to a fulfilling and rewarding career as a data analyst.

The most popular employers that hire data analysts are:

  • Technology Companies (e.g., Google, Microsoft, Amazon): Technology giants like Google, Microsoft, and Amazon are among the largest employers of data analysts. They handle massive amounts of data across various platforms and industries, offering diverse and challenging projects. Working for these companies provides exposure to cutting-edge technologies and a fast-paced, innovative environment, which can greatly enhance a data analyst's skillset and open up future career opportunities.
  • Financial Institutions (e.g., JPMorgan Chase, Goldman Sachs, Citibank): Major banks and financial institutions heavily rely on data analysts to analyze financial data, identify trends, and make strategic decisions. Job seekers in the financial industry can benefit from exposure to complex financial data, gaining insights into market trends and the potential for career growth within the finance sector.
  • Consulting Firms (e.g., Deloitte, Accenture, McKinsey & Company): Consulting firms hire data analysts to support their clients in diverse industries. As a data analyst in a consulting firm, job seekers can expect exposure to a wide range of projects, valuable networking opportunities, and the chance to work with high-profile clients. This experience can lead to accelerated career advancement and the development of a versatile skillset.
  • Healthcare Organizations (e.g., Mayo Clinic, Kaiser Permanente, UnitedHealth Group): Healthcare organizations are increasingly leveraging data analytics to improve patient outcomes and optimize healthcare processes. Data analysts in this sector can make a significant impact by analyzing medical data, patient demographics, and treatment trends. Job seekers interested in healthcare can benefit from a fulfilling career that positively affects people's lives.
  • E-commerce Companies (e.g., Alibaba, eBay, Shopify): E-commerce companies heavily rely on data analysis to understand consumer behavior, optimize marketing strategies, and enhance the user experience. Working for such companies offers exposure to vast datasets and opportunities to influence key business decisions. Data analysts can further develop their skills in customer analytics and contribute to the growth of the rapidly evolving e-commerce industry.
  • Government Agencies (e.g., National Aeronautics and Space Administration (NASA), U.S. Census Bureau, Federal Reserve): Government agencies require data analysts to interpret and manage data for various purposes, including research, policy-making, and public services. Job seekers in the public sector can benefit from job stability, opportunities to work on projects with societal impact, and exposure to diverse datasets.
  • Retail Companies (e.g., Walmart, Target, Amazon Retail): Retailers employ data analysts to optimize inventory management, pricing strategies, and customer satisfaction. Job seekers in the retail industry can gain valuable experience in retail analytics, understanding consumer preferences, and contributing to the success of well-established brands.

10 Interesting Facts

During a job interview, bringing up these interesting facts about the data analyst role can demonstrate your enthusiasm for the position and highlight the exciting and rewarding aspects of being a data analyst. It also shows that you are well-informed about the profession and have a genuine interest in contributing to the organization's success:

  • Impactful Decision-making: As a data analyst, one gets the opportunity to contribute significantly to an organization's decision-making process by providing valuable insights and data-driven recommendations.
  • Diverse Industries: Data analysts can work across various industries, such as finance, healthcare, e-commerce, technology, and government, allowing them to explore different sectors and gain versatile experience.
  • Problem-Solving Variety: The job offers a diverse range of problem-solving challenges, from uncovering patterns in customer behavior to optimizing supply chain efficiency, ensuring that no two days are the same.
  • Continuous Learning: Data analysts are continually exposed to new tools, technologies, and analytical methods, making it a profession that fosters continuous learning and skill development.
  • High Demand: With the increasing emphasis on data-driven decision-making, data analysts are in high demand, providing excellent job stability and numerous career opportunities.
  • Innovative Technologies: Working in the field of data analysis means leveraging cutting-edge technologies like artificial intelligence, machine learning, and data visualization tools.
  • Collaborative Environment: Data analysts often work in cross-functional teams, collaborating with professionals from diverse backgrounds, fostering a dynamic and enriching work environment.
  • Real-World Impact: Data analysts can witness the tangible impact of their work, as their insights often lead to improved business strategies, cost savings, and enhanced customer experiences.
  • Continuous Growth: Data analysts can evolve into specialized roles like business intelligence analysts, data scientists, or data engineers, creating a clear pathway for career advancement.
  • Global Opportunities: The data analysis field offers opportunities to work in various locations and even on remote projects, providing a chance to experience different cultures and work in a global context.

Hiring Process

The hiring process for a data analyst role typically involves several steps to assess the candidate's skills, qualifications, and fit for the position. Job seekers should prepare thoroughly for each stage of the hiring process, showcasing their technical skills, analytical abilities, and suitability for the data analyst role. Researching the company, understanding their data-related challenges, and aligning their experiences with the job requirements can significantly enhance their chances of landing the position. Here's a quick description of each step that would be helpful for a job seeker:

  • Job Application: The first step is to submit a comprehensive job application, including a resume and cover letter. Tailor your application to highlight relevant data analysis skills and experiences.
  • Resume Screening: The hiring team will review submitted resumes to shortlist candidates based on their qualifications, experience, and alignment with the job requirements.
  • Initial Interview: Shortlisted candidates are often invited for an initial interview. This may be conducted via phone or video call and serves as an opportunity for the employer to learn more about the candidate's background and interest in the role.
  • Technical Assessment: Some companies may require candidates to take a technical assessment or skills test to evaluate their data analysis proficiency. This could involve solving data-related problems or working with datasets.
  • In-Person or Virtual Interview: Candidates who perform well in the initial interview and technical assessment may be invited for an in-person or virtual interview. This interview typically dives deeper into the candidate's technical skills, problem-solving abilities, and data analysis techniques.
  • Behavioral Interview: In addition to technical skills, employers also assess a candidate's behavioral fit within the team and organization. Behavioral interviews may focus on past experiences, teamwork, communication, and how the candidate handles challenges.
  • Case Study or Presentation: Some employers may ask candidates to prepare and present a case study or analysis during the interview process. This assesses the candidate's ability to analyze data, draw insights, and present findings coherently.
  • Reference Checks: After a successful interview, the employer may conduct reference checks to validate the candidate's qualifications and work history.
  • Job Offer: If the candidate performs well in all stages of the hiring process, the employer extends a job offer, outlining the details of the position, salary, benefits, and other relevant information.
  • Negotiation and Acceptance: Upon receiving the job offer, the candidate may engage in salary negotiation and other terms before formally accepting the offer and commencing the onboarding process.

Hiring Assessment Tests

Employers use a variety of assessment tests to evaluate candidates for the data analyst role. Employers may use a combination of these tests to gain a comprehensive understanding of a candidate's analytical, technical, and communication skills, as well as their problem-solving abilities and fit for the data analyst role. It's essential for candidates to prepare for these assessments by practicing sample tests and showcasing their strengths during the hiring process. Here are the 5-7 most appropriate tests commonly used for this position:

  • Numerical Reasoning Test: This test assesses a candidate's ability to interpret and work with numerical data, which is essential for data analysts who frequently deal with quantitative information and financial data.
  • Logical Reasoning Test: Data analysts must be skilled in logical thinking and problem-solving. This test evaluates a candidate's ability to analyze and draw logical conclusions based on given information.
  • Psychometric Test: Psychometric tests evaluate a candidate's cognitive abilities, personality traits, and behavioral tendencies, helping employers assess their suitability for the data analyst role and their potential team dynamic.
  • Situational Judgement Test (SJT): SJTs present candidates with hypothetical workplace scenarios and ask them to choose the most appropriate responses. This test assesses a candidate's judgment and decision-making skills, crucial for data analysts who often face complex situations.
  • Microsoft Excel Test: Proficiency in Microsoft Excel is fundamental for data analysts. This test evaluates a candidate's skills in using Excel for data manipulation, analysis, and visualization.
  • Verbal Reasoning Test: Data analysts must be able to comprehend written information and communicate effectively. This test assesses a candidate's ability to understand and evaluate verbal data.
  • Abstract Reasoning Test: Abstract reasoning tests evaluate a candidate's ability to identify patterns and relationships in abstract data, which is relevant for data analysts who work with complex datasets.

Excel Test During Data Analyst Hiring Process

Employers use Excel assessment tests for data analyst roles during the hiring process for several reasons:

  • Relevance to the Role: Excel is a fundamental tool for data analysts, widely used for data manipulation, analysis, and visualization. Assessing a candidate's proficiency in Excel helps employers ensure they have the necessary technical skills for the job.
  • Efficiency and Standardization: Excel assessment tests allow employers to efficiently evaluate a large number of candidates in a standardized manner. It helps them compare candidates objectively based on their Excel capabilities.
  • Data Handling Skills: Data analysts need to work with large datasets, and Excel is commonly used for data cleaning, sorting, and filtering. The assessment test assesses a candidate's ability to handle and manipulate data effectively.
  • Data Visualization: Excel offers various tools for creating charts and graphs to visualize data. Employers may want to assess a candidate's ability to present data in a visually appealing and easy-to-understand manner.
  • Problem-Solving Abilities: Excel assessment tests often include scenarios that require candidates to solve data-related problems, demonstrating their problem-solving and critical thinking skills.

Tips for Passing Excel Assessment Test:

  • Review Excel Functions: Familiarize yourself with common Excel functions such as VLOOKUP, SUMIFS, COUNTIFS, AVERAGE, and IF statements. Practice using these functions to manipulate and analyze data.
  • Data Manipulation Techniques: Practice data cleaning, filtering, and sorting tasks in Excel. Understand how to remove duplicates, convert data formats, and perform other data manipulation operations.
  • Pivot Tables and Charts: Learn how to create pivot tables and pivot charts to summarize and visualize data. Practice using these features to analyze datasets efficiently.
  • Keyboard Shortcuts: Familiarize yourself with essential Excel keyboard shortcuts to improve your efficiency during the assessment test.
  • Practice Sample Tests: Take practice Excel assessment tests online. These tests simulate real assessment scenarios and can help you become familiar with the format and types of questions.
  • Manage Time Wisely: During the assessment test, manage your time effectively to attempt all questions. If you encounter a challenging question, move on and come back to it later if time permits.
  • Read Instructions Carefully: Pay attention to the instructions provided for each question. Understand the requirements before attempting to solve the problem.
  • Double-Check Your Work: Before submitting your answers, review your work to ensure accuracy and correctness.
  • Stay Calm and Focused: Excel assessment tests can be time-pressured, but try to remain calm and focused. Avoid rushing and carefully read each question.

By preparing thoroughly and practicing Excel skills, you can approach the assessment test with confidence and increase your chances of performing well. Demonstrating your proficiency in Excel during the hiring process will showcase your readiness to excel in the data analyst role.

Data Analyst Excel Test Complexity Levels

The complexity levels of Excel assessment tests for data analyst job during the hiring process can vary based on the employer's requirements and the specific role. Generally, Excel assessment tests are designed to evaluate a candidate's proficiency in using the software for data manipulation, analysis, and visualization. Here are some common complexity levels that candidates might encounter:

  • Basic Level: Basic-level Excel assessment tests assess fundamental skills, such as data entry, basic functions (e.g., SUM, AVERAGE), formatting, and simple chart creation. Candidates might be asked to perform basic calculations and use basic formulas.
  • Intermediate Level: Intermediate-level tests involve more advanced Excel functions, including VLOOKUP, HLOOKUP, COUNTIF, and pivot tables. Candidates may need to clean and filter data, perform conditional formatting, and create more complex charts.
  • Advanced Level: Advanced-level Excel assessments challenge candidates with complex data analysis tasks. This may include using array formulas, nested functions, INDEX-MATCH, and advanced data manipulation techniques. Candidates might be asked to analyze large datasets and present insights using advanced charting options.

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Video: Excel Interview Questions For Data Analyst

Take a Data Analyst Free Practice Test

What are the biggest challenges you might run, when doing data analysis?

Even though each situation might be different, below are the typical challenges that data analysts might run into, when trying to solve business problem

  • Importing the data into Excel or other tool used (how to convert data provided in one format and bring it into Excel). This might get even more complicated with larger data volumes.
  • Presence of Duplicate entries and spelling mistakes, reduce data quality.
  • If you are extracting data from a poor source, then this could be a problem as you would have to spend a lot of time cleaning the data.
  • Working with multiple sources: when you combine data from these sources, it may happen that the variation in representation could result in a delay.
  • Quality of the data: if there is incomplete data, then that could be a problem to perform analysis of data
  • Selecting the best vehicle to present results: best way to communicate the findings to decision makers in the format easiest to understand. (reports, charts or dashboards)

How to assess quality of the data model?

Even though the answer might vary, below are some criteria to assess the quality of the data model:

  • Can model be easily consumed by end users (customers) to help facilitate decision making process?
  • Can model support future extensions and future extensions?
  • Can model easily support generation of output format: reports or dashboards?

What is the process of Data Analysis?

Data Analysis the process of collecting, cleaning, interpreting, transforming and modeling data to gather insights and generate reports to help make business decisions. Typically data analysis can be broken down into multiple steps:

  • Data Sources Identification: As part of this step data sources are being identified and ways of extracting and importing the data are being analyzed. This is necessary so that data from master sources can then be used for analysis.
  • Data Import: The data gets collected from various sources and is stored so that it can be cleaned and prepared. In this step, all the missing values and outliers are removed.
  • Analyze Data: Once the data is ready, the next step is to analyze the data. A model is run repeatedly for improvements. Then, the mode is validated to check whether it meets the business requirements.
  • Report Generation: Once the model is implemented and then reports can be generated to help facilitate decision making process.

Excel Formulas and Functions Questions

What are the benefits of using formulas in Excel sheet?

What are the most used functions in Microsoft Excel?

Following are the functions available in Excel for manipulating the data:

  • Common Functions – SUM, AVERAGE, MIN, MAX
  • Math and Financial Functions – SQRT, DEGREE, RAND(), GCD
  • Logical Functions – IF, AND, Or, FALSE, TRUE
  • Date and Time functions – NOW(), DATEVALUE(), WEEKDAY(NOW())
  • Index Match functions – VLOOKUP and INDEX MATCH

How we can calculate average?

We can calculate average by using average formula which will help you to calculate average of any list.

data analyst excel assignment

Which formula is use for sum if there is a condition?

What is the function of SUMIF?

data analyst excel assignment

SUMIF is use to get a sum when there is one condition. (It uses one range, one criteria, and one Sum range)

What is the function of Sumifs? And difference between sumif and sumifs?

data analyst excel assignment

SUMIFS is use when you have multiple criteria to sum. It uses one sum range, and multiple criteria ranges to calculate a sum of a specific criteria’s.

What is the difference between Sumifs and DSum?

Sumif and Dsum both are use to calculate a sum when there is multiple criteria’s. but the main difference between both functions are:

  • Dsum need heading of the table to sum but Sumif not required.
  • Sumif or sumifs need all criteria’s to be written at a time otherwise answer will be zero but the dsum not required all criteria to be written.

Data Analyst Excel How-To Questions

How can you wrap the text within a cell?

How would you hide or show the ribbon?

You can hide or show (minimize or maximize) the ribbon pressing a “^” button at the button right corner of the ribbon (see image below where button is highlighted in red) or by pressing CNTRL F1.

data analyst excel assignment

How to remove leading / trailing spaces in a text in Excel?

To remove of leading, trailing, and double spaces, you need to use the TRIM function.

For example, if you have a text string in cell A1 and you want to remove the spaces, you can use the following formula:

TRIM function doesn't remove single spaces between words.

data analyst excel assignment

Excel Keyboard Shortcuts Questions

What is the keyboard shortcut to put the filter on data in Microsoft Excel ?

Which keyboard shortcut would you use to move to the previous worksheet and next sheet?

What is the keyboard shortcut to Hide/Show Ribbon in Excel?

What is the keyboard shortcut to put the filter on data in Microsoft Excel 2016 or Excel 2019?

Excel Conceptual Questions

How can you sum up the Rows and Column number quickly in the Excel sheet?

By using SUM function, you can get the total sum of the rows and columns, in an Excel worksheet and the short key auto sum is ALT + =.

What are the typical errors in Excel?

Below are the typical errors in Excel:

  • #N/A Error: ‘Value Not Available’ error. You will see this when you use a lookup formula and it can’t find the value (hence Not Available).
  • #DIV/0! Error: Typically this error is displayed when a number is divided by 0. This is called the division error.
  • #VALUE! Error: Typically this error occurs when you use an incorrect data type in a formula.
  • #REF! Error: Typically this error occurs when reference in the formula is no longer valid. This could be the case when the formula refers to a cell reference and that cell reference does not exist (happens when you delete a row/column or worksheet that was referred in the formula).
  • #NAME ERROR: This error is likely to a result of a misspelled function.
  • #NUM ERROR: Number error can occur if you try and calculate a very large value in Excel. For example, =194^643 will return a number error.

Strategies to Prepare & Pass the Test

Remember, preparation and practice are key to succeeding in the data analyst job hiring process. By focusing on enhancing your technical skills, understanding the role's requirements, and showcasing your passion for data analysis, you can stand out as a strong candidate and increase your chances of getting hired.

Preparing for the data analyst job hiring process and assessment tests is crucial to increase your chances of success. Here are some strategies, tips, and tricks to help you excel during the job interview and employment assessment tests:

  • Understand the Job Description: Thoroughly review the data analyst job description to understand the specific skills and qualifications the employer is seeking. Tailor your resume, cover letter, and interview responses to highlight relevant experiences and technical skills.
  • Enhance Technical Skills: Data analysts need proficiency in various tools and programming languages. Focus on improving your skills in SQL, Excel, Python, R, and data visualization tools like Tableau or Power BI. Online tutorials, courses, and practice projects can be beneficial.
  • Practice Data Analysis Projects: Work on sample data analysis projects or participate in online competitions to practice your analytical skills and gain practical experience. Showcase these projects in your portfolio or during the interview to demonstrate your capabilities.
  • Review Statistics and Mathematics: Brush up on statistical concepts and mathematical principles used in data analysis. Familiarize yourself with hypothesis testing, regression analysis, and other common statistical techniques.
  • Practice Assessment Tests: Take practice tests for numerical reasoning, logical reasoning, verbal reasoning, and any other assessment tests that might be part of the hiring process. Practice will help you become more comfortable with the format and improve your performance.
  • Case Study Preparation: If a case study or presentation is part of the interview process, practice presenting your findings concisely and clearly. Focus on providing actionable insights and recommendations based on the data.
  • Research the Company: Learn as much as you can about the company's data-related challenges, projects, and industry trends. Being informed will enable you to ask relevant questions during the interview and showcase your interest in the organization.
  • Review Your Past Work: Be prepared to discuss your past data analysis projects or experiences. Highlight any achievements or challenges you faced and how you overcame them.
  • Improve Communication Skills: Data analysts need to communicate complex information to non-technical stakeholders. Practice explaining technical concepts in a clear and understandable manner during the interview.
  • Be Curious and Ask Questions: Show your curiosity about the company's data infrastructure, data sources, and data-driven decision-making processes. Asking thoughtful questions demonstrates your genuine interest in the role.
  • Time Management: During assessment tests, manage your time effectively to attempt all questions. If you get stuck on a difficult question, move on and come back to it later if time permits.
  • Stay Calm and Confident: During the interview and assessment tests, stay calm and composed. Confidence in your abilities will make a positive impression on the hiring team.

Good luck with your preparation and assessments! Hope you will get hired quickly!

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Next Step: Prepare for the Assessment Test

Preparing for an employment assessment test can be a daunting task, but practicing beforehand can significantly improve your chances of success. Below are 3 reasons why you might consider to get prepared by practicing using materials from JobTestPrep.

Using JobTestPrep practice materials can be an effective way to prepare for the employment assessment test , helping you to build confidence and increase your chances of success. By following these tips and practicing regularly, you can improve your chances and succeed on the test. Select the best Option for the assessment among the tests listed below:

  • Cognitive Ability Test: Cognitive ability tests assess candidates' problem-solving, analytical, and reasoning skills, which are essential for a wide range of positions.
  • Leadership Test: For leadership positions, a leadership test may be used to assess candidates' leadership potential and ability to manage teams and projects effectively.
  • Customer Service Skills Test: Candidates applying for customer service representative positions may undergo this test to evaluate their ability to handle customer inquiries, resolve issues, and deliver excellent service.
  • Verbal Reasoning Test: Company may use this test to evaluate candidates' ability to understand written information and communicate effectively, which is crucial for various roles, including administrative assistants and communication specialists.
  • Behavioral Test: Behavioral tests can provide insights into candidates' work-related behaviors and personality traits, helping the company to identify individuals who align with the organization's values and culture.
  • Microsoft Office Test: Candidates applying for administrative roles or positions involving data analysis and reporting may be tested on their proficiency in Microsoft Office applications, such as Excel, Word, and PowerPoint.
  • Situational Judgment Test (SJT) : SJTs can help evaluate how candidates respond to real-world work scenarios, providing insights into their decision-making and problem-solving abilities in the context of company's operations.

All the best on your job interview and assessment test! Good luck & I truly hope you will get hired soon!

Practice Now

Why Prepare with JobTestPrep Practice Materials

  • Realistic Test Simulation: JobTestPrep provides practice tests that accurately replicate the format and difficulty level of the actual test, allowing you to prepare effectively for the real assessment.
  • Eliminate Stress by Practicing Time Management Practice: Practicing with time limits helps you improve your speed and accuracy during the test, ensuring you can efficiently complete all sections within the allotted time.
  • Get Detailed Feedback and Customized Study Plan: JobTestPrep's practice tests offer detailed feedback and personalized study plans based on your performance, enabling targeted improvement and optimized study time.

Practice Assessment Test Now

Practice Data Analyst Assessment Test

Data Analyst pre-employment assessments can cover a broad range of skills and attributes, depending on the nature of the job. Here are some common types of assessments that might be considered during the hiring process:

  • Cognitive or Aptitude Tests: These assess a candidate's ability to learn, solve problems, and think critically. They often cover areas such as math, logic, and verbal reasoning.
  • Personality Assessments: These tests aim to evaluate a candidate's personality traits, work style, and interpersonal skills. They can provide insights into how well a candidate might fit into a particular work culture.
  • Technical Skills Assessments: For roles that require specific technical expertise, candidates may be asked to complete assessments or tests to demonstrate their proficiency in relevant tools (i.e. Microsoft Excel ), programming languages, or other technical skills.
  • Behavioral Interviews: While not a traditional test, behavioral interviews involve asking candidates about their past behavior in certain situations to predict how they might behave in the future. This assesses qualities like problem-solving, teamwork, and communication skills.
  • Situational Judgement Test (SJT) : These assessments evaluate a candidate's ability to understand and manage their emotions and relate effectively to others in the workplace, based on the situation.
  • Microsoft Office Tests: for some of the job requiring knowledge of Microsoft Office Applications specialized knowledge of Microsoft Word , Microsoft Excel and Microsoft PowerPoint   are conducted

Cognitive Ability Test

Cognitive ability tests assess candidates' problem-solving, analytical, and reasoning skills, which are essential for a wide range of positions.

Numerical Reasoning Test

Used by Employers to determine your numerical reasoning and data interpretation skills.

Personality Test

A personality assessment test evaluates an individual's behavioral traits, preferences, and characteristics to provide insights into their unique personality profile.

Behavioral Test

Behavioral tests can provide insights into candidates' work-related behaviors and personality traits, helping the company to identify individuals who align with the organization's values and culture.

Microsoft Office Test

Candidates applying for administrative roles or positions involving data analysis and reporting may be tested on their proficiency in Microsoft Office applications, such as Excel, Word, and PowerPoint.

SJT (Situational Judgement Test) can help evaluate how candidates respond to real-world work scenarios, providing insights into their decision-making and problem-solving abilities in the context of company's operations.

data analyst excel assignment

Amazon Data Analyst Hiring Assessment Test

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How to Use Microsoft Excel Like a Data Analyst

Jess Feldman

Written By Jess Feldman

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Table of Contents

Meet our expert: stephanie jones, what is excel , pros & cons of excel in data analysis, most important functions of excel for data analysis, data jobs that use excel, can you learn excel at a data analytics bootcamp, the future of excel.

data analyst excel assignment

Since 1987, Microsoft Excel has been used in virtually every office by employees with various job titles. But how is Excel used in data analysis today and can it be learned at a bootcamp ? Thinkful Data Analytics Instructor Stephanie Jones explains the pros and cons of using Excel for data analysis, the top Excel functions that every data analyst needs to know, and Stephanie’s favorite free resources (and datasets) to start learning Excel!

Stephanie received a BS in Math from the University of Denver and a Master’s in Statistics from the University of South Carolina. 

Stephanie has plenty of real-world experience in data too, working as a Revenue and Data Science Analyst and as a consultant for a natural language processing company that focused on AI solutions. Stephanie also helped develop a statistics curriculum for the University of South Carolina! 

Her passion for teaching adult career-changers led Stephanie to become an instructor at Thinkful, where she loves the one-on-one aspect of the program and giving personalized instruction: “I was excited about the opportunity to jump into the bootcamp world because it is an adult learning space that addresses all learning types,” says Stephanie, “We support those who have test anxiety or have life situations that prevent them from turning in a homework assignment everyday. I love that and that's why I chose Thinkful.”

Excel is a spreadsheet software. Excel is a convenient go-to software that is both comprehensible and familiar, and a key part of Excel is how it can be used for ad hoc analysis. Many people are familiar with Excel and that level of comfort is where much of its power stems from.

How do Data Analysts use Excel?

Data analysts use Excel in much the same way that you might use the calculator app on your iPhone. When you aren’t sure what is going on with a dataset, putting it into Excel can bring clarity to the project. 

You don’t have to be a Data Analyst by title to start using Excel, though. If you can type and hit enter, then you can start using Excel. That's the beauty of it – you can self-study and continue to learn. And with Thinkful, you have someone like me who will help you when you are stuck! 

What types of data can be entered into an Excel spreadsheet?

Everyone thinks of financial data with Excel, but it can apply to any industry data – I've even worked on FDA approval cases and human resources datasets! All types of data are appropriate! 

The only exception would be the size of the datasets. Small to medium-size datasets are best for Excel. If a dataset becomes too large, it's cumbersome in Excel. Many times, data analysts will take a look at the underlying data using Excel before they use a heavier application like Python or SQL . 

Give us an example of a problem that a data analyst could solve with Excel!

A scheduling manager could use Excel to record their employee’s schedules. When Joe, your employee, complains about being scheduled on Friday afternoons, you can sit down with Joe and breakdown the data to see if he was being disproportionately scheduled. Using the dataset, you can offer him a straight answer with no emotion attached. That's the best part about data analytics! You receive a final answer at the end that is logical and impossible to deny.

What are the pros and cons of using Excel for data analysis?

Excel is powerful because it’s quick and easy to use, but the downside is that it isn't scalable. As data sizes become larger, we hit limits in our notebook and time limits on our computer. Excel also lacks the ability to automate processes. 

Are there alternatives to Excel? 

Google Sheets is a free alternative to Excel. The collaborative aspect in Google Sheets is great, but it also makes it harder to protect your data from other parts of the company. It is awesome software but Google Sheets isn’t quite as advanced as Excel is and I doubt they will ever catch up. Excel is continuously improving and expanding.

As a Business Analyst, I am typically sharing data with Tableau or Excel. If I needed someone on my team to change something, though, I would never share with Tableau. I would only be comfortable sharing with Excel, because I know my team will be comfortable working with it.

Any function that can break down data is useful for data analysts.We often hear about the Excel VLOOKUP function, which has actually been replaced by XLOOKUP within the newest version of Excel. At Thinkful, we teach students about all three of those important Excel functions, plus Index Match which is a little more difficult to master, but provides a more robust solution. Other Excel functions to break down your data include pivot tables, VLOOKUP, sorting, and filtering. 


data analyst excel assignment

Pivot tables are a way to aggregate data to look at smaller subsets without using manual filters. Without it, you would use the manual filters on every column, check to see if they work, and then use [SUM] functions. All of that is built for you with a pivot table and pivot chart. The best part is that pivot tables are quick and easy to change. If you are in a meeting and someone wants to see 2019 instead of 2020 data, in just one click you can change the filter and all of your visualizations and pivot charts will change. 

data analyst excel assignment

VLOOKUP is the most popular function in Excel to bring together two datasets. For instance, if you have employee numbers in one table and names in a separate table, but want to bring them together, VLOOKUP can help you do that. VLOOKUP is more accessible and logical to use when looking up a small piece of information in a dataset. If you have a larger dataset, you should use alternative methods, like XLOOKUP or Index Match. Unfortunately, Index Match is difficult for someone to learn on their own. In contrast, VLOOKUP has many detailed tutorials that can be found on the web, so you can easily teach yourself.

data analyst excel assignment

XLOOKUP came out in early 2020, and it was created as the solution to VLOOKUP and Index Match's problems. XLOOKUP doesn't work with past versions of Excel, but it is faster and much more user friendly. It will eventually be the industry standard, but because many people have old versions of Excel, VLOOKUP is still important to learn.

What is the Analysis ToolPak in Excel?

data analyst excel assignment

The Analysis ToolPak is a free add-on for Excel feature intended for the intermediate user. It allows users to do different statistical testing within their Excel notebook. With the Analysis ToolPak, you don't need to know the underlying functions for standard deviation. You can run it one time, and it will put out all of those statistics at once. 

Any position with the word "analyst" at the end of it requires Excel! That includes Data Analyst, Business Analyst, Business Operations Analyst, and Reporting Analyst. 

What types of projects have your Thinkful students built using Excel?

For one of our projects, students can use a dataset with housing prices for a certain neighborhood. We had just finished our Tableau section where students learned to make geographic heat maps, which is when you fill parts of the map based on frequency or quantity. One of my students wanted to learn how to use Power Maps in Excel, and together we figured out how to set that up for her. 

Since you took a traditional path into data, do you think it’s possible to learn data analytics at a bootcamp?

Bootcamps are the best way to learn data analysis. I took an expensive and long education path, but my Thinkful graduates are just as qualified as me when it comes to data analytics! At the end of the day, it's about the result-based skills. Thinkful covers the theory enough to use it, but doesn't force its students to memorize the formulas like I had to in a university. Students learn where to find the information and how to use the resources they will have on a job, such as the Thinkful resources on their dashboard (which they will have access to forever). I had to learn these skills on the job with my boss breathing down my neck and a coworker doing the other half of the project. It was stressful, made me feel like an imposter, and created self-doubt. In contrast, Thinkful students receive hands-on work experience throughout the bootcamp. They can be comfortable asking questions and finding solutions in a positive environment. At Thinkful, there is pressure on students to cover the material, but never to learn anything on their own. I tell my students that struggling for more than 20 minutes is not a good use of their time. They need to ask for help. 

How does Thinkful’s data analytics bootcamp incorporate Excel into the curriculum?

Excel is taught throughout the entire data analytics curriculum . Any skill that we build, we build first in Excel, and then we discuss other features and ways to access data outside of Excel. We always relate it back. For example, I teach the Python section of the curriculum. As soon as we hit the Pandas section, I ask students what the Pandas dataframe looks like. The answer is, it's a spreadsheet, which means we are right back to Excel again! Everything connects back to Excel because of the comfort-level. It doesn't mean that it is the easiest, best, or scaleable way, but it's a good place to start. 

Can you teach yourself Excel?

Excel has official and unofficial documentation plus tutorials everywhere online. Excel and information about the software is so ubiquitous because it's been around for so long. It's very easy to find the answer for anything you want to do in Excel because there is always someone who has done it before and documented it. 

For Excel newbies, what resources do you recommend?

Check out Microsoft Excel’s support page for great documentation.

Thinkful offers a Data Analytics prep course that our students may take before the bootcamp begins. It fully prepares the student to begin the Data Analytics immersive program. When a student doesn’t go through the free prep course work, they ask many questions the pre-work explains and that slows down the class.

If you’re looking for data, head to Kaggle . You can also review on people’s projects on there, too!

To practice with Excel, you need data! Where do you recommend finding sample Excel data? has a ton of datasets to play with. 

As a statistician, I recommend Nate Silver FiveThirtyEight who publishes their datasets and some of the code. You can read the article and then review the dataset and code to run it yourself. It's helpful if you want to learn some of the techniques they use. 

The best place to find data is within your everyday life, such as finances, credit card statements, and work schedules. Your home and work life has familiar and comfortable data to work with, so you’ll quickly know if something in your dataset isn’t correct!

Microsoft Excel has been for a very long time. Do you see Excel retaining its importance in the future of the tech workforce? 

We will always have Excel in data analysis, but it's role is already changing. Websites like Amazon collect the data of every click, which means huge datasets. As we get bigger datasets, it won't be feasible to apply them to Excel. Microsoft will ultimately adapt with better software to handle larger loads, but Excel will not change much. It also won’t go away because we teach it to students as early as middle school. Everyone has a basic understanding of its processes and uses it at a basic level in the workplace. Analysts will utilize Jupyter Notebook or Python code for big data projects, but the majority of the workforce will still be reading Excel spreadsheets.

Find out more and read Thinkful reviews on Course Report. This article was produced by the Course Report team in partnership with Thinkful .

About The Author

Jess Feldman

Jess Feldman

Jess Feldman is an accomplished writer and the Content Manager at Course Report, the leading platform for career changers who are exploring coding bootcamps. With a background in writing, teaching, and social media management, Jess plays a pivotal role in helping Course Report readers make informed decisions about their educational journey.

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IBM Data Analytics with Excel and R Professional Certificate

Prepare for a career in data analytics. Gain the in-demand skills and hands-on experience to get job-ready in less than 3 months. No prior experience required.

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Sandip Saha Joy

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Professional Certificate - 9 course series

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Beginner level

No prior experience or degrees required.

What you'll learn

Master the most up-to-date practical skills and knowledge data analysts use in their daily roles

Learn how to perform data analysis, including data preparation, statistical analysis, and predictive modeling using R, R Studio, and Jupyter

Utilize Excel spreadsheets to perform a variety of data analysis tasks like data wrangling, using pivot tables, data mining, & creating charts

Communicate your data findings using various data visualization techniques including, charts, plots & interactive dashboards with Cognos and R Shiny

Skills you'll gain

  • Data Visualization (DataViz)
  • Data Science
  • Spreadsheet
  • Data Analysis
  • Microsoft Excel
  • IBM Cognos Analytics
  • Relational Databases (RDBMS)
  • Data Visualization
  • R Programming
  • Pivot Table

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data analyst excel assignment

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Prepare for a career in Data Analytics

  • Receive professional-level training from IBM
  • Demonstrate your proficiency in portfolio-ready projects
  • Earn an employer-recognized certificate from IBM
  • Qualify for in-demand job titles: Data Analyst, Associate Data Analyst, Business Analyst


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Prepare for the in-demand field of data analytics. In this program, you’ll learn high valued skills like Excel, Cognos Analytics, and R programming language to get job-ready in less than 3 months , with no prior experience or degree needed.

Data analytics is a strategy-based science where data is analyzed to find trends, answer questions, shape business processes, and aid decision-making. This Professional Certificate focuses on data analysis using Microsoft Excel and R programming language . If you’re interested in using Python, please explore the IBM Data Analyst PC. 

This program will teach you the foundational data skills employers are seeking for entry level data analytics roles and will provide a portfolio of projects and a Professional Certificate from IBM to showcase your expertise to potential employers.

You’ll learn the latest skills and tools used by professional data analysts and upon successful completion of this program, you will be able to work with Excel spreadsheets, Jupyter Notebooks, and R Studio to analyze data and create visualizations. You will also use the R programming language to complete the entire data analysis process,  including data preparation, statistical analysis, data visualization, predictive modeling and creating interactive dashboards. Lastly, you’ll learn how to communicate your data findings and prepare a summary report.

This program is ACE® recommended—when you complete, you can earn up to 15 college credits.

Applied Learning Project

You will complete hands-on labs to build your portfolio and  gain practical experience with Excel, Cognos Analytics, SQL, and the R programing language and related libraries for data science, including Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.

Projects include:

Analyzing fleet vehicle inventory data using pivot tables.

Using key performance indicator (KPI) data from car sales to create an interactive dashboard.

Identifying patterns in countries’ COVID-19 testing data rates using R.

Using SQL with the RODBC R package to analyze foreign grain markets.

Creating linear and polynomial regression models and comparing them with weather station data to predict precipitation.

Using the R Shiny package to create a dashboard that examines trends in census data.

Using hypothesis testing and predictive modeling skills to build an interactive dashboard with the R Shiny package and a dynamic Leaflet map widget to investigate how weather affects bike-sharing demand.

Introduction to Data Analytics

Explain what Data Analytics is and the key steps in the Data Analytics process

Differentiate between different data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst

Describe the different types of data structures, file formats, and sources of data

Describe the data analysis process involving collecting, wrangling, mining, and visualizing data

Excel Basics for Data Analysis

Display working knowledge of Excel for Data Analysis.

Perform basic spreadsheet tasks including navigation, data entry, and using formulas.

Employ data quality techniques to import and clean data in Excel.

Analyze data in spreadsheets by using filter, sort, look-up functions, as well as pivot tables.

Data Visualization and Dashboards with Excel and Cognos

Create basic visualizations such as line graphs, bar graphs, and pie charts using Excel spreadsheets.

Explain the important role charts play in telling a data-driven story. 

Construct advanced charts and visualizations such as Treemaps, Sparklines, Histogram, Scatter Plots, and Filled Map Charts.

Build and share interactive dashboards using Excel and Cognos Analytics.

Assessment for Data Analysis and Visualization Foundations

Demonstrate readiness for performing foundational data analysis and data visualization tasks and key steps in the Data Analytics process.

Differentiate between the roles different data professionals play in a modern data ecosystem.

Perform basic Excel tasks for Data Analysis including data quality and data preparation skills.

Exhibit abilities in visualizing data using Excel and proficiency in creating dashboards using Excel and Cognos Analytics.

Introduction to R Programming for Data Science

Manipulate primitive data types in the R programming language using RStudio or Jupyter Notebooks.

Control program flow with conditions and loops, write functions, perform character string operations, write regular expressions, handle errors.

Construct and manipulate R data structures, including vectors, factors, lists, and data frames.

Read, write, and save data files and scrape web pages using R.

SQL for Data Science with R

Create and access a database instance on the cloud

Compose and execute basic SQL statements - SELECT, INSERT, UPDATE, DELETE, CREATE, DROP

Construct SQL statements to filter, sort, group results, use built-in functions, compose nested queries, access multiple tables

Analyze data from Jupyter using R and SQL by combining SQL and R skills to query real-world datasets

Data Analysis with R

Prepare data for analysis by handling missing values, formatting and normalizing data, binning, and turning categorical values into numeric values.

Compare and contrast predictive models using simple linear, multiple linear, and polynomial regression methods.

Examine data using descriptive statistics, data grouping, analysis of variance (ANOVA), and correlation statistics.

Evaluate a model for overfitting and underfitting conditions and tune its performance using regularization and grid search.

Data Visualization with R

Create bar charts, histograms, pie charts, scatter plots, line graphs, box plots, and maps using R and related packages.

Design customized charts and plots using annotations, axis titles, text labels, themes, and faceting.

Create maps using the Leaflet package for R.

Create interactive dashboards using the Shiny package for R.

Data Science with R - Capstone Project

Write a web scraping program to extract data from an HTML file using HTTP requests and convert the data to a data frame.

Prepare data for modelling by handling missing values, formatting and normalizing data, binning, and turning categorical values into numeric values.

Interpret datawithexploratory data analysis techniques by calculating descriptive statistics, graphing data, and generating correlation statistics.

Build a Shiny app containing a Leaflet map and an interactive dashboard then create a presentation on the project to share with your peers.

data analyst excel assignment

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Frequently asked questions

How long does it take to complete the professional certificate.

This Professional Certificate is self-paced. Learners spending 10-12 hours a week can complete within 6 months. For others it can vary between 3 to 9 months.

What background knowledge is necessary?

No prior experience, degrees, statistical or programming knowledge is necessary. Just the passion to self-learn online, comfort & interest in working with numbers and data, and basic computer literacy.

Do I need to take the courses in a specific order?

Yes, it is highly recommended the courses be taken in the order they are presented in the certificate. 

What will I be able to do upon completing the Professional Certificate?

This Professional Certificate is intended to prepare you with skills and confidence to take on an entry level role in Data Analytics or Data Science.  After completing this certificate program you will be able to: Describe the data ecosystem, tasks a Data Analyst performs, as well as skills and tools required to become a successful Data Analys

Explain basic functionality of spreadsheets, and utilize Excel to perform a variety of data analysis tasks like data wrangling, using pivot tables, and data mining

Create various types of visualizations including charts, and dashboards using Excel and Cognos Analytics.

Perform basic R programming tasks such as using common data structures, data manipulation, using APIs, webscraping, and working with R Studio and Jupyter.

Create relational databases and query the data using SQL and R from JupyterLab

Complete the data analysis process, including data preparation, statistical analysis, and predictive modeling.

Communicate data findings using data visualization charts, plots, and dashboards using libraries such as ggplot, leaflet and R Shiny.

How is this Professional Certificate different from the IBM Data Analyst Professional Certificate?

Both this and the IBM Data Analyst Professional Certificate are intnded to prepare you with job-ready skills for an entry level Data Analytics role. The initial three foundational courses, that do not involve any programming, are common between the two programs. The next set of courses in this PC are based on the R Programming language, whereas in the other program they are based on the Python language. Other than that they teach similar concepts and skills. So if you want Data Analysis skills using R, you should enroll in this program whereas is you want Python skills, you can enroll in the ther one. Or you can do both, by first completing one and then the other.

Can I get college credit for taking the IBM Data Analytics with Excel and R Professional Certificate?

Yes. The IBM Data Analytics with Excel and R Professional Certificate recently secured a credit recommendation from the American Council on Education's (ACE) Credit Recommendation, which is the industry standard for translating workplace learning to college credit. Learners can earn a recommendation of 15 college credits for completing the program. This aims to help open up additional pathways to learners who are interested in higher education and prepare them for entry-level jobs.

How do you share your proof of completion with the educational institutions for transferring credit?

To share proof of completion with schools, certificate graduates will receive an email prompting them to claim their Credlybadge, which contains the ACE®️credit recommendation.  Once claimed, you will receive a competency-based transcript that signifies the credit recommendation, which can be shared directly with a school from the Credly platform. Please note that the decision to accept specific credit recommendations is up to each institution and is not guaranteed. 

Where can I find more information on ACE credit recommendations?

Please see Coursera’s ACE Recommendations FAQ Opens in a new tab .

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

What is the refund policy?

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy Opens in a new tab .

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Certificate, you’re automatically subscribed to the full Certificate. Visit your learner dashboard to track your progress.


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42 Excel Interview Questions for Data Analyst

42 Excel Interview Questions for Data Analyst


In this article, we have put together an amazing list of basic, intermediate, and advanced MS Excel interview questions and answers for data analysts and business analysts. These questions are frequently asked in most interviews, and they will definitely help you to be ready to secure your dream job.

Click here to know what does a Data Analyst do?

Now, let’s see 42 excel interview questions and answers for data analyst and business analyst:

Q.1 Explain Microsoft Excel in short.

Microsoft Excel is a software or a desktop application that can be used to store information in the form of rows and columns. It also has features including arithmetic and other mathematical operations, and data visualization. It is available in most operating systems like Mac, Windows, Androids, and so on.

Q.2 Explain the characteristics of Microsoft Excel.

  • MS Excel is user-friendly and also makes data verification and validation easier.
  • Availability of Graphing tools, Shapes, Icons, Charts, and so on.
  • Build-in functions like COUNT, SUM, AVERAGE, DATE, and much more are helpful to a large extent.
  • Data analysis and Data visualization can be achieved with tables, filters, graphs, and so on.

Q.3 What is a spreadsheet?

Spreadsheets are a collection of cells that help you manage the data. A single workbook may have more than one worksheet. All the sheets are available at the bottom of the window, along with their names

Q.4 How can you add new rows and columns to an Excel sheet?

To add new rows and columns select the place where you intend to add them and right-click on it. Then select the Insert option from where you can choose to select an entire row or column.

Q.5 Can we rearrange cells in Excel?

Yes, we can rearrange the cells in Excel. It provides the option of rearranging cells by insertion and deletion in the following ways

  • Shifting cells to the right
  • Shifting cells down
  • Inserting/Deleting an entire row
  • Inserting/Deleting entire column
  • Shifting cells to left
  • Shifting cells up

Q.6 How to add comments/notes in MS Excel cells?

Comments can be added to a specific cell by doing a right-click and selecting the insert the comment option. It is also possible to edit, delete and reply to a comment.

Q.7 What Does the Red Triangle in the Cell’s Corner Indicate?

A red triangle in the upper right corner of a cell indicates that a cell comment has been attached to this cell. If you hover over the cell with your cursor, the comment will be displayed.

Q.8 How do you find duplicates in a column?

To find but not delete duplicates in a column, select the target range of data and navigate to the Style group on the Home tab and click the arrow next to Conditional Formatting. You will then be able to choose Highlight Cell Rules, Duplicate Values, and enter the values you wish to find duplicates of. This will highlight duplicates of the values you entered.

Q.9 How to filter a table?

The filter mechanism is used when you want to display only specific data from the entire dataset. By doing so, there is no change being made to the data. The shortcut to add a filter to a table is Ctrl+Shift+L.

Q.10 What are the ways to extract unique values in Excel?

Excel can extract unique values by temporarily filtering out duplicates, or by permanently deleting duplicates. The first can be achieved by selecting the desired range of data and navigating to Data > Sort & Filter > Advanced. To permanently delete duplicate values and create a list of unique values only, click Data > Data Tools > Remove Duplicates.

Q.11 Define Excel Charts.

A chart in Excel is a feature that allows you to display data through a range of visually intuitive graphs. These charts and graphs can make it easier and quicker to comprehend data compared to just looking at the numbers on the worksheet. Some of the Excel Charts include,

  • Bar graphs 
  • Line graphs
  • Area graph 

Q.12 How Do You Hyperlink in Excel?

To create a link in Excel, select the element you wish to use as the anchor (this can be a cell or an object like a picture). You can then either select Link from the Insert tab, right-click and select Link on the menu, or press Ctrl+K. 

Q.13 What is meant by ribbon in MS Excel?

Users can access most of the common functionalities of Excel using the toolbars and menus that form a part of the ribbon. The user also has the option of customizing the ribbon.  For example , we often add the ‘Developer’ tab on the ribbon. We can remove or add an option with the help of CTRL+F1. The ribbon appears at the top of the application.

Q.14 Explain the significance of Freezing Panes in Microsoft Excel.

Freezing panes are used to have a view of the headers of the columns and rows even if we scroll to a large extent up or down. The freeze pane is achieved by selecting the cell from View and then by selecting one of the freeze options.

Q.15 How to enable Protection in MS Excel?

Protection is achieved in MS Excel to prevent access to certain operations. Protection in MS Excel is achieved in three ways

  • Protection via password on the opening of the workbook.
  • Protection against hide/unhide/add/deletion of worksheets.
  • Window sizes/positions are protected from being modified.

Q.16 What is Relative Cell Address?

The Relative Cell Address is a type of cell reference in Microsoft Excel that is modified and replaced while the Autofill feature is used or copied.

Q.17 What is the Absolute Cell Address?

The absolute cell address is a type of cell reference used when the cell address must remain unchanged while the Autofill feature is used or copied. The ’$’ sign is used to keep the column and row addresses constant.

Q.18 What are Macros in MS Excel?

A macro is a step or a group of steps that are performed more than once. A macro can then be called on whenever necessary to complete the sequence of actions without the user having to type each step manually. This saves valuable time and effort when performing repetitive tasks with larger sets of data. 

Q.19 What Is a Dashboard in Excel?

Dashboards are a feature of Excel used to simplify and condense the presentation of data. Their purpose is to display large amounts of data on one page in a format that is easy to view and comprehend, so multiple factors can be quickly considered during the decision-making process. Dashboards achieve this by making use of various charts, graphs, gauges, and figures that display data in an intuitive way to facilitate the thorough analysis of large sets of data. 

Q.20 Name the types of Report Formats available.

There are three types of formats available for reports

Q.21 What is Data Validation?

Data Validation restricts the type of values that a user can enter into a particular cell or a range of cells. In the Data tab, select the ‘Data Validation’ option present under Data Tools. 

Q.22 What is a Pivot table in Excel?

PivotTable is a powerful tool to calculate, summarize, and analyze data that lets you see comparisons, patterns, and trends in your data. You can use a Pivot Table to analyze numerical data in detail and answer unanticipated questions about your data. A PivotTable is specially designed for: Querying large amounts of data in many user-friendly ways.

Q.23 Explain the characteristics of the Pivot Tables.

The characteristics of the pivot tables are:

  • Customized proper reports can be made.
  • Various data movements and relationships can be determined.
  • Data can be analyzed from different views.
  • Operations like sort, sum, and many other mathematical functions.
  • Links to other data sources can be added.

Q.24 How does a Slicer work in Excel?

To filter data in a Pivot table, we can use slicers. 

  • To create a slicer, go to the Insert tab, and select Slicer present under Filter. 
  • Then, select the list of fields for which you want to create slicers.

Q.25 What do you mean by Pivot Charts? 

The pivot charts are imaged depictions of the pivot table. Pivot tables and Pivot charts are related to each other. In order to have a pivot chart, we need to choose a cell from the pivot table and then select an option for a Pivot Chart. This is available under the Insert menu in the ribbon. Examples of charts include a bar, pie, area, and so on.

Q.26 How do you create dropdown lists in Excel?

To create dropdown lists, follow the given steps:

  • Click on the Data tab present in the ribbon
  • Then, from the Data Tools group, click on Data Validation
  • Navigate to Settings>Allow>List
  • Select the source list array

Q.27 Name the different types of Functions in MS Excel.

Some of the different categories of Functions in MS Excel include:

  • Date and Time
  • Math and Trig
  • Lookup and Reference

Q.28 What is the difference between formulas and functions in Excel?

  • Formulas are defined by the user that is used to calculate some results. Formulas either be simple or complex and they can consist of values, functions, defined names, etc. Example finding Simple interest.
  • A function is a built-in piece of code that is used to perform some particular action. Excel provides a huge number of built-in functions such as SUM, PRODUCT, IF, SUMIF, COUNT, etc.

Q.29 Explain the Operator Precedence of Formulas in Excel.

BODMAS  rules are followed in formulas. The term is known as  B racket,  O rder,  D ivision,  M ultiplication, Addition, and  S ubtraction. If we have a formula that has a bracket and division, then the expression enclosed in the bracket shall be calculated before the division operation.

Q.30 Explain the SUM and SUMIF functions.

SUM  function takes n number of arguments and performs a summation of each one of them. It basically sums up all the numbers in the range of cells. 

SUMIF  function is used to perform summation only if a certain condition is met. Thus SUM and SUMIF functions are almost identical except for the presence of criteria in SUMIF. 

Q.31 Explain the COUNT function.

COUNT function shall return the total count of cells that have numbers in the range of cells mentioned in the parameter.

Q.32 What Is the Difference Between COUNT, COUNTA, COUNTBLANK, and COUNTIF?

All four of these functions count cells within a specified range. However, the criteria a cell needs to meet to be counted differs with each one. COUNT totals the number of cells that contain numerical values, COUNTA totals the number of cells that contain any kind of value, COUNTBLANK simply counts blank cells, and COUNTIF totals based on a condition specified by the user.

Q.33 What is the What-If Analysis in Excel?

The What-If Analysis in Excel is a powerful tool to perform complex mathematical calculations, experiment with data, and try out different scenarios. 

Q.34 Define VLOOKUP in Excel.

VLOOKUP is a built-in function of excel. It is utilized to find and get data from a cell range. This is actually called a vertical lookup. As the name suggests, the data has to be organized vertically. While we are dealing with a large chunk of data, and we need to get hold of certain parts of the data fulfilling certain conditions, then that is the time when VLOOKUP is used.

Q.35 What is a Horizontal Lookup in Microsoft Excel?

Horizontal Lookup or  HLOOKUP  looks for a value from the topmost row of the table horizontally and then moves in a downward direction. It searches for a value in the table’s first row and returns another value in the same column from a row according to the given condition.

Q.36 What is the use of VLOOKUP and HLOOKUP?

Use HLOOKUP when your comparison values are located in a row across the top of a table of data, and you want to look down a specified number of rows. Use VLOOKUP when your comparison values are located in a column to the left of the data you want to find. 

Q.37 How to get the current date in Microsoft Excel?

We can get the current date in MS Excel by using the TODAY ()  function.

Q.38 How does the AND function work in Microsoft Excel?

AND is an inbuilt function that gives TRUE if all the conditions mentioned in the form of parameters are satisfied.

Q.39 How does the IF function work in Microsoft Excel?

In Excel, the IF() function performs a logical test. It returns a value if the test evaluates to true and another value if the test result is false. It returns the value depending on whether the condition is valid for the entire selected range. 

Q.40 How do we wrap a text in Microsoft Excel?

We can wrap a text within a cell by simply selecting the cell, and then clicking on the  Wrap Text  option which is a part of the Home tab.

Q.41 What are the wildcards available in Excel?

Wildcards only work with text data. Excel has three wildcards.

  • *(Asterisk)
  • ? (Question mark)

Q.42 How do you apply a single format to all the sheets present in a workbook?

To apply the same format to all the sheets of a workbook, we have to

  • Right-click on any sheet present in that workbook
  • Then, click on the Select All Sheets option
  • Format any of the sheets and you will see that the format has been applied to all the other sheets as well

In this article, we have discussed the various Excel interview questions that can be asked in an interview for Data Analysts and Business Analysts. You can prepare by referring to the given answers for each of these Excel interview questions. Practicing Excel regularly and going through these Excel interview questions will keep you prepared for the interview.

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Phase 2 - Data Preparation

  • Using SQL, a database will be created to store the data collected in the previous step.

Phase 3 - Insights Generation and Dashboard Building

  • Establish a connection between the database and Tableau/Python/R tools to extract the required data. Generate user-friendly reports according to the business needs and develop the dashboard using Tableau/Power BI.

Analysis of Patient Data (Domain: Healthcare)

  • This project requires learners to analyze the patient data of those suffering from different diseases across various summaries. The facility, chain organizations, and dialysis stations analysis is required to be carried out where the patients are undergoing dialysis. The project also focuses on the payment mode aspect wherein if any discounts or reduction in payments have happened then those are analyzed.

Loan of Customers (Domain: Banking and Finance)

  • In this project, learners analyze the loan given by a financial institution to different customers of varied grades and sub-grade levels. The analysis needs to consider the loan disbursement reasons, funded amount, and revolving balance values for every customer in different states and geolocations. The project requires the customers payment modes and the last payment values.

Employee Retention (Domain: HR analytics)

  • This HR-related project considers the attrition rate of employees working at an organization at different levels. The attrition rate analysis is done with respect to different factors such as monthly income, last promotion year, job role, and work-life balance of every employee of different departments.

Industrial Combustion Energy Use (Domain: Energy)

  • The project requires learners to analyze the usage of different fuels in different facilities in different applications by finding the MMBTu and GWHt values. The fuels used for different geo-locations and for different primary titles are also taken into consideration while doing analysis.

Flights delay analysis (Domain: Aviation)

  • The primary aim of the project is to determine the different reasons behind the delay of flights of various airlines. The analysis needs to consider the number of flights in operation, the number of flights cancelled, and the statistical summary of week-wise, state-wise, and city-wise flight distributions.

Olist Store Analysis (Domain: eCommerce)

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Topics to be covered.

  • Excel: Basics to Advanced
  • MS office Versions(similarities and differences)
  • Interface(latest available version)
  • Row and Columns
  • Keyboard shortcuts for easy navigation
  • Data Entry(Fill series)
  • Find and Select
  • Clear Options
  • Formatting options(Font,Alignment,Clipboard(copy, paste special))
  • Mathematical calculations with Cell referencing(Absolute,Relative,Mixed)
  • Functions with Name Range
  • Logical functions:IF,AND,OR,NESTED IFS,NOT,IFERROR
  • Usage of Mathematical and Logical functions nested together
  • Combination of Arithmatic
  • Lookup functions
  • Data Validation(with Dependent drop down)
  • Number Formatting(with shortcuts)
  • CTRL+T(Converting into an Excel Table)
  • Formatting Table
  • Remove Duplicate
  • Advanced Sort
  • Advanced Filter
  • Conditional formatting(icon sets/Highlighted colour sets/Data bars/custom formatting)
  • Charts:Bar,Column,Lines,Scatter,Combo,Gantt,Waterfall,pie
  • Pivot Reports:Insert,Interface,Crosstable Reports;Filter,Pivot Charts,
  • Slicers:Add,Connect to multiple reports and charts
  • Calculated field, Calculated item
  • Dashboard:Types,Getting reports and charts together, Use of Slicers.
  • Design and placement: Formatting of Tables,Charts,Sheets,Proper use of Colours and Shapes
  • Power Query: Interface, Tabs
  • Connecting to data from other excel files, text files, other sources
  • Data Cleaning
  • Transforming
  • Loading Data into Excel Query
  • Using Loaded queries
  • Merge and Append
  • Insert Power Pivot
  • Similarities and Differences in Pivot and Power Pivot reporting
  • Getting data from databases, workbooks, webpages
  • Add Developer Tab
  • Record Macro:Name,Storage
  • Record Macro to Format table(Absolute Ref)
  • Format table of any size(Relative ref)
  • Play macro by button
  • as command(in new tab)
  • Editing Macros
  • VBA:Introduction to the basics of working with VBA for Excel: Subs, Ranges, Sheets
  • Comparing values and conditions
  • if statements and select cases
  • Repeat processes with For loops and Do While or Do Until Loops
  • Communicate with the end-user with message boxes and take user input with input boxes, User Form
  • Introduction to Databases
  • Introduction to RDBMS
  • Explain RDBMS through normalization
  • Different types of RDBMS
  • Software Installation(MySQL Workbench)
  • Types of SQL Commands (DDL,DML,DQL,DCL,TCL) and their applications
  • Data Types in SQL (Numeric, Char, Datetime)
  • Usage of Case When then to solve logical problems and handling NULL Values (IFNULL, COALESCE)
  • Having Clause
  • COUNT String Functions
  • Date & Time Function
  • Primary key
  • Foreign Key (Both at column level and table level)
  • Full outer join
  • Update & Delete
  • Data Partitioning
  • Indexes (Different Type of Indexes)
  • Views in SQL
  • Procedure with IN Parameter
  • Procedure with OUT parameter
  • Procedure with INOUT parameter
  • User Define Function
  • Window Functions
  • Union, Union all
  • Sub Queries, Multiple Query
  • Handling Exceptions in a query
  • CONTINUE Handler
  • EXIT handler
  • Triggers - Before | After DML Statement
  • What is Tableau ?
  • What is Data Visulaization ?
  • Tableau Products
  • Tableau Desktop Variations
  • Tableau File Extensions
  • Data Types, Dimensions, Measures, Aggregation concept
  • Tableau Desktop Installation
  • Data Source Overview
  • Live Vs Extract
  • Overview of worksheet sections
  • Bar Chart, Stacked Bar Chart
  • Discrete & Continuous Line Charts
  • Symbol Map & Filled Map
  • Text Table, Highlight Table
  • Formatting: Remove grid lines, hiding the axes, conversion of numbers to thousands, millions, Shading, Row divider, Column divider
  • What are Filters ?
  • Types of Filters
  • Extract, Data Source, Context, Dimension, Measure, Quick Filters
  • Order of operation of filters
  • Apply to Worksheets
  • Need for calculations
  • Types: Basic, LOD's, Table
  • Examples of Basic Calculations: Aggregate functions, Logical functions, String functions, Tablea calculation functions, numerical functions, Date functions
  • LOD's: Examples
  • Table Calculations: Examples
  • What is Data Combining Techniques ?
  • Joins, Relationships, Blending & Union
  • Combined Axis
  • Donut Chart
  • Lollipop Chart
  • KPI Cards (Simple)
  • KPI Cards (With Shape)
  • What are Groups ? Purpose
  • What are Bins ? Purpose
  • What are Hierarchies ? Purpose
  • What are Sets ? Purpose
  • What are Parameters ? Purpose and examples
  • Reference Lines
  • Overview of Dashboard: Tiled Vs Floating
  • All Objects overview, Layout overview
  • Dashboard creation with formatting
  • Actions: Filter, Highlight, URL, Sheet, Parameter, Set
  • How to save the workbook to Tableau Public website ?

Power BI 

  • Understanding Power BI Background
  • Installation of Power BI and check list for perfect installation
  • Formatting and Setting prerequisits
  • Understanding the difference between Power BI desktop & Power Query
  • Getting familiar with the interface BI Query & Desktop
  • Understanding type of Visualisation
  • Loading data from multiple sources
  • Data type and the type of default chart on drag drop.
  • Geo location Map integration
  • Finanical sample data in Power BI
  • Preparing sample dashboard as get started
  • Map visual Types and usages in different variation
  • Understanding scatter Plot chart with Play axis and the parameters
  • Understanding the use of AI in power BI
  • AI analysis in power bi using chart
  • Q&A chat bot and the use in real life
  • Hirarchy tree
  • Understanding Column Chart
  • Understanding Line Chart
  • Implementation of Conditional formating
  • Implementation of Formating techniques
  • Loading data from folder
  • Understanding Power Query in detail
  • Promote header, Split to limiter, Add columns, append, merge queries etc
  • Loading multiple data from different format
  • Understanding modelling (How to create relationship)
  • Connection type, Data cardinality, Filter direction
  • Making dashboard using new loaded data
  • Power Query Custom Column & Conditional Column
  • Manage Parameter
  • Introduction to Filter and types of filter
  • Trend analysis, Future forecast
  • Understanding Tool tip with information
  • Use and understanding of Drill Down
  • Visual interaction and customisation of visual interaction
  • Drill through function and usage
  • Button triggers
  • Bookmark and different use and implementation
  • Navigation buttons
  • Introduction to DAX
  • Table Dax, Calculated column, DAX measure and difference
  • Eg:- Calendar, Calendar auto, Summarize, Group by etc
  • Calculated Column
  • Related, Lookup value, switch, Datedif,Rankx,Date functions
  • Dax Measure and Quick Measure
  • Remove filters, Keep filters, All, Allselected, Time Intelligence Functions,Rolling average,YoY, Running total
  • Custom visual and understanding the use of custom
  • Loading custom visual, Pinning visual
  • Loading to template for future use
  • Publishinhg Power Bi
  • Introduction to
  • Schedule refresh
  • Data flow and use power bi from online
  • Download data as live in power point and more

Value Added Courses

Business Statistics

Fundamentals of r, fundamentals of python.

  • SAS(Self Paced)
  • Data Types, Measure Of central tendency, Measures of Dispersion
  • Graphical Techniques, Skewness & Kurtosis, Box Plot
  • Random Variable, Probability, Probility Distribution, Normal Distribution, SND, Expected Value
  • Sampling Funnel, Sampling Variation, Central Limit Theorem, Confidence interval
  • Introduction to Hypothesis Testing
  • Hypothesis Testing (2 proportion test, 2 t sample t test)
  • Anova and Chisquare
  • Data Cleaning(Invalid cells,Blanks,Outliers,Null values)
  • Imputation Techniques(Mean and Median)
  • Scatter Diagram
  • Correlation Analysis
  • Data types(Numeric,Char,Logical,Complex,Vector,List,Matrix,Factor,Array,Dataframe),Relational operators,Logical operators
  • If,Ifelse,For loop,While loop,Repeat,Functions
  • Merging dataframes,Analyzing Iris Dataset using apply functions,dplyr package(Filter,Sel,Arrange),Data visualization using ggplot2,Scatterplot,Histogram,Boxplot
  • Variables,data types(integer,Boolean,Float,List,tuple,string),Opearators in python
  • Dictionaries,Sequence methods,Concatenate,Repetition,len,min,max functions,Index position,Addition and deletion of elements,Reverse,Sorting
  • Sets,re module(findall,search,split,match),if,elifGetting input from user,Identity Operators
  • For,While loops,Functions,Lambda functions,Math module,Calender module,Date & time module
  • Data frame creation using different methods,Using Pandas anlysis on Universities,Salary data sets,Visualization using Matplotlib and Seaborn,Numpy introduction

Introduction to ChatGPT and AI

  • What is ChatGPT?
  • The history of ChatGPT
  • Applications of ChatGPT
  • ChatGPT vs other chatbot platforms
  • Industries using ChatGPT
  • The benefits and limitations of ChatGPT
  • Future developments in ChatGPT technology
  • Ethical considerations related to ChatGPT and AI

Types of AI and Chatgpt architecture

  • What is AI?
  • Types of AI
  • What is Machine Learning?
  • Neural Networks
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Robotics and AI

ChatGPT Functionalities and Applications

  • How does ChatGPT work?
  • ChatGPT Functionalities
  • Drafting emails and professional communication
  • Automating content creation
  • Resume and Cover letter creation
  • Research and information gathering
  • Brainstorming ideas and creative problem solving
  • Best Practices for Using ChatGPT

ChatGPT Prompt Engineering

  • What is Prompt Engineering?
  • Types of Prompts
  • Crafting Effective Prompts
  • Using ChatGPT to generate prompt

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Basic Excel Tutorial

50 Most Important Essential Excel Skills for a Data Analyst

Working frequently on a large amount of data can be devastating. As a data analyst, your main work is to examine business risks, get data and find factors affecting business. For this reason, it is essential to fetch accurate information that managers will use to make decisions.

Here are 50 essential excel skills for a data analyst.

1. Pivot Tables

Pivot tables are a crucial part of data analysis for more than 20 years now. At the moment, it is now impossible to imagine a business running without this tool. Pivot tables summarize, sort, reorganize, group, count, total or average any data in a given table. With a pivot table, you can quickly transform columns into rows and rows into columns.

You can also group by any field, whether columns and do any advance calculations. If you plan to analyze your data using excel, pivot tables are the most valuable tool to use.

2. Data Filters

Data analysts use filters to explore and sub-group data in a table quickly. Filtering helps to hide data that you do not need to use at that particular time. Applying filters helps to narrow down your data to make it easy and convenient. Data filters are effective, especially when you need to filter more than one column. Filtering is more fast and effective as compared to conditional formatting and sorting.

3. Advanced Charts

Advanced excel charts are a great way of creating compelling and accurate data for a business or a company. There are three types of advanced excel charts that you can use as a data analyst to impress your manager and create a rapport with them.

  • Sparklines – These charts are small and lovely in visualizing trends like increase and decrease in value. They include line, column, and win/loss.
  • Gantt charts – Data analysts use these popular charts to track the activities of a project against time. Gantt charts are called bar charts.
  • Thermometer – Thermometer charts are great in visualizing the actual value and the target value of an activity.

These three charts will help you to build unique visualizations which will impress your boss.

The vlookup function is one commonly used and recognizable function in Microsoft Excel. Vlookup helps to look for a value in a given table and returns information from another column relating to that value. It works by combining data from different lists to one or comparing two lists for matching or missing items.

5. Worksheets

A worksheet is a collection of cells organized in particular rows and columns. Usually, each worksheet contains over 1048576 rows and 16384 columns where you can manage your information. Data analysts use worksheets to add and analyze their data on a given workbook. Once you open an excel workbook, there will be tabs at the bottom of the window where you can easily insert a new worksheet.

6. Operators

Operators in Excel specify which calculation to perform on a given distribution of values. Data analysts use these four types of operators daily in Microsoft Excel as follows:

  • Arithmetic operators – Used for addition, subtraction, multiplication, and division.
  • Comparison – Used when comparing values.
  • Text – Used to join two or more texts to form a single text line
  • Reference – Used to reference ranges in your Excel sheet

7. Graphics Data

Graphs are valuable tools in determining the mathematical relationship between two variables. Excel charts make graphical representations of a given set of data. Graphic representation of data involves using symbols such as bars and lines in a bar graph or a line graph. Data analysts will be able to predict the results and perform accurate analysis after seeing the visual image of the data.

8. Data Interpretation

Data interpretation is an essential skill for analyzing data. It helps to acquire necessary information from a pool of irrelevant ones. The management uses the data to make the right decisions concerning business performance, management, and future planning. Data interpretation is helpful for individuals, businesses, organizations, and basically for research and statistics.

9. Lookup Functions

The Lookup function in Microsoft Excel has default behaviors when solving specific problems that make it worthwhile to data analysts. It would be best to use lookup when you need to look in a single/multiple row or column and find another value from the same position in a second row or column.

There are two ways to use lookup as follows:

  • Vector form

Analysts prefer the lookup function because it is less restrictive, simpler to use, easier to audit, and allows for left-to-right and right-to-left procedures.

10. Number Series

Number series in Excel provide accurate inferences for data analysis and critical decision-making. You can represent the data in diagrams, charts, and graphs to describe dates, time, cost, and percentages.

You can easily add numbers to a row of data by dragging and dropping the fill handle to auto-fill a specific column with a series of numbers. Besides that, these number series organize your data to help evaluate your company's progress.

Macros entail a recorded set of actions saved so that they are easily found again. Doing the same set of actions repeatedly allows you to create a macro for doing all activities with a single command. Data analysts use macros to save time when they are working on a task repeatedly in Microsoft Excel. Remember, you can run a macro as many times as you want as long as it is recorded. Creating a macro generally involves recording your mouse clicks and keystrokes.

12. Flat Data Tables

Flat data in Excel is data that contains values in all the cells within a given table. Flat tables, therefore, play a role in eliminating the potential for redundant data. They are very convenient for data entry and analysis, primarily where the data is for use by many contacts. Proper planning of the structure of the tables enables the sorting of information from the existing data.

13. Data Sorting

Sorting data in Excel is an integral part of data analysis. You can arrange a list alphabetically, compile another from the highest to the lowest, or group rows by colors. Sorting helps a researcher organize, visualize quickly, understand their data better, and make the right decisions.

You can sort data by:

  • Text (A to Z or Z to A)
  • Numbers (largest to smallest or smallest to largest)
  • Dates and time (oldest to newest or newest to oldest)
  • A custom list, i.e., large, medium, and small
  • A format that may include cell color, font color, and icon

14. Excel Shortcut Keys

If you do a lot of data analysis, knowing excel shortcut keys is vital in increasing productivity and speeding up your completion of tasks. Impress your colleagues and manager by being able to task without a mouse if you fancy yourself as an excel power user. There are more than 100 Microsoft shortcuts; they include:

  • Save as: Alt -F-A
  • Bold: Ctrl+B
  • Create table: Ctrl+T
  • Add filters: Alt-A-T
  • Sort A-Z: Alt-A-S-A
  • Sort Z-A: Alt-A-S-D
  • Remove duplicates: Alt-A-M
  • Save: Ctrl+S
  • Close workbook: Ctrl+W

These great excel data analysis shortcuts increase efficiency and speed, thus improving your experience.

15. Data Validation

Data validation is a handy tool used to set validations on any data entered into your worksheet. It means that data is accepted only when it meets specific validation criteria. Otherwise, you will get an error message that the values are invalid and rejected. You can create data validation rules on decimals, whole numbers, lists, date, time, custom, and any value. This tool ensures that a business can completely trust the data produced by analysts to be accurate and useful. The different alert styles stop and warn the user from entering invalid data in a cell.

16. Quick Analysis

Data analysts use quick analysis in Excel to format their data into a chart, table, summary formula, highlighted figures, or sparklines with just a few clicks. Formatting data makes it possible to analyze your data quickly and easily instead of going through various tabs.

Tools that you can use for quick analysis include:

  • Formatting – Highlight data using conditional formatting
  • Charts – Chart the selected data
  • Totals – Create standard summary formulas
  • Tables – Summarize data in a table/pivot table
  • Sparklines – Create mini charts placed in single cells

17. Power View

Power View is a data visualization technology in excel that allows you to instantly create interactive charts, graphs, maps, and others that light up your data. It is one of the three data analysis tools that are available in excel. Power view gives data analysts the option of creating data models and presentations that are more sophisticated. Power View lets you dig deeper into your data to find other great patterns for forecasting, clustering, and grouping.

18. Conditional Formatting

Conditional formatting in Microsoft Excel allows you to apply a specific format to cells that only meet certain criteria. This skill helps an analyst visually explore, emphasize, differentiate, and analyze data to detect critical issues and evaluate the trends quickly. The conditional format makes it easier to highlight a range of cells using data bars, color scales, and icons related to different variations in the given data.

19. Managing Page Layout

Managing page layout describes how an analyst can use a group of commands that control how a spreadsheet will appear when printed. Some of the most popular commands in excel are:

  • Repeated titles
  • Headers and footers
  • Orientation and paper size

Other page layout options include page orientation, margin size, print titles, and page breaks. As an analyst, you should modify your workbook's page layout in a way that makes its orientation to be appealing. Adjust and format your workbook to make your data look more professional.

20. Flash fill

Flash fill helps you enter data faster and accurately. It works by making predictions on the rest of the data based on your first entry. The tool automatically fills your data after sensing the pattern you are using. Furthermore, this feature can also remove, insert, format, and reverse text, numbers, and dates. Data analysts use flash fills to help avoid typing errors, especially when dealing with complex formulas.

21. Text Formulas

As a data analyst, your understanding of formulas will help you design better spreadsheets with reduced errors and troubleshooting. Organizing your data to take advantage of formulas is a powerful way to capture and save a working solution. Text formulas will help you build the analysis in your head productively, hence providing more value to the business. Text formulas are vital for data analysis; data analysts should increase their skills with formulas.

22. SUMIF Function

Sumif function is among the most crucial Excel functions that sum up values that meet specific criteria. Sumif can only test one standard at a time, while sumifs test for multiple conditions. One main advantage of the sumif function is its ability to limit the number of spreadsheets so that you have all your data on a single sheet. The sumif formula uses the following arguments:

  • Range – Contains a content of cells where you will apply the criteria
  • Criteria – It is criteria used to determine the cells to add
  • Sum range – The cells that are to add together

Most data analysts prefer sort; however, it is only available to Microsoft 365 subscribers only. As the name suggests, it sorts the contents of a column in ascending or descending order. The sorted array automatically updates after making changes to the original data. The syntax of sort =

  • Array – Range containing the values you want to sort
  • Sort index – Indicates the row or column to sort by
  • Sort order –Represents number 1 for ascending and -1 for descending
  • By col – Indicates the desired sort direction, whether actual or false

24. Countif

The countif function is another mega Excel function for data analysis that counts the number of values that meet specified criteria. Countif is used to count cells that have dates, numbers, and text. i.e., in financial analysis, countif can help count the number of times a manager exceeded their target. Countif function uses the following arguments:

  • Range –It defines one or several cells that you want to count
  • Criteria – It is tested against each of the cells in the supplied range

25. Sparkline

Data analysts need to understand the different types of charts and know their usage to create intuitive visualizations. Sparkline charts are one powerful and influential advanced excel chart that will help you make unique dashboards. Since sparklines are small in size, they can easily be embedded in data grids and dashboards to show the pattern of values like sales. The three different types of sparklines include line, column, and win/loss.

26. Essential Keyboard Shortcuts

Essential window keyboard shortcuts shave a moment off your task, which saves your time a great deal. That is why an analyst must know as many shortcuts as possible. Basic essential keyboard shortcuts include:

  • Ctrl+Z: Undo
  • Ctrl+W: Close
  • Ctrl+A: Select all
  • Alt+Tab: Switch apps
  • Alt+F4: Close apps
  • Ctrl+V: Copy
  • Ctrl+V: Paste

Using shortcuts makes you efficient when handling tasks which boosts productivity. They make you do more with fewer efforts.

27. Removing Duplicate Values

Excel has a built-in tool that helps delete repeated entries in a given set of data. People who work with large data sets in Microsoft excel should be prudent enough to check for errors such as duplicates. Upon using the remove duplicates feature, the duplicate data will be permanently deleted. Most analysts, however, will copy the original data to a different worksheet to prevent losing vital information. Duplicates are annoying and can lead to misinterpretation of data.

The trim function helps remove irregular spacing from data, mainly when imported from other applications. Trim removes all the extra spaces except for single spaces to clean up the cells of a given worksheet. It has only one argument, which is the text from which you want the spaces removed.

29. Concatenate

The concatenate function combines values from multiple cells into one. Since concatenate prompts you for the different values to use, it is suitable for connecting other text parts. In data analysis, you may also need to split data from one cell into separate cells; concatenate function will help you do that. It uses the following arguments:

  • Text 1- The first item to join can be a text value, call reference, or number.
  • Text 2 – It entails the additional text items that you wish to join.

30. Countblank

The blank count function counts the number of empty cells in a given range of cells. It is among the excel statistical functions that are built-in and useful in highlighting blanks. During analysis, contents with blanks can be visually counted using the blank count tool. The syntax of count blank requires only one argument, which is the blank count range. The range represents the range of cells in which blanks to count.

Summits are a function in Microsoft Excel used, to sum up cells that meet multiple criteria. Sumifs sum values when adjacent cells meet criteria based on either dates, numbers, or text. The sumifs syntax relies on the following:

  • Sum range: Cells to add
  • Criteria_range 1: Range of cells to apply criteria 1
  • Criteria1: Determines which cells you need to add
  • Criteria_range 2, criteria2: Contains additional ranges along with their criteria

Excel uses the rank function to return the rank of a number in a list of numbers. You can use RANK.AVG to produce the average rank if more than one number has the same status. By default, data analysts use ranks to arrange data values in ascending order (smallest to largest) then label the slightest value as rank. In a worksheet, rank determines the position of a specific value in an array. This function uses the following arguments:

  • Number: The value which you need to find the rank
  • Ref: A list or array of numbers
  • Order: A number that specifies how the ranking will happen; 0 for descending and 1 for ascending.

In an Excel spreadsheet, values can refer to text, dates, numbers, or boolean.

  • Text: Represents data such as high or low
  • Dates: Shows calendar dates like 20 Nov 2018
  • Numbers: Represents numeric data such as 15 or 20
  • Boolean: It shows the results of a comparison which can be TRUE or FALSE

On the other hand, a value function converts a text into a number. The type of value depends on the data an analyst is referring to.

A data analyst uses the unique function in excel to return a list of unique values in a list or range. The values can be numbers, texts, dates, and times. Excel will automatically create the required size range after you press enter. You can also extract a list of unique values using an array formula, a pivot table, or a power query. The unique syntax relies on the following:

  • Array: Range from which to extract the unique values
  • By col: It shows how to compare and remove; by row is FALSE and by column is TRUE
  • Exactly once: Consists of values that occur once

When you want to count the characters in cells, use the len function for accurate results. The function counts letters, numbers, symbols, and all spaces in a given worksheet. A data analyst can count both characters in one cell and characters in multiple cells with this function.

36. Left/Right

When you use data from other resources, it is usually not ready for analysis, and data scientists have to spend more than 80% of their time cleaning up their data. According to a specific order for each operator, excel calculates the formula from left to right. You can pull a set of numbers out of a cell into another cell in the left function starting from the left point. The right function does the same, although it starts from the rightmost point.

Maxis, just like minifs, allows you to match on criteria except that it focuses on the maximum number. The maxims feature is available on Windows or Mac if you have an office 2019 or Microsoft 365 subscription. The arguments of maxif are:

  • Max range – The actual range of cells from which the user will determine the maximum value
  • Criteria range – Set of cells to be evaluated with the criteria
  • Criteria1 – Entails a number or expression that defines the cells being assessed
  • Criteria range2 – Here, you specify additional ranges

Graphs create a clearer picture of a set of data values as compared to tables. Good decisions are made based on the impact that the images provided. They allow managers to incorporate this information so that it is helpful in analysis and future statements. Types of graph charts in excel include:

  • Column charts
  • Line charts
  • Area charts

39. Power Query

Power query is an essential excel tool that you should know to be a good data analysis whizz. The function makes importing and transforming data from different sources a simple task. Transformation involves cleaning and reshaping your data as needed; you can choose to remove a column, change rows or merge tables. With a power query, you can set up a query once then reuse it after refreshing.

40. Formatting Data

In data analysis, formatting refers to the general appearance or presentation of your task. Data formatting means that as you enter data in a new worksheet, StatsDirect determines the data type and applies the appropriate format. By default, all cells in a worksheet use the general layout, but a data format arranges the data fields for a specific shape. For instance, you can change the appearance of a number or format the alignment, font, and border of the spreadsheet.

41. Slicers

Slicers are used to filter pivot tables quickly or excel tables in Microsoft excel. Data analysts connect multiple slicers to multiple pivot tables to create excellent reports. Slicers are of great help to users who are not familiar with your worksheet. Moreover, they take your workbook to the next level by making your workbook's impressive look and functioning.

42. Power Pivots

Power pivot is one essential excel add-in tool that you can use to perform decisive data analysis actions. The tool enables analysts to manipulate data input and create data models and group tables related to each other. Besides that, power pivot gives you more options for creating presentations from data volumes from different places. It is available in excel 2019, 2016, 2013, 2010, and also in excel office 365.

43. Index Match

Index match performs so many incredible tasks in excel. It is the combination of two powerful functions in excel, index, and match. Index returns the value of a cell in a table basing on the column and row number, whereas match returns the position of a cell in a row or column. This function offers excellent flexibility in data analysis once a user understands how it works.

44. Indirect

The Microsoft excel indirect function returns a reference to change. An analyst can use indirect reference to create a reference specified by a certain text from the cell values. Also, it helps lock the specified cell in a formula, so it does not perform calculations. Indirect references are not affected even after changing or deleting a cell reference.

45. Iferror

Data analysts use the Microsoft excel iferror function to trap and handle errors in a given formula. The function works by returning an alternate value if a formula results in an error. It checks for errors such as #N/A, #VALUE!, #REF!, #NAME, and #NULL. Furthermore, the built-in excel function can be a worksheet function by entering a formula in a worksheet.

46. Xlookup

The MX lookup is a great search tool in Microsoft Excel that allows you to find specific values from various cells. It acts as a modification of the standard lookup functions such as vlookup. Xlookup is the newest model of vlookup with most limitations eliminated. Data analysts use this powerful tool to search for specific entries from an extensive array of data. Xlookup increases speed and productivity.

47. Find/Search

The Microsoft excel search function returns the location of one text string inside another. The search usually allows wildcards and is not always case-sensitive. Find function is case-sensitive and is used to return the position of a specific cell within a text.

48. Drop Down List

The dropdown list is an excel function for data validation that allows its users to select an option from any list of choices. In data analysis, it helps incorporate scenarios and make a worksheet dynamic. Managers and business owners use dropdown lists to ensure that users select an item from the list instead of typing their value. The skill guarantees accuracy and impactful information. It also speeds up the data entry process, especially if the entries are long.

49. Formulas

Formulas are expressions that calculate the value of a given range of cells in excel. There are so many advanced excel formulas that every experienced financial analyst should know. The formulas range from simple primary mathematics to very complex statistical, logical, and engineering tasks like IF functions. Compiled below are the top 10 most useful excel formulas.

  • Sum, Count, Average
  • IF Statements
  • Sumif, Countif, Average
  • Concatenate
  • Conditional formatting
  • Index + Match

Mastering these basic excel formulas is vital for any beginner who is working towards becoming a proficient analyst.

Excel is probably the most convenient and commonly used spreadsheet for data analysis in many corporations, firms, and businesses. Even with larger data sets, Excel makes it easy for users to study and visualize the information more accurately. Learning and using the above excel skills correctly is the key to success for your profession as a data analyst.

50. IF Formulas

There are several formulas in excel, and this can become overwhelming when you are getting started with data analysis. The IF Formula is used in Microsoft Excel to run logical tests, returning one value for an actual result and another for a false result. IF functions test more than one condition, which is helpful if you want to check something on a worksheet. Suppose functions help data analysts to automate decision-making in their spreadsheets.

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Assignment -Introduction to Data Analysis using Excel

data analyst excel assignment

Prepare your presentation with Copilot for Microsoft 365

You’ve been asked to give a new presentation and Copilot for Microsoft 365 can help! For this example, we’ll imagine you’re a professional landscaper and you’ve been asked to present to a local community organization about tulips.

Start from an outline

Often the best way to prepare a new presentation is to create an outline of what you plan to cover. For our example we’ll start with Copilot in OneNote.

Start OneNote.

Navigate to the section where you want your presentation outline to live.

Create a new page for your presentation.

Start Copilot from the ribbon.

The Copilot pane will open on the right, waiting for your prompt. You can use natural language, and the more details you can give Copilot the better your results will be.

You could just enter:

Create an outline for a 45-minute presentation on tulips.

But you’ll get better results if you do a couple more things.

Give it context

Start by telling it what role you want Copilot to play in creating this content.

Act as a professional landscaper. Create an outline for a 45-minute presentation on tulips.

By setting that context first, you let the AI know how you want the content framed.

Give it more details

Try adding to your prompt details about what you want it to cover, and who the audience is.

Act as a professional landscaper speaking to a group of interested community members. Create an outline for a 45-minute presentation on tulips. Include sections on the history of the flower, different types, best time to plant, care and feeding.

Now when you run the prompt, you’ll get a more detailed response.

Tip:  Don’t be afraid to play around with the specifics – add or remove details, change the order, try different contexts.

If you’re happy (or mostly happy) with the draft outline Copilot has created, select the copy button in the Copilot pane and paste the outline onto your OneNote page.

Review and edit

Now you’ll want to add your own touches. Go through the outline and add or remove things as you see fit.

Tip:  OneNote excels as a research tool. Don’t be afraid to add your own notes, copy in content from websites, or add other supporting materials to the page that will be helpful as you prepare your presentation.

Create your handout

When you’re happy with your outline it’s time to create some handouts for the audience. Select your outline in OneNote and copy it to the clipboard. Then open Microsoft Word to a new, blank, document.

Screenshot shows Draft with Copilot in Word.

When Word opens the Copilot dialog should appear. Let’s give it a prompt:

Act as a professional landscaper creating an article for an audience of interested community members. Make it clear, simple, and engaging. Base it off this outline: <paste outline from OneNote>.

Copilot will draft an article for you based on your presentation outline.

Save to OneDrive

Before you spend much time editing your handout, save it to OneDrive. This will make sure your work is saved as you go and it’s key to our final step in preparing the presentation.

Go through the article and make sure that what Copilot added is what you wanted. Edit for voice and tone and make sure any facts it’s added are accurate. Remove anything you don’t want and add anything it missed.

Tip:  You can ask Copilot to add more content if you like. Place the cursor where you want that content to be, then click the Copilot button on the ribbon. Tell it what you want. Add two paragraphs about other plants that look good with tulips.

Go to the Insert tab, select Pictures , and then Online Pictures . Search for “Tulips” and select one or more nice images to make your article more appealing.

Create the slide deck

Now it’s time to let Copilot in PowerPoint get to work.

Open PowerPoint to a new blank deck.

Select Copilot from the ribbon.

In the prompt box type Create presentation from  file.

Copy Link button in Word share tray

Copilot in PowerPoint will build a draft presentation based on your Word document, complete with images and speaker notes.

As always, it’s important that you review the draft Copilot has created. Add any additional slides or information you want, remove any that you don’t.  Add your own expertise where appropriate.

If you want to change any of the images Copilot has added just right-click the image and select Change picture .

Tip:  Practice with Speaker Coach When you’re happy with the presentation you might want to practice it once or twice with Speaker Coach before the big day. For more information see  Rehearse your slide show with Speaker Coach.

Give it a try!

Next time you have a presentation to create let Copilot for Microsoft 365 help you at each step of the way.

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