phd data science italy

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Doctoral school

PhD in Modeling and Data Science

Doctoral School of the University of Torino 

Ph.d. modeling and data science.

Coordinator: prof. Laura SACERDOTE - [email protected]

Key information  

Programme lenght: 3 years Programme location: Turin Department :  Department of Mathematics "G. Peano"

Industrial PhD programme About this programme

Academic training

Training through research and complementary training

Pursuing a PhD degree 

Submitting your application Attendance Topics of the funded projets for the 39th cycle Dissertation and award Further information

Coordinator Laura Sacerdote

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PhD in DATA SCIENCE

• DATA SCIENCE

Presentation

Università degli Studi di Roma "Tor Vergata" - Via Cracovia, 50, 00133 Roma RM

Department of Economics, Management and Statistics  DEMS

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PhD in Economics, Statistics and Data Science

• Fatti e persone
• Working papers

The four-year PhD in Economics, Statistics and Data Science (ECOSTATDATA)  provides the most effective response to the important challenges which nowadays doctoral programmes in the areas of economics, statistics and data analytics, both in Italy and Europe, have to cope with: i) high qualification of the faculty, in terms of teaching abilities and publication records; ii) capability of attracting high quality students; iii) interdisciplinarity; iv) internationalization; v) relations with the non-academic job market; vi) placement of students who have successfully discussed their dissertations.

ECOSTATDATA builds upon the fruitful collaboration among economists, statisticians and data scientists from the Department of Economics, Management and Statistics (DEMS) and the Department of Statistics and Quantitative Methods of the University of Milano-Bicocca (UniMiB), which has started twenty years ago within the BSc in Statistics and Economics, as well as the MSc in Statistics and Economics and is going on with the more recent MSc in Data Science.

Coordinator : Prof.  Matteo Manera

Deputy Coordinator : Prof.  Giorgio Vittadini

NEW!!! Call for applications 2024-2025 (XL cycle)

A.A. 2024-2025 (cycle XL)

Call for Applications

DEMS - University of Milano-Bicocca, Italy

The Department of Economics, Management and Statistics (DEMS) of the University of Milano-Bicocca invites applications to its PhD Programme in Economics, Statistics and Data Science (ECOSTATDATA) for the academic year 2024-25 (XL cycle).

The PhD Programme is articulated in  three curricula , Economics (ECO), Statistics (STAT) and Big Data & Analytics for Business (BIDAB). The length of the PhD Programme is  four years , starting in late October 2024 (the precise starting date will be announced in due course on the PhD website).

The Call of Applications 2024-2025 offers at least 10 fully-funded scholarships .

The selection procedure is regulated by the official Call for Applications (Bando di Concorso), which will be published in the Doctoral School’s and in the PhD programme websites  on April 12, 2024 , with deadline on May 14, 2024.

The official Call for Applications contains detailed information on: i) the documents which each candidate has to submit; ii) structure, contents and timing (May 27, 2024 - June 21, 2024) of the entrance examination; iii) description of the projects related to the scholarships and positions offered.

The official Call for Applications will be published  here .

The PhD programme (in a nutshell...)

Introduction.

ECOSTATDATA belongs to the PhD School of UniMiB, it is affiliated to DEMS, it lasts four years and it is articulated in three curricula, the original two curricula Economics (ECO) and Statistics (STAT), and, starting from cycle XXXVII (academic year 2021-2022), the “new” curriculum Big Data & Analytics for Business (BiDAB) .

The first-year teaching activities are mainly devoted to structured courses (tool courses), which are compulsory. Some of these courses are fixed and specific to each curriculum, some are in common between the three curricula, some other courses are chosen by students within each curriculum.

The second-year teaching activities take the form of less structured courses (elective courses or reading groups).

In general, the first-year courses are offered by “internal” teachers, while second-year courses are often open to the collaboration of foreign instructors (visiting scholars).

The curriculum Economics (ECO)

This curriculum is indicated to students with a strong background in quantitative economics and provides advanced training in econometrics, microeconometrics, time series analysis, microeconomics and macroeconomics.

The curriculum Statistics (STAT)

This curriculum is designed for students with a strong background in statistics, both methodological and applied , and provides advanced training in probability, stochastic processes, statistical inference, Bayesian statistics, statistical learning, statistical modelling, computational statistics and data analysis.

The “new” curriculum Big Data & Analytics for Business (BiDAB)

This curriculum starts from cycle XXXVII (academic year 2021-2022) , and provides students with rigorous training in data management and programming, with focus on: the analysis of large amounts of structured and unstructured data (natural language); the main paradigms of big data and data visualization, based on the use of innovative techniques of machine learning, text and web mining.

“Flexible” and “training” profiles

By means of appropriate sequences of courses, suggested and monitored by the Programme Committee and the supervisors, students are able to build up “flexible” profiles, which are mainly addressed to scientific research, both in universities or in non-academic institutions, at national or international level.

ECOSTATDATA facilitates the interaction between economic, statistical and data management skills by proposing innovative “training” profiles, which are  mainly addressed to the non-academic job market. The “training” profiles aim at:

• offering to the non-academic job market high-level skills which are not currently available;
• attracting students who are interested in ECOSTATDATA as a way to gain new and advanced skills to be immediately spent into that segment of the job market which is not academically- or research-oriented;
• eliciting the collaboration of high-quality national multi-national companies, which are active in human capital investment and are ready to use the instruments of the executive doctorate, the apprenticeship contracts as well as the direct financing of a PhD scholarship on specific research projects.

Length of the programme

The current length of many PhD programmes in economics, statistics and data science in Italy, including the PhD in Economics DEFAP-Bicocca and in Statistics and Mathematical Finance of UniMiB, is three years. This length is insufficient to guarantee that the PhD theses meet the quality standards achieved by the best European PhD programmes. For this reason, ECOSTATDATA lasts four years . This duration is in line with the recent choices of some of the best Italian PhD programmes in economics, statistics and data science, as well as the PhD programmes in this area offered by the most prestigious European academic institutions.

Interdisciplinarity

ECOSTATDATA fosters interdisciplinary research activities, by favouring co-tutorships between economists, statisticians and data scientists, as well as through the “flexible” and “training” profiles.

Relations with the non-academic job market

ECOSTATDATA is particularly active in collaborating with national, multi-national, high-quality and innovation-oriented companies. In particular, ECOSTATDATA is able to: i) offer high-level skills which are not currently available on the non-academic job market; ii) attract students who are interested in ECOSTATDATA as a way to gain new and advanced skills to be immediately spent into that segment of the job market which is not academically- or research-oriented; iii) elicit the collaboration of high-quality national multi-national companies, which are active in human capital investment and are ready to use the modern instruments of the executive doctorate, the apprenticeship contracts as well as the direct financing of PhD scholarships on specific research projects.

Internationalization

The international experience which has flourished within the PhD in Economics DEFAP-Bicocca and the PhD in Statistics and Mathematical Finance of UniMiB, together with the professional networks developed by many faculty members, guarantees that ECOSTATDATA is particularly active in collaborating with prestigious foreign universities, in terms of both students and faculty members exchange programs and joint degrees.

ECOSTATDATA is managed by two bodies:

• the Programme Committee (PC), that is the executive and decision-making board composed by full professors, associate professors and researcher of UniMiB and from other renowned Italian and foreign universities and research institutions;
• the Advisory Board (AB), which collaborates with the PC to organize the teaching and research activities of the programme, is headed by the programme Coordinator and is formed by a limited number of professors and researchers who are representative of the three curricula.

Teaching activities

The teaching activities proposed by ECOSTATDATA are organized during the first two years and differ for each curriculum, although some courses are common. Some economics courses at the first and the second year within the curriculum Economics can be offered jointly with the PhD programme in Economics and Finance of the Catholic University of Milano.

First- year courses

• Curriculum Economics (selected courses)

Mathematics; Computational Statistics I; Econometrics; Microeconometrics; Time Series Analysis; Microeconomics; Macroeconomics; Research Methods; Finance.

• Curriculum Statistics (selected courses)

Mathematical Analysis, Numerical Optimization, Probability, Stochastic Processes, Bayesian Statistics, Statistical Inference, Statistical Learning, Computational statistics II, Statistical Modelling, R for Data Science, Data Management.

• Curriculum Big Data & Analytics for Business (selected courses)

Databases for Structured/Unstructured Data (SQL); Programming in Python; Data Quality and Cleaning for Big Data; Architecture for Big Data Processing; Machine Learning; Cloud & Distributed Algorithm; Data Mining; Natural Language processing and Understanding; Human-Centered AI; Social Media Analysis; Semantic Web; Deep Learning and Computer Vision for Business; Data Visualization & Visual Analysis.

Second-year courses

Second-year courses are mainly “reading groups”, that are built upon the research interests of both instructors and students, and are  articulated into one/two introductory lecture/s and a series of meetings where students critically discuss the readings assigned by the instructor during the initial lecture.

The second-year courses are generally offered during the first part of the second year, in order forstudents to be full-time dedicated to their dissertations as early as possible.

Within each curriculum, a careful selection of courses, monitored by the PC and the student’s supervisor, allows each student to identify a “flexible” profile, which coherent with his/her research interests.

Generally, structured courses have written exams, while the exams associated with the reading groups are more flexible (e.g. written projects and/or oral presentations). The organization of the exams (i.e. form, number of questions, etc.) is decided by the PC and communicated to students at the beginning of each course. 

Monitoring the quality of teaching

The PC runs every year a systematic evaluation of the quality of the courses offered by the PhD programme, by submitting to each student of a given course a detailed questionnaire. Data from the questionnaires are elaborated statistically, sent to each instructor, and discussed within the PC, in order to identify potential problems and solutions.

Admissions to the second year and to years after the second

Admission to the second year is based on the performance of each student in the first-year exams, including the number of “fail” and the number of “resits” each student has been given. Admissions to the third and the fourth years are based on the progresses of the research work. Rules on admission to the second and subsequent years, as well as all the other rules regulating the teaching and research activities of ECOSTAT are formalized by the PC and communicated to each student after enrollment.

Research activities

The Programme Committee (PC) approves the (minimum) number of papers which form a typical PhD dissertation, namely 2. These papers have to be self-contained, independent and potentially publishable on high-quality internationally refereed journals.

Supervision

In order to facilitate students in identifying a sound research project and a suitable supervisor, within the first part of the year the PC organizes a presentation of the research groups which are active among the PC and the Advisory Board (AB) members. Supervisors are asked to systematically monitor the progresses made by their supervisees and periodically report to the PC about the proceedings of their dissertations.

PhD students, especially from the second year, are strongly invited to attend the department seminars organized on a weekly basis at UniMiB. Students of both curricula are also invited to present the progress of their research work in specific seminars, which are part of the student’s evaluation process and, if possible, are jointly organized in order to enhance cross-fertilization between economists, statisticians and data scientists. 

Admission to third and fourth year

Admission to the third and fourth year is formalized by the PC, based on the evaluation of the student’s research work. Admission to the third year takes also into account the performance of each student in the second-year exams.

Admission to external evaluation

Fourth-year students should present, by the end of the year, the final version of their dissertation in front of the PC. If possible, each presentation will be assigned a discussant. The admission to the external reviewers is formalized by the PC, based on the overall evaluation of the PhD thesis.

Thesis discussion

Based on the reports of the external reviewers, students are admitted to the discussion in front of the Evaluation Committee either with minor or major revisions. Students who have successfully defended their dissertation are awarded by the Evaluation Committee the title of “PhD in Economics and Statistics” (students enrolled in cycles XXXIV, XXXV and XXXVI) or the title of “PhD in Economics, Statistics and Data Science” (students enrolled from cycle XXXVII). Students can request to (and obtain from) the Administrative Offices of UniMiB an official document reporting the specific curriculum they have been enrolled in.

ECOSTATDATA takes care of the optimal placement of its students. On this respect, the Programme Committee is very active in: i) providing students with systematic and detailed information on the job market, domestic and international, academic and non-academic; ii) advising and assisting students who intend to apply for academic positions abroad.

Programme committee

Research groups.

The research activities which characterize the PhD programme in Economics, Statistics and Data Science (ECOSTATDATA) are carried out by an active and lively community of junior and senior researchers.

Within DEMS, researchers are organized in clusters , among which the most relevant for ECOSTATDATA are:

- Business, economic and social statistics (coordinator: Prof. Pelagatti)

- Empirical microeconomics and microeconometrics (coordinator: Prof. Manera)

- Experimental and behavioural economics (coordinator: Prof. Stanca)

- Macroeconomics and macroeconometrics (coordinator: Prof. Morana)

- Microeconomics: theory and applications (coordinator: Prof. Gilli)

- Statistics (coordinator: Prof. Ongaro)

- Strategy, organization and innovation (coordinator: Prof. Torrisi)

Detailed information about people involved in each cluster can be found here .

The other two main groups of researchers supporting the programme are affiliated to the Department of Statistics and Quantitative Methods (DiSMeQ) of UniMiB and to the Department of Statistics (DiSTAT), Catholic University of Milano.

Detailed information about the research activities carried on by the DiSMeQ members can be found here .

Detailed information about the research activities carried on by the DiSTAT members can be found here .

Ex-alumni - XXXIV cycle

Supervisor(s): Prof. Silvia Biffignandi, University of Bergamo

Ex-alumni - XXXV cycle

Supervisor(s): Prof. Francesca Greselin, University of Milano-Bicocca; Prof. Ricardas Zitikis, University of Western Ontario, CA

Milano PhD Workshop 2024

The ECOSTATDATA PhD students are happy to announce the second edition of the Milano PhD Workshop , that will be held at the premises of the University of Milano-Bicocca, September 23-27, 2024.

The event is jointly organized with the PhD students in economics of the major universities in the Milanese area.

The program of the event is under construction and will be available shortly.

For details you can contact the local organizers:

PhD students' seminar series 2023-2024

We are very happy to announce this new initiative: the  ECOSTATDATA PhD Seminar Series!

This initiative aims to create a friendly environment where all PhD students at DEMS have the opportunity to present their own research or research proposal to obtain constructive feedback from peers and senior researchers.

Regular reminders before each presentation will be sent, and we really hope you will join this initiative. Your presence and support will be key to make this a success!

The Organizers 

@Angelica Bertucci  

@Ludovica De Carolis  

@Matteo Ferraro  

@Gregorio Ghetti  

@Lorena Popescu  

March 28, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker: Andrea Sorrentino

April 18, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Francesco Ferlaino

Field: Macroeconomics

May 09, 2024 - Aula Seminari (U7 - 2104) 17:00

Speaker:  Luca Danese

Field: Bayesian Nonparametrics

May 16, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Angelica Bertucci

May 23, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Matteo Ferraro

May 30, 2024 Aula Seminari (U7 - 2104) 12:00

Speaker:  Lucia Tommasiello

June 6, 2024 - Aula Seminari (U7 - 2104) 12:00

Speaker:  Mattia Longhi

June 13, 2024 - Aula Seminari (U7 - 2104) 17:00

Speaker:  Claudia Sartirana

June 20, 2024 - Aula Seminari (U7 - 2104) 17:00

Speaker 1:  Ludovica De Carolis

Speaker 2:  Jiefeng Bi

Field: Bayesian Statistics

Past events (selected) 2018-2024

The PhD in Economics, Statistics and Data Science (ECOSTATDATA), the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca, joint with the Italian Society of Econometrics (SIdE), the Free University of Bolzano, the Fondazione Eni Enrico Mattei (FEEM), the International Association of Applied Econometrics (IAAE) and the Rimini Center for Economic Analysis (RCEA), have organized the 4th Italian Workshop on Econometrics and Empirical Economics (IWEEE 2024) - Climate and Energy Econometrics , at the Free University of Bolzano, during the period January 25-26, 2024. 

The PhD in Economics, Statistics and Data Science (ECOSTATDATA), the Center for European Studies (CefES) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Bayesian Structural VAR, held by Prof. Fabio Canova , BI Norwegian Business School, during the period November 9-14, 2023. 

The PhD in Economics, Statistics and Data Science (ECOSTATDATA) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Statistical Learning, held by  Prof. Botond Szabo , Bocconi University, during the period October 5-27, 2021.

The PhD in Economics, Statistics and Data Science (ECOSTATDATA) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Statistical Learning, held by Prof. Omiros Papaspiliopoulos , Bocconi University , during the period October 5-27, 2021. Detailed information on this course (instructor, objectives, programme, references, prerequistes) can be found  here

The PhD in Economics, Statistics and Data Science (ECOSTATDATA), the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca and the Fondazione Eni Enrico Mattei (FEEM), Milano, have organized the summer school on Frontiers of Energy Econometrics , at the Como Lake School of Advanced Studies, during the period September 13-17, 2021. Detailed information on the programme and the application procedure can be found on the summer school website:  https://toee.lakecomoschool.org/

The PhD in Economics and Statistics (ECOSTAT) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized the course Statistical Learning, held by Prof. Rajen Shah , University of Cambridge, during the period October 5-30, 2020. Detailed information on this course (instructor, objectives, programme, references, prerequistes) can be found  here.

The PhD in Economics and Statistics (ECOSTAT) and the Department of Economics, Management and Statistics (DEMS) at the University of Milano-Bicocca have organized and hosted the course Statistical Learning and Big Data, held by Prof.  Sharon Rosset , Tel Aviv University, during the period October 7-18 2019. Detailed information on this course (instructor, objectives, programme, references, prerequistes) can be found  here .

The PhD programme in Economics and Statistics (ECOSTAT) has sponsored the 1 st  CefES International Conference on European Studies, to be held at the University of Milano-Bicocca, Building U6, on June 10th-11 th  2019. Details on this event can be found  here .

The PhD programme in Economics and Statistics (ECOSTAT) has sponsored the International Conference on Econometric Models of Climate Change, held at the University of Milano-Bicocca on August 29th-30 th  2019. Details on this event can be found  here .

Within the Seminar Series DEMS-ECOSTAT,  Prof. Peter M Robinson  (LSE),  has presented the paper titled “Long-range dependent curve time series” (joint with Degui Li and Han Lin Shang). Prof. Robinson is one of the most famous econometricians worldwide and has been in the editorial boards of the most influential journals in econometrics and statistics, from Econometrica to the Journal of Econometrics, from the Journal of the American Statistical Association to the Annals of Statistics. Peter Robinson’s presentation is available  here , while his paper is available  here . This  event  has been held on February 14th 2019, 12.00am, at the Aula del Consiglio, U7, fourth floor, Piazza dell’Ateneo Nuovo 1, 20126 - Milano.

Within the celebrative events of the Twentieth Anniversary of the University of Milano-Bicocca, the Department of Economics, Management and Statistics, in collaboration with the School for Graduate Studies, has organized the International Conference on  The Mathematics of Subjective Probability .  This event was held on September  3rd-5th  2018, at Room U4/2, Piazza della Scienza 1, 20126 - Milano.

Within the celebrative events of its Twentieth Anniversary, the University of Milano-Bicocca, in collaboration with its School for Graduate Studies, has organized the  Lectio Magistralis of Prof. Robert Engle  (NYU University), winner of the 2003 Nobel Memorial Prize in Economic Sciences, on “A Financial Approach to Environmental Risk”. This event was held on June 22nd 2018, 10.00am, at the Auditorium Guido Martinotti U12, Via Vizzola 5, 20126 - Milano.

The Center for European Studies (CefES-DEMS-UNIMIB), the PhD program in Economics and Statistics (ECOSTAT-UNIMIB), and the  Department of Economics, Management and Statistics  (DEMS-UNIMIB) have organized the one-day international conference on  Economic and Financial Implications of Climatic Change .  Two plenary sessions  on the economic and financial implications of climatic change have been organized on June 22 nd  2018, following Prof. Robert Engle’s talk, from 11.30am to 4.45pm, at the Auditorium Guido Martinotti U12, Via Vizzola 5, 20126 - Milano.

XXXVII cycle - Teaching activities - Year II (terms I - II) - reading groups

Reading groups (rg) offered in academic year 2022-23 (xxxvii cycle – ii year) for the curriculum in economics (eco):.

I term (October 2022 – December 2022)

-  Social Network Theory  (Instructor: Prof. F. Panebianco, Catholic University of Milano)

-  Applications of Game Theory  (Instructor: Prof. M. Gilli, University of Milano-Bicocca)

-  Empirical Banking  (Instructor: Prof. Elena Beccalli, Catholic University of Milano)

-  Advanced Asset Pricing and Portfolio Management  (Instructor: Prof. A. Tarelli, Catholic University of Milano)

-  Empirical Corporate Finance  (Instructor: Prof. E. Croci, Catholic University of Milano)

-  Programming in Python  (Instructor: Prof. L. Viarengo, Catholic University of Milano)

II term (January 2023 – April 2023)

-  Spatial Models  (Instructor: Prof. S. Colombo, Catholic University of Milano)

-  Financial Frictions  (Instructor: Prof. D. Delli Gatti, Catholic University of Milano)

-  The Microeconomics of International Trade  (Instructor: Prof. V. Gattai, University of Milano-Bicocca)

-  Innovation and Industrial Evolution  (Instructor: Prof. C. Garavaglia, University of Milano-Bicocca)

-  Structural VAR Models  (Instructors: Proff. V. Colombo, G. Rivolta, Catholic University of Milano)

-  Applied Health Economics and Policy  (Instructors: Proff. G. Turati, E. Cottini, L. Salmasi, Catholic University of Milano)

Note:  the RG for the curriculum ECO are offered jointly with the PhD in Economics and Finance of the Catholic University of Milano (CUM). CUM is in charge of the timetable of each RG, whose updated version can be found  here . 

The following extra-RG are offered by ECOSTATDATA in the II term:

-  Expected Utility and Decision Theory  (Instructor: Prof. G. Cassese, University of Milano-Bicocca)

-  Estimated DSGE Models  (Instructor: Prof. Alice Albonico, University of Milano-Bicocca)

-  Authority and Delegation  (Instructor: Prof. Irene Valsecchi, University of Milano-Bicocca)

Note:  the timetable of the extra-RG is available  here . 

Reading Groups (RG) offered in academic year 2022-23 (XXXVII cycle – II year) for the curriculum in Statistics (STAT):

I term (October 2022 – December 2022)

-  The Dependent Dirichlet Process and Related Models  (Instructors: Proff. F. Camerlenghi, B. Nipoti, University of Milano-Bicocca)

-  Some Issues in Statistical Modelling  (Instructor: Prof. R. Borgoni, University of Milano-Bicocca)

-  Empirical Bayes in Bayesian Inference  (instructor: Prof. S. Rizzelli, Catholic University of Milano)

-  Automated Machine Learning & Neural Architectural Search  (Instructor: Prof. A. Candelieri, University of Milano-Bicocca)

-  Deep Learning  (Instructor: Prof. M. Borrotti, University of Milano-Bicocca)

Note:  the timetable of the RG for the curriculum STAT is available  here . 

Reading Groups (RG) offered in academic year 2022-23 (XXXVII cycle – II year) for the curriculum Big Data & Analytics for Business (BIDAB):

II term (January 2023 – April 2023)

-  Databases for Structured and Unstructured Data – SQL  (POSTPONED) (Instructor: Prof. F. Mercorio, University of Milano-Bicocca)

-  Human-centered AI  (Instructor: Prof. F.M. Zanzotto, University of Roma-Tor Vergata)

Note:  the timetable of the RG for the curriculum BIDAB is available  here . 

XXXVIII cycle - Teaching activities - Year I (terms I - II - III - IV) - courses

The I term teaching activities start on 24 October 2022 and end on 23 December 2022. The I term exam session starts on 9 January 2023 and ends on 13 January 2023.

Note:  the timetable of the I term courses is available  here

The courses/modules offered during the I term for the curriculum Economics (ECO) are:

-  Computational Statistics I  (Instructor: Prof. G. Bertarelli, University of Pisa)

-  Mathematics – Linear algebra  (Instructor: Prof. N. Pecora, Catholic University of Milano)

-  Mathematics I  (Instructor: Prof. D. Visetti, University of Milano-Bicocca);

-  Mathematics II  (Instructor: Prof. F. Cavalli, University of Milano-Bicocca);

-  Mathematics III  (Instructor: Prof. M. Longo, Catholic University of Milano)

The courses/modules offered during term I for the curriculum Statistics (STAT) are:

-  Mathematical Analysis  (Instructors: Prof. C. Zanco, University of Milano; Proff. C.A. De Bernardi, E. Miglierina, Catholic University of Milano)

-  Numerical Optimization  (Instructor: Prof. L. Mascotto, University of Milano-Bicocca) 

The courses/modules offered during term I for the curriculum Big Data & Analytics for Business (BiDAB) are:

-  Programming in Python  (Instructor: Prof. M. Cesarini, University of Milano-Bicocca)

-  Architecture for Big Data Processing  (Instructor: Prof. V. Moscato, University of Napoli)

-  Architecture for Big Data Processing Lab  (Instructor: Prof. G. Sperlì, University of Napoli)

The II term teaching activities start on 16 January 2023 and end on 5 April 2023. The II term exam session starts on 17 April 2023 and ends on 21 April 2023. 

The courses/modules offered during the II term for the curriculum Economics (ECO) are:

-  Econometrics I  (Instructor: Prof. M. Manera, University of Milano-Bicocca)

-  Econometrics I – Tutorials  (Instructor: Dr. C. Cattaneo, European Institute on Economics and the Environment)

-  Econometrics II  (Instructor: Prof. M.L. Mancusi, Catholic University of Milano)

-  Econometrics II – Tutorials  (Instructor: Dr. E. Villar, Catholic University of Milano)

-  Econometrics III  (Instructor: Prof. A. Ugolini, University of Milano-Bicocca)

-  Econometrics III - Tutorials  (Instructor: Dr. D. Valenti, Fondazione Eni Enrico Mattei)

-  Microeconomics I  (Instructor: Prof. M. Mantovani, University of Milano-Bicocca)

-  Microeconomics I – Tutorials  (Instructor: Dr. F. Campo, University of Milano-Bicocca)

-  Microeconomics II  (Instructtor: Prof. M. Gilli, University of Milano-Bicocca)

-  Microeconomics II – Tutorials  (Instructor: Prof. M. Gilli, University of Milano-Bicocca)

-  Microeconomics III  (Instructor: Prof. L. Colombo, Catholic University of Milano)

-  Microeconomics III – Tutorials  (Instructor: Dr. D. Bosco, University of Milano-Bicocca)

-  Microeconomics IV  (Instructor: Prof. P. Bertoletti, University of Milano-Bicocca)

-  Microeconomics IV – Tutorials  (Instructor: Dr. G. Crea, University of Pavia)

Note:  the timetable of the II term courses for the curriculum ECO is available  here .

The courses/modules offered during the II term for the curriculum Statistics (STAT) are:

-  Probability I & II  (Instructor: Prof. F. Camerlenghi, University of Milano-Bicocca)

-  Stochastic Processes  (Instructor: Prof. B. Buonaguidi, Catholic University of Milano)

-  R for Data Science  (Instructor: Prof. A. Gilardi, University of Milano-Bicocca)

-  Statistical Inference I  (Instructor: Prof. A. Caponera, University of Milano-Bicocca)

Note:  the timetable of the II term courses for the curriculum STAT is available  here .

The courses/modules offered during the II term for the curriculum Big Data & Analytics for Business (BIDAB) are:

-  Probability  (Instructor: Prof. A. Di Brisco, University of Piemonte Orientale)

-  Statistical Inference I  (Instructor: Prof. R. Ascari, University of Milano-Bicocca)

Note:  the timetable of the II term courses for the curriculum BIDAB is available  here .

The III term teaching activities start on 26 April 2023 and end on 7 July 2023. The III term exam session starts on 17 July 2023 and ends on 21 July 2023. 

The courses/modules offered during the III term for the curriculum Economics (ECO) are:

- Macroeconomics I (Instructor: Prof. G. Femminis, Catholic University of Milano)

- Macroeconomics II (Instructor: Prof. A. Albonico, University of Milano-Bicocca)

- Macroeconomics III (Instructor: Prof. R. Masolo, Catholic University of Milano)

- Macroeconomics IV (Instructor: Dr. B. Barbaro, University of Milano-Bicocca)

-  Computational Statistics II  (Instructor: Prof. A. Pini, Catholic University of Milano)

-  Research Methods  (Instructors: Prof. T. Colussi, Catholic University of Milano; Prof. K. Aktas, University of Milano-Bicocca)

- Finance I – Empirical Corporate Finance (Instructor: Prof. A. Signori, Catholic University of Milano)

- Finance II – Asset Pricing Theory (Instructor: Prof. A. Sbuelz, Catholic University of Milano)

- Finance III – Banking (Instructors: Proff. M. Migliavacca, F. Pampurini, Catholic University of Milano)

Note:  the timetable of the III term courses for the curriculum ECO is available here .

The courses/modules offered during the III term for the curriculum Statistics (STAT) are:

-  Statistical Inference II  (Instructor: Prof. A. Solari, University of Milano-Bicocca)

- Bayesian Statistics (Instructors: Prof. R. Argiento, University of Bergamo; Proff. B. Nipoti, T. Rigon, University of Milano-Bicocca)

- Data Management (CANCELLED)

Note:  the timetable of the III term courses for the curriculum STAT is available here .

The courses/modules offered during the III term for the curriculum Big Data & Analytics for Business (BIDAB) are:

- Technology and Innovation Management (Instructors: Proff. S. Torrisi, L. D'Agostino, F. Di Pietro, M. Guerzoni, University of Milano-Bicocca)

- Machine Learning (Instructor: Prof. L. Malandri, University of Milano-Bicocca)

- Natural Language Understanding (CANCELLED)

-  Social Media Analytics  (Instructor: Prof. R. Boselli, University of Milano-Bicocca)

Note:  the timetable of the III term courses for the curriculum BIDAB is available here .

The IV term teaching activities start on 4 September 2023 and end on 20 October 2023. The IV term exam session starts on 23 October 2023 and ends on 27 October 2023. 

Note:  the timetable of the IV term courses is under construction and is currently shared with all the ECOSTATDATA students, who can monitor online any updates/modifications.

The courses/modules offered during the IV term for the curriculum Statistics (STAT) are:

- Statistical Learning (POSTPONED)

- Statistical Modelling I (Instructor: Prof. F. Castelletti, Catholic University of Milano)

- Statistical Modelling II (Instructor: Prof. F. Greselin, University of Milano-Bicocca)

- Statistical Modelling III (Instructor: Dr. S. Verzillo, European Commission - Joint Research Center)

- Statistical Modelling IV (Instructors: Prof. F. Pennoni, University of Milano-Bicocca; Prof. F. Bartolucci, University of Perugia)

The courses/modules offered during the IV term for the curriculum Big Data & Analytics for Business (BIDAB) are:

- Statistical Inference II (Instructor: Prof. R. Ascari, University of Milano-Bicocca)

- Explainable AI for Business Value (Instructor: Prof. F. Mercorio, University of Milano-Bicocca)

- Deep Learning and Computer Vision for Business (Instructor: Prof. E. Frontoni, Polytechnic University of Marche, TBC)

XXXVIII cycle - Teaching activities - Year II (terms I - II) - reading groups

Reading groups (rgs) offered in academic year 2023-24 (xxxviii cycle – ii year) for the curriculum economics (eco):.

I term (October 2023 – December 2023) and II term (January 2024 – April 2024)

Note:  the RGs for the curriculum ECO are offered jointly with the PhD in Economics and Finance of the Catholic University of Milano. Detailed information on each RG and its timetable can be found  here . 

Reading Groups (RGs) offered in academic year 2023-24 (XXXVIII cycle – II year) for the curriculum Statistics (STAT):

I term (November 2023 – December 2023) and II term (January 2024 – April 2024)

Note:  the timetable of the RGs for the curriculum STAT is shared online (via Google Calendar) with students officially enrolled in the PhD program. 

- RG Approximate Bayesian Computational Methods (Instructor: Dr. A. Fasano, Catholic University of Milano)

- RG Automated Machine Learning & Neural Architectural Search (Instructor: Prof. A. Candelieri, University of Milano-Bicocca)

- RG Spatio-temporal Data (Instructors: Prof. R. Borgoni, Dr. P. Maranzano, University of Milano-Bicocca)

- RG Some Issues on Statistical Modelling (Instructor: Prof. R. Borgoni, University of Milano-Bicocca)

- RG Deep Learning (Instructor: Prof. M. Borrotti, University of Milano-Bicocca)

Reading Groups (RGs) offered in academic year 2023-24 (XXXVIII cycle – II year) for the curriculum Big Data & Analytics for Business (BIDAB):

I term (November 2023 - December 2023) and   II term (January 2024 – April 2024)

Note:  the timetable of the RGs for the curriculum BIDAB is shared online (via Google Calendar) with students officially enrolled in the PhD program.

- RG Natural Language Processing (Instructor: Dr. A. Seveso, University of Milano-Bicocca)

- RG Generative AI (Instructor: Dr. Navid Nobani, University of Milano-Bicocca)

XXXIX cycle - Teaching activities - Year I (terms I - II) - courses

The I term teaching activities start on 23 October 2023 and end on 22 December 2023. The I term exam session starts on 8 January 2024 and ends on 12 January 2024.

Note:  the timetable of the I term courses is shared online (via Google Calendar) with all students officially enrolled in the PhD program.

- Microeconomics I (Instructor: Prof. Marco Mantovani, University of Milano-Bicocca)

-  Mathematical Analysis I-II-III  (Instructors: Prof. J. Somaglia, Polytechnic of Milano; Proff. C.A. De Bernardi, E. Miglierina, Catholic University of Milano)

-  Architecture for Big Data Processing & Lab  (Instructors: Proff. V. Moscato and G. Sperlì, University of Napoli)

The II term teaching activities start on 15 January 2024 and end on 27 March 2024. The II term exam session starts on 8 April 2024 and ends on 12 April 2024.

Note:  the timetable of the II term courses is shared online (via Google Calendar) with all students officially enrolled in the PhD program.

- Microeconomics II (Instructor: Prof. M. Gilli, University of Milano-Bicocca)

- Microeconomics III (Instructors: Prof. L. Colombo and Dr. M. Magnani, Catholic University of Milano)

- Microeconomics IV (Instructors: Prof. P. Bertoletti, University of Milano-Bicocca, and Dr. G. Crea, University of Pavia)

- Econometrics I (Instructors: Prof. M. Manera, University of Milano-Bicocca, and Dr. C. Cattaneo, European Institure on Economics and the Environment)

- Econometrics II (Instructors: Prof. A. Ugolini, University of Milano-Bicocca, and Dr. D. Valenti, Polytechnic of Milano)

- Econometrics III (Instructors: Prof. M.L. Mancusi and Dr. E. Villar, Catholic University of Milano)

The courses/modules offered during the II term for the curriculum Statistics (STAT) are:

- Probability I-II (Instructor: Prof. F. Camerlenghi, University of Milano-Bicocca)

- Stochastic Processes (Instructor: Prof. B. Buonaguidi, Catholic University of Milano)

- Statistical Inference I (Instructor: Dr. A. Caponera, Luiss Guido Carli University) 

- R for Data Science (Instructor: Dr. A. Gilardi, Polytechnic of Milano)

The courses/modules offered during the II term for the curriculum Big Data & Analytics for Business (BiDAB) are:

- Probability (Instructor: Prof. A. Di Brisco, University of Insubria)

-  Statistical Inference I (Instructor: Dr. R. Ascari, University of Milano-Bicocca)

The Ph.D. program in Data Analytics and Decision Sciences (DADS) aims at training highly qualified senior data analysts and data managers capable of carrying out research at universities, international institutions, tech and financial companies, regulatory authorities, and other public bodies.

The program stems from the cooperation between three departments: Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Dipartimento di Ingegneria Gestionale (DIG), Dipartimento di Matematica (DMAT), and the Center for Health Data Science at Human Technopole. It allows the enrolled students to work in a highly interdisciplinary environment with strong connections to international research centers and private companies. The program provides successful candidates with the opportunity to acquire a high degree of professional expertise in specific scientific and technological fields.

The program lasts three years: upon its successful completion and final exam, candidates will be awarded the title of Ph.D. in Data Analytics and Decision Sciences. The first year is devoted to the courses that build the broad competence and the strong interdisciplinary set of skills required by data analytics. The next two years focus on the development of the Doctoral thesis. Students are required to spend at least one semester in a research institution abroad, taking advantage of the network of international collaborations of the three departments involved in the program.

All the students enrolled in the DADS Doctoral Program are supported by scholarships from public institutions and private companies. A call for applications to Ph.D. positions and scholarships is issued around April (see http://www.dottorato.polimi.it/en/looking-for-a-phd/call-for-positions-and-scholarships/ for more details).

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65 Best universities for Data Science in Italy

Updated: February 29, 2024

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Below is a list of best universities in Italy ranked based on their research performance in Data Science. A graph of 715K citations received by 29.9K academic papers made by 65 universities in Italy was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. Polytechnic University of Milan

For Data Science

2. University of Bologna

3. University of Pisa

4. Sapienza University of Rome

5. Federico II University of Naples

6. Polytechnic University of Bari

7. University of Milan

8. University of Padua

9. University of Bari

10. Polytechnic University of Turin

11. University of Rome Tor Vergata

12. University of Turin

13. University of Calabria

14. University of Trento

15. University of Catania

16. University of Florence

17. University of Cagliari

18. University of Pavia

19. University of Genoa

20. University of Milano-Bicocca

21. University of Parma

22. University of Salerno

23. University of Modena and Reggio Emilia

24. Free University of Bozen

25. University of Campania Luigi Vanvitelli

26. University of Aquila

27. Polytechnical University of Marche

28. University of Perugia

29. University of Messina

30. University of Udine

31. University of Trieste

32. University of Brescia

33. University of Verona

34. University of Siena

35. Catholic University of the Sacred Heart

36. Mediterranean University of Reggio Calabria

37. University of Salento

38. Bocconi University

39. Ca' Foscari University of Venice

40. Roma Tre University

41. University of Insubria

42. Sant'Anna School of Advanced Studies

43. University of Palermo

44. University of Sannio

45. University of Catanzaro

46. University of Cassino and Southern Lazio

47. University of Camerino

48. University of Ferrara

49. Parthenope University of Naples

50. University of Bergamo

51. University of Sassari

52. University of Eastern Piedmont

53. IUAV University of Venice

54. G. d'Annunzio University of Chieti

55. LUISS University

56. Campus Bio-Medico University of Rome

57. Basilicata University

58. Carlo Cattaneo University

59. Normal School of Pisa

60. Carlo Bo University of Urbino

61. University of Molise

62. University of Tuscia

63. Vita-Salute San Raffaele University

64. University of Foggia

65. University of Macerata

The best cities to study Data Science in Italy based on the number of universities and their ranks are Milan , Bologna , Pisa , and Rome .

Computer Science subfields in Italy

151 data-science-phd positions in Italy

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Call for prospective PhD Student applications a.y. 2024-2025 for Economics, Management ad Political and Social Sciences

3 Apr 2024 Job Information Organisation/Company ALMA MATER STUDIORUM - Università di Bologna Research Field Economics Management sciences Political sciences Researcher Profile First Stage Researcher

3 years scholarship for PhD Program in Management, Finance And Accounting – 40th cycle 2024-2025 at LIUC

Engineering and Management- Business Administration.Application Deadline is May 22, 2024 at 5:00 p.m. CEST.Complete information and instructions for sending the application form are available on LIUC web site

Notice of Competition for the admission to PhD programmes A.Y. 2024/2025 - Cycle XL, University of Cagliari

27 Mar 2024 Job Information Organisation/Company Università degli Studi di Cagliari Department PhD and Professional Master Office at the Post Lauream Secretariat Research Field Other Researcher

PhD Position: Fluid Gap Electrostatic Transducers for Soft Robotics and Energy Harvesting

28 Mar 2024 Job Information Organisation/Company Sant'Anna - School of Advanced Studies Pisa Department Institute of Mechanical Intelligence Research Field Engineering Researcher Profile First Stage

National Quantum Science and Technology Institute , NQSTI

3 Apr 2024 Job Information Organisation/Company CNR ISTITUTO DI NANOTECNOLOGIA Research Field Engineering » Electronic engineering Engineering » Materials engineering Physics Researcher Profile

7 Fully Funded PhD Scholarships in Economics and Finance

The University of Trento is now inviting applications from qualified candidates for 7 PhD Scholarships in the PhD programme in Economics and Finance – jointly with the Free University of Bolzano

4 PhD fellowships available at SISSA - Physics and Chemistry of Biological Systems

programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD in Physics and Chemistry of Biological Systems SISSA welcomes applications for 4 PhD fellowships in

BANDO N. 400.12 IIT PNRR

29 Mar 2024 Job Information Organisation/Company Consiglio Nazionale delle Ricerche - CNR Department Istituto di Informatica e Telematica Research Field Information science Researcher Profile

PhD in Structural Health Monitoring-Driven Decision Making for Optimal Bridge Management

1 Mar 2024 Job Information Organisation/Company Politecnico di Milano Department DABC Research Field Engineering Researcher Profile First Stage Researcher (R1) Country Italy Application Deadline 15

Full time researcher (1 staff Unit) - PNRR National Quantum Science and Technology Institute project, NQSTI,

2 Apr 2024 Job Information Organisation/Company CNR Institute of Nanotechnology Department Sede di Lecce Research Field Physics Engineering » Materials engineering Engineering » Electronic

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Statistical sciences.

The PhD Course in Statistics aims at training specialists in the fields of data management and analysis and leads to a wide range of career opportunities, both in academic and research institutions.

  Find out more

The doctoral programme aims to train international specialists in the fields of data management and data analysis. The programme leads to a wide range of career opportunities, both at academic level and in other highly qualified research institutions. The study project of the doctoral course is carried out according to the following steps. Start of activities. On arrival, students are informed of the regulations and the course programme for the first few months. Each student is assigned a tutor, who is available to help and guide them in their choice of research topic for the second and third year. First-year programme. The basic first-year programme comprises a core of five compulsory courses covering advanced mathematics, probability theory, computer programming, statistical theory and modelling. In addition, a number of specialised modules are offered, covering a range of more advanced topics. A side objective of the courses during the first year is to train students in group work, seminar presentations and the preparation of scientific papers. At the end of the first year, the Academic Board assesses doctoral students for admission to the second year. Admission is conditional on achieving a satisfactory level in the first year's activities. For each of the five core modules, assessment is based on a final examination. By September of the first year, students admitted to the second year propose to the Academic Board their research programme to be developed during the second and third years. Students may join local research groups or start an independent research project. The PhD programme and the Department of Statistical Sciences can provide a suitable research environment for this. Broad areas of research include: statistical methodology and its applications; statistical and econometric methods; social statistics; demography. After approval of the project, the Academic Board assigns a supervisor to each PhD student. Research programme in the second and third year. The research activity in the second and third year is the distinctive feature of the PhD programme and is aimed at achieving independent research capabilities. During the second and third years up to 12 months may be spent at a university or other highly qualified institution abroad. Students are strongly encouraged to include a period of research abroad in their programme, taking advantage of the national and international collaborative networks of the members of the Doctoral Board. The result of the research must be presented as a thesis containing original scientific results relevant to the field of statistics and its applications.

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Vanderbilt to establish a college dedicated to computing, AI and data science

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Mar 25, 2024, 11:20 AM

Vanderbilt has begun work to establish a transformative college dedicated to computer science, AI, data science and related fields, university leaders announced today. In addition to meeting the growing demand for degrees in technological fields and advancing research in rapidly evolving, computing-related disciplines, the new, interdisciplinary college will collaborate with all of Vanderbilt’s schools and colleges to advance breakthrough discoveries and strengthen computing education through a “computing for all” approach.

The College of Connected Computing will be led by a new dean, who will report to Provost and Vice Chancellor for Academic Affairs C. Cybele Raver and to School of Engineering Dean Krishnendu “Krish” Roy. The search for the college’s dean is scheduled to begin in late August, and recruiting of faculty will begin in the coming months. It will be the first new college at Vanderbilt since the university and the Blair School of Music merged in 1981.

“Of all the factors shaping society, few are more influential than the rapid emergence of advanced computing, AI and data science,” Chancellor Daniel Diermeier said. “To continue to carry out our mission, prepare all our students for their careers and advance research across the university, Vanderbilt must contribute even more to the study, understanding and innovative application of these fast-changing disciplines. Our aim is to make Vanderbilt a global leader in these fields, ensuring our continued academic excellence and capacity for world-changing innovation.”

“Our new college will enable us to build upon our strong programs and catapult Vanderbilt to the forefront of breakthrough discovery and innovation—in key areas of computer science and also in a wide range of other disciplines that capitalize on advanced computational methods. In launching this new college, we will provide students with highest-caliber educational opportunities at the intersection of these pathbreaking fields,” Raver said. “The creation of this college represents a tremendous win and will be transformative for our entire university community.”

Raver noted the ways that Vanderbilt is forging a bold and distinct strategic path to address burgeoning research and educational opportunities, including increasing demand for expertise in computing-related fields. Moreover, she said, the global interest in AI “aligns perfectly” with Vanderbilt’s leading work in that field. She said a dedicated college will enable Vanderbilt to keep making groundbreaking discoveries at the intersections of computing and other disciplines and will more effectively leverage advanced computing to address some of society’s most pressing challenges.

“The establishment of this interdisciplinary, ‘cross-cutting’ college is a watershed moment—not only for the School of Engineering, but also for the entire university,” Roy said. “The future of education, research and thinking in all disciplines is now inherently tied to, and will be greatly influenced by, the knowledge and power of computing. The idea of ‘computing for all’ is fundamental to the future of learning.”  

Many of the specific details about the college—including its departments, degree programs and research infrastructure—will be informed by the recommendations of a task force on connected computing composed of faculty from across the university. In addition, Vice Provost for Research and Innovation Padma Raghavan will launch a Computing Catalyst working group that will engage faculty and staff leaders in computing from across campus and solicit their input on strategically expanding the university’s computing resources. “The decision to establish this new college is rooted in conversations with faculty,” Raver said. “We are continuing that faculty engagement with this working group, and we’re fortunate to have the advice of some of the best minds in these fields as we embark on this exciting journey.”   

The members of the Connected Computing Task Force include:

Krishnendu Roy , Chair   Bruce and Bridgitt Evans Dean of Engineering  University Distinguished Professor of Biomedical Engineering; Pathology, Microbiology and Immunology; and Chemical and Biomolecular Engineering        

Douglas Adams   Vice Dean of the School of Engineering   Daniel F. Flowers Chair Distinguished Professor of Civil and Environmental Engineering  Professor of Mechanical Engineering  Faculty Affiliate, VINSE        

Hiba Baroud   Associate Chair and Associate Professor of Civil and Environmental Engineering James and Alice B. Clark Foundation Faculty Fellow Associate Professor of Computer Science  Faculty Affiliate, VECTOR , Data Science Institute         

Gautam Biswas   Cornelius Vanderbilt Professor of Computer Science and Computer Engineering Professor of Engineering Management  Senior Research Scientist, ISIS   Faculty Affiliate, Data Science Institute        

Erin Calipari   Associate Professor of Pharmacology  Associate Professor of  Molecular Physiology & Biophysics Associate Professor of  Psychiatry & Behavioral Sciences Director, Vanderbilt Center for Addiction Research  Faculty Affiliate, Vanderbilt Brain Institute        

Laurie Cutting   Patricia and Rodes Hart Professor and Professor of Special Education  Professor of Psychology Professor of Pediatrics Professor of Electrical and Computer Engineering Professor of Radiology & Radiological Sciences Associate Provost in the Office of the Vice Provost of Research and Innovation Associate Director of the Vanderbilt Kennedy Center  Faculty Affiliate, Vanderbilt Brain Institute        

Benoit Dawant   Cornelius Vanderbilt Professor of Electrical Engineering Incoming Chair of the Department of Electrical and Computer Engineering  Director and Steering Committee Chair, Vanderbilt Institute for Surgery & Engineering  Professor of Biomedical Engineering Professor of Computer Science      

Abhishek Dubey   Associate Professor of Computer Science  Associate Professor of Electrical and Computer Engineering  Director, SCOPE lab at ISIS   Faculty Affiliate, Institute for Software Integrated Systems and Data Science Institute         

Bennett Landman   Stevenson Professor of Electrical and Computer Engineering and Chair of the Department of Electrical and Computer Engineering  Professor of Biomedical Engineering Professor of Computer Science Professor of Neurology Associate Professor of Biomedical Informatics Associate Professor of Psychiatry and Behavioral Sciences Associate Professor of Radiology and Radiological Sciences Faculty Affiliate, Vanderbilt Institute for Surgery and Engineering (VISE) , Vanderbilt Brain Institute , Vanderbilt Kennedy Center , Vanderbilt University Institute of Image Science (VUIIS) , Data Science Institute         

Michael Matheny   Professor of Biomedical Informatics  Professor of Biostatistics Professor of Medicine Director, Center for Improving the Public’s Health Through Informatics        

Sandeep Neema   Professor of Computer Science  Professor of Electrical and Computer Engineering  Chair of the Executive Council, Institute for Software Integrated Systems         

Ipek Oguz   Assistant Professor of Computer Science  Assistant Professor of Biomedical Engineering Assistant Professor of Electrical & Computer Engineering  Faculty Affiliate, Vanderbilt Institute for Surgery and Engineering (VISE)         

J.B. Ruhl   David Daniels Allen Distinguished Chair of Law  Director, Program in Law and Innovation   Co-Director, Energy, Environment and Land Use Program   Faculty Affiliate, Data Science Institute         

Jesse Spencer-Smith     Professor of the Practice of Computer Science  Adjunct Professor of Psychology Interim Director and Chief Data Scientist, Data Science Institute         

Jonathan Sprinkle   Professor of Computer Science  Professor of Electrical & Computer Engineering  Professor of Civil & Environmental Engineering  Faculty Affiliate, Institute for Software Integrated Systems         

Yuankai “Kenny” Tao   Associate Professor of Biomedical Engineering  Associate Professor of Ophthalmology & Visual Sciences  SPIE Faculty Fellow in Engineering Faculty Affiliate, Vanderbilt Institute for Surgery & Engineering        

Holly Tucker   Mellon Foundation Chair in the Humanities Professor of French  Director, Robert Penn Warren Center for the Humanities         

Kalman Varga   Vice Chair of the Department of Physics & Astronomy Professor of Physics  Director, Minor in Scientific Computing  Faculty Affiliate, VINSE        

Steven Wernke   Chair of the Department of Anthropology Associate Professor of Anthropology  Director, Vanderbilt Institute for Spatial Research (VISR) Faculty Affiliate, Data Science Institute    

Jules White Professor of Computer Science  Associate Professor of Biomedical Informatics  Senior Advisor to the Chancellor for Generative AI in Education and Enterprise Solutions  Faculty Affiliate, Institute for Software Integrated Systems , Data Science Institute         

Dan Work   Director of Graduate Studies in Civil Engineering Professor of Civil & Environmental Engineering  Professor of Computer Science  Faculty Affiliate, VECTOR , Institute for Software Integrated Systems , Data Science Institute           

Tracey George   ex officio   Vice Provost for Faculty Affairs and Professional Education  Charles B. Cox III and Lucy D. Cox Family Chair in Law and Liberty  Professor of Law       

Tiffiny Tung   Ex officio   Vice Provost for Undergraduate Education  Gertrude Conaway Vanderbilt Chair in the Social and Natural Sciences Professor of Anthropology   

  Members of the Vanderbilt community can learn more about this initiative and share feedback with the faculty working group by visiting vanderbilt.edu/about/computingtaskforce .  

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Bouvé College of Health Sciences

Khoury college of computer sciences, health informatics.

The Master of Science in Health Informatics prepares students to successfully address the combined clinical, technical, and business needs of health-related professions.

Northeastern’s interdisciplinary Master of Science in Health Informatics program was the first master’s program in the field. Our students graduate with the knowledge of how technology, people, health, and the healthcare system interrelate; the ability to use technology and information management to improve healthcare delivery and outcomes; and the skills to communicate effectively among healthcare practitioners, administrators, and information technology professionals.

The MS in Health Informatics program is a collaboration between the Bouvé College of Health Sciences and the Khoury College of Computer Sciences. The program offers a flexibly designed curriculum for both part-time and full-time students, combining core courses in healthcare systems and management with elective courses that span vital topics across the industry. Students are able to choose classes based on their interests and backgrounds; students from the healthcare industry are introduced to new technologies. An advisory board of industry professionals provides expert guidance in the program’s development and ongoing curricular advancements—the program remains at the leading edge of advances in the industry.

This interdisciplinary master’s degree program is designed for healthcare professionals with limited computing and information technology experience, as well as IT professionals with little knowledge of healthcare environments.

• The  Personal Health Informatics Concentration includes courses such as Creation and Application of Medical Knowledge, Computer/Human Interaction, Biostatistics in Public Health, and completion of a Thesis or Capstone Project.
• Health Informatics (Without Concentration)  requires courses associated with Business Management, Health Informatics, Technical (such as Biostatistics and Public Health), and a Capstone Project.
• The  Health Informatics Analytics Concentration  is offered in conjunction with Northeastern’s College of Engineering , offering electives such as Computational Modeling and Structured Data Analytics for Industrial Engineering, Healthcare Systems Modeling and Analysis, and Data Mining in Engineering. Required coursework include courses associated with Business Management, Health Informatics courses, Technical, and a Capstone Project.

More Details

Unique features.

• This MS program is designed for students with no clinical or technical experience as well as for people with experience in either discipline.
• The program can be completed with no concentration, with a concentration in Health Informatics Analytics or in Personal Health Informatics.
• Flexible course schedules and formats meet the needs of both working professionals and full-time students
• Faculty for this program are senior leaders in the field
• Coursework provides an academic pathway to the PhD in Personal Health Informatics
• Research capstone project allows students to make an active contribution to the field
• Health Informatics program graduates have a nearly 100% job placement within three months of graduation
• The program is STEM certified

Program Objectives

• Understand how information technology, people, health, and the healthcare system interrelate
• Use information technology and information management concepts and methods to improve healthcare delivery and outcomes
• Communicate effectively among healthcare practitioners, administrators, and IT professionals, and understand each group’s needs and constraints

Career Outlook

Health informatics is a rapidly evolving field—one in which jobs are projected to grow by 23% into 2020. As the healthcare system evolves, the ability to integrate technology into patient care is becoming increasingly imperative, creating great demand for professionals with knowledge of health sciences, computer science, and information technology. Northeastern’s MS in Health Informatics graduates have gone on to hold positions as clinical, data, business, technical, application, and security analysts, as project managers, and as CIOs, CMIOs, and directors of many prominent companies. Recent graduates hold positions at Harvard Pilgrim Healthcare, Partners Healthcare, Beth Israel, Tufts Medical Center, Lawrence General Hospital, Humedica, and Verisk Analytics.

Testimonials

– sara khalil, ms ‘22, looking for something different.

A graduate degree or certificate from Northeastern—a top-ranked university—can accelerate your career through rigorous academic coursework and hands-on professional experience in the area of your interest. Apply now—and take your career to the next level.

Program Costs

Finance Your Education We offer a variety of resources, including scholarships and assistantships.

How to Apply Learn more about the application process and requirements.

Requirements

• Online application and fee
• Unofficial undergraduate/graduate transcripts; (you can submit official transcripts from all colleges/universities attended at the time of admission)
• Statement of purpose that should include career goals and expected outcomes and benefits from the program
• Recent professional resumé listing detailed position responsibilities
• Three confidential letters of recommendation
• Official TOEFL or IELTS examination scores (international students only)

International students are required to submit official transcripts to  World Education Services  for credentialing. Once you have received verification of your degree and transcript from WES, please forward to the address below.

Send all supplemental application materials to:

If you are mailing  from outside the U.S.,  please send your documents to:

Northeastern University Bouvé College of Health Sciences Applicant ID: XXXXXXX (insert your applicant ID number) Graduate Application Documents 360 Huntington Ave. Boston, MA 02115 USA

If you are mailing  from inside the U.S. , please send your documents to:

Northeastern University Bouvé College of Health Sciences Applicant ID: XXXXXXX (insert your applicant ID number) Graduate Application Processing Center P.O. Box 1434 Portsmouth, NH 03802 USA

Are You an International Student? Find out what additional documents are required to apply.

Admissions Details Learn more about the Bouvé College of Health Sciences admissions process, policies, and required materials.

Admissions Dates

Applicants must submit the online application and all required admission materials no later than the stated deadlines to be considered for admission. Admissions decisions are made on a rolling basis.

Industry-aligned courses for in-demand careers.

For 100+ years, we’ve designed our programs with one thing in mind—your success. Explore the current program requirements and course descriptions, all designed to meet today’s industry needs and must-have skills.

View curriculum

Co-op makes the Northeastern graduate education richer and more meaningful. It provides master’s students with up to 12 months of professional experience that helps them develop the knowledge, awareness, perspective, and confidence to develop rich careers. In addition to the esteemed faculty, many students enroll in the master’s programs largely because of the successful co-op program.

Graduate students typically have an experiential work opportunity following their second semester. This could be a six- to eight-month co-op or a three- to four-month summer internship. Those who initially experience co-op may have the opportunity to seek an internship for the following summer, or vice versa.

Student participation in experiential education provides enhanced:

• Maturity, responsibility, and self-knowledge
• Technical expertise
• Occupational information
• Job seeking and job success skills
• Networking opportunities with those in desired career paths

Northeastern’s co-op program is based on a unique educational strategy which recognizes that classroom learning only provides some of the skills students will need to succeed in their professional lives. Our administration, faculty, and staff are dedicated to the university’s mission to “educate students for a life of fulfillment and accomplishment.” Co-op is closely integrated with our course curriculum and our advising system. The team of graduate co-op faculty within the Khoury College of Computer Sciences provides support for students in preparing for and succeeding on their co-ops.

These multiple connections make co-op at Northeastern an avenue to intellectual and personal growth: adding depth to classroom studies, providing exposure to career paths and opportunities, and developing in students a deeper understanding that leads to success in today’s world.

Our Faculty

Northeastern University faculty represents a broad cross-section of professional practices and fields, including finance, education, biomedical science, management, and the U.S. military. They serve as mentors and advisors and collaborate alongside you to solve the most pressing global challenges facing established and emerging markets.

Jay Spitulnik

By enrolling in Northeastern, you’ll gain access to students at 13 campus locations, 300,000+ alumni, and 3,000 employer partners worldwide. Our global university system provides students unique opportunities to think locally and act globally while serving as a platform for scaling ideas, talent, and solutions.

Below is a look at where our Nursing and Healthcare alumni work, the positions they hold, and the skills they bring to their organization.

Where They Work

• Massachusetts General Hospital
• Boston Children’s Hospital
• Beth Israel Deaconess Medical Center
• Brigham and Women’s Hospital
• Boston Medical Center

What They Do

• Healthcare Services
• Business Development
• Community and Social Services

What They're Skilled At

• Patient Safety
• Healthcare Management

Learn more about Northeastern Alumni on  Linkedin .

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IIT Jodhpur Invites Applications For MTech, PhD, Data Science Programs, Check Details

Iit jodhpur admissions 2024-25: the department of mathematics additionally offer a minor program in data science tailored for undergraduate students of the institute..

IIT Jodhpur: The course duration for MTech program in Data and Computational Sciences is two years.

The Indian Institute of Technology Jodhpur (IIT-Jodhpur) is currently accepting applications for postgraduate programs in MTech, MTech-PhD dual degree, and PhD, along with a minor program in Data Science. Interested and eligible individuals can submit their applications on the official website by April 20.

IIT Jodhpur Admissions 2024-25: Programs Offered

• Two-year MTech program in Data and Computational Sciences • Four-year MSc-MTech in Mathematics-Data and Computational Sciences • MTech-PhD dual degree program in Data and Computational Sciences • PhD Program in Pure & Applied Mathematics and Applied Statistics • Online executive program in Data Science and Computations

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The Department of Mathematics additionally offer a minor program in Data Science tailored for undergraduate students of the institute. Furthermore, a wide array of mandatory and optional courses are available for students pursuing BTech, MTech, MSc, and PhD degrees from diverse departments within the institute.

IIT Jodhpur Admissions 2024-25: Research

• Pure Mathematics
• Applied Mathematics
• Data Science
• Computational Science

The department comprises faculty members specialising in a range of research fields, including Algebra, Mathematical Physics, Scientific Computation, Numerical Analysis, Partial Differential Equations, Topological Dynamics, Low Dimensional Chaos, Dynamical Systems, Renormalization in Low-dimensional dynamics, Wavelet Analysis, Fractional Transform Theory, Image Processing, Financial Risk Analysis, Categorical Data Analysis, Reliability Theory, and Applied Probability.

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Maps of the April 2024 Total Solar Eclipse

By Jonathan Corum

On April 8, the moon will slip between the Earth and the sun, casting a shadow across a swath of North America: a total solar eclipse.

By cosmic coincidence, the moon and the sun appear roughly the same size in the sky. When the moon blocks the glare of the sun, the sun’s outer atmosphere, or corona, will be briefly visible.

Below are several maps of the eclipse’s path as well as images of what you might experience during the event.

Where Can I See the Total Eclipse?

The eclipse will begin at sunrise over the Pacific Ocean, then cut through Mexico and cross the United States from Texas to Maine. Most of North America will see a partial eclipse, but viewers within the deepest shadow — a band sliding from Mazatlán, Mexico, to the Newfoundland coast near Gander, Canada — will experience a total solar eclipse.

Percentage of

the sun obscured

during the eclipse

Indianapolis

Little Rock

San Antonio

Viewers inside the path of the total eclipse may notice a drop in temperature , a lull or shift in the wind , the appearance of bright planets in the sky, and the quieting of birds and other wildlife.

Many cities lie inside the path of the total eclipse, as shown below, the width of which varies from 108 miles to 122 miles.

5:13 p.m. NDT

20% partial eclipse

NEWFOUNDLAND

SASKATCHEWAN

Fredericton

4:33 p.m. ADT

3:26 p.m. EDT

3:20 p.m. EDT

Minneapolis

3:18 p.m. EDT

3:13 p.m. EDT

San Francisco

90% partial eclipse

3:05 p.m. EDT

Los Angeles

1:51 p.m. CDT

1:40 p.m. CDT

1:33 p.m. CDT

12:16 p.m. CST

12:12 p.m. CST

11:07 a.m. MST

Mexico City

EL SALVADOR

12:23 p.m. CST

1:36 p.m. CDT

3:09 p.m. EDT

3:27 p.m. EDT

Explore our interactive cloud outlook for eclipse viewing times and average cloud data at your location.

What Will I See?

A composite image of the 2017 solar eclipse over Madras, Ore.

Aubrey Gemignani/NASA

If the sky is clear, viewers in the path of the total eclipse should see a “diamond ring” effect a few seconds before and after the total eclipse, as the edge of the sun slips in and out of view.

The “diamond ring” effect during the 2017 solar eclipse.

Rami Daud/NASA, Alcyon Technical Services

The sun’s outer atmosphere, or corona, is normally hidden by the sun’s glare. These tendrils and sheets of gas, heated to a million degrees Fahrenheit or more, are in constant motion and shaped by the sun’s swirling magnetic field.

The sun’s corona during the 2017 solar eclipse.

The sun is relatively active this year and is nearing the expected peak of its 11-year solar cycle . Researchers at Predictive Science are using data about the sun’s magnetic field to predict and model a dramatic corona for the April eclipse.

A prediction of how the sun’s corona might appear during the April 8 total eclipse.

Predictive Science

What Colors Should I Wear?

As the sky darkens, light-sensitive cells in human eyes become more sensitive to blue and green hues than to reds and oranges. This shift in color perception is known as the Purkinje effect , after a 19th-century Czech scientist, and is typically seen at twilight.

Watching the 2017 total eclipse at Southern Illinois University.

Andrea Morales for The New York Times

To take advantage of the Purkinje effect, wear green clothes or a contrasting combination of greens and reds. Blue-green colors (shorter wavelengths) will appear brighter, while red colors (longer wavelengths) will appear to recede into the darkness.

What If I Miss It?

The next two total solar eclipses in the United States won’t occur until 2044 and 2045 . But eclipse chasers might catch one in 2026 in Greenland, Iceland and Spain; 2027 along the coast of Northern Africa; 2028 in Australia and New Zealand; or 2030 across Southern Africa and Australia.

A Total Solar Eclipse Is Coming. Here’s What You Need to Know.

These are answers to common questions about the April 8 eclipse, and we’re offering you a place to pose more of them.

By Katrina Miller

What’s the Cloud Forecast for Eclipse Day? See if the Weather Is on Your Side.

April 8 could be your best opportunity to see a total solar eclipse for decades. But if clouds fill the sky, you may miss the spectacle.

By Josh Katz, K.K. Rebecca Lai and William B. Davis

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Our Coverage of the Total Solar Eclipse

Hearing the Eclipse:  A device called LightSound is being distributed to help the blind and visually impaired experience what they can’t see .

Maine Brac es Itself :  Businesses and planning committees are eager for visitors, but some in remote Aroostook County are not sure how they feel  about lying smack in the path of totality.

A Dark Day for Buffalo:  When the sky above Buffalo briefly goes dark  on the afternoon of April 8, the city will transcend its dreary place in the public consciousness — measured as it so often is by snowstorms — if only for about three minutes. The city can’t wait.

Under the Moon’s Shadow:  The late Jay Pasachoff, who spent a lifetime chasing eclipses , inspired generations of students to become astronomers by dragging them to the ends of the Earth for a few precarious moments of ecstasy.

A Rare Return:  It is rare for a total solar eclipse to hit the same place twice — once every 366 years on average. People in certain areas will encounter April 8’s eclipse  about seven years after they were near the middle of the path of the “Great American Eclipse.”

A Small City’s Big Plans:  Let the big cities have their eclipse mega-events. In Plattsburgh, N.Y., success looks different  for everyone stopping to look up.

 No Power Outages:  When the sky darkens during the eclipse, electricity production in some parts of the country will drop so sharply that it could theoretically leave tens of millions of homes in the dark. In practice, hardly anyone will notice  a sudden loss of energy.

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