--- source_url: "https://dds.technion.ac.il/tracks/masters-degree/data_science/" title: "Data Science - The Faculty of Data and Decision Sciences : The Faculty of Data and Decision Sciences" mirrored_at: 2026-08-21T01:37:28.702Z host: dds.technion.ac.il cited_in_42a: true mirror_canonical: "https://index.42a.ai/dds.technion.ac.il/tracks/masters-degree/data_science/index" --- > **Original source:** https://dds.technion.ac.il/tracks/masters-degree/data_science/ - [About](#tab1) - [Admission](#tab2) - [Supplementary Courses](#tab3) - [Who is it for](#tab4) - [Fields of Study](#tab5) - [Requirements](#tab6) - [Doctoral Studies](#tab7) ## About In an era where information is created at a dizzying pace and changes constantly and decisions require the creation of in-depth analysis, the ability to make sense of large quantities of data is a necessary and sought-after power. A master’s degree in Data Science offers tools and knowledge that will enable you to face the great challenges of the 21st century in all areas of life: medicine, social media, finance, urban planning, smart cities and more. The Data Science graduate program emphasizes experience in research methods in the scientific and technological fields dealing with the collection, management, analysis and presentation of big data. Upon completion of the program, as researchers in the field of data science, you will know how to develop scientific solutions to the many challenges involved in working with large and varied amounts of frequently-changing data with varying degrees of certainty. As befits a multidisciplinary and diverse faculty, the research is based on knowledge in mathematics, computer science, operations research, statistics, computational learning, psychology, and more. The degree conferred in the program is an M.Sc. in Data Science. ### Admission 1. Honors in B.Sc. studies (an average of 86 or higher) 2. M.Sc. in Data Science and Engineering from a recognized university 3. Graduates in other fields will be required to complete supplementary courses Honors students with a final average of 86 or higher with a B.Sc. in Data Science and Engineering from the Technion or from another recognized university will be accepted for M.Sc. studies in Data Science. Candidates who have completed a B.Sc. with honors in Mathematics, Computer Science, Electrical Engineering, Information System Engineering, Industrial and Management Engineering or Physics may be required to take supplementary courses. Admission to the program will be determined according to the candidates’ background and academic achievements, as well as their experience and achievements in industry or research and letters of recommendation. The list of required supplementary courses will be determined by the degree admissions committee. ## Supplementary Courses Graduates of a four-year B.Sc. program are required to complete 20 credits in graduate programs, to fulfill the advanced English requirement (2 credits) and a research project as part of a thesis. A total of 22 credits and a thesis are required. Graduates of a three-year B.Sc. program are required to complete 30 credits, of which 10 credits can be accumulated from advanced courses in undergraduate studies, to fulfill the advanced English requirement (2 credits) and a research project as part of a thesis. A total of 32 credits and a thesis are required. ## Supplementary Courses **Course Number** **Course Name** **Pts.** 00940345 Discrete Mathematics 3.5 00950295 Algebraic Methods For Data Science 3.5 00960327 Nonlinear Models in Operations Research 3.5 00940700 Introduction to Data Science and Engineering 1.5 00940219 Software Engineering 3.5 00940223 Data Structures and Algorithms 3.5 00940250 Introduction to Computability 2.5 00940314 Stochastic Models in Oper.research 3.5 00940412 Probability (advanced) 4 00940424 Statistics 1 3.5 00960411 Machine Learning 1 3.5 ## Who are the studies suitable for - ![](https://dds.technion.ac.il/wp-content/uploads/2024/03/icon4-2.svg) ## Honors Students - ![Brain](https://dds.technion.ac.il/wp-content/uploads/2024/03/icon2-2.svg) ## Candidates with Analytical Thinking - ![Light bulb](https://dds.technion.ac.il/wp-content/uploads/2024/03/yellow1.svg) ## Candidates Who like Developing Challenging Scientific Solutions ## Fields of Study The selection of courses offered as part of the program reflects the research areas relevant to the field as well as courses for creating the common basis for working with data and extracting knowledge from it. The curriculum emphasizes courses in statistics and probability, machine learning and artificial intelligence, optimization, game theory and algorithmics. Students must choose at least three lists from which they will take at least one course during their master’s degree. **Course Number** **Course Name** **Pts.** 00960200 Mathematical Tools For Data Science 3.5 00960415 Topics in Regression 3 00960425 Time Series and Forecasting 2.5 00960426 Survival Analysis and Machine Learning 00960450 Multiple Comparisons 2.5 00970400 Introduction to Causal Inference 2.5 00970404 Selected Topics in Statistics: Survey Methodology 00970414 Statistics 2 3 00970449 Nonparametric Statistics 2.5 00970470 Semiparametric Models 2 00980413 Stochastic Processes 3.5 00980414 Theory of Statistics 3 00980423 Introduction to Stochastic Processes 2 2 00970400 Causal Inference 2.5 00980455 Probability and Stochastic Processes 2 00980460 Applied Multivariate Analysis 3.5 **Course Number** **Course Name** **Credits** 00960236 Generative AI and Diffusion Models 2.5 00960292 Fintech Prediction Methods 3 00960293 Computational Learning in Portfolio Selection 2.5 00960336 Optimization Methods in Machine Learning 2 00960576 Learning and Complexity in Game Theory 2 00970200 Deep Learning, Theory and Practice 3.5 00970202 Modern Computer Vision 00970203 Reinforcement Learning 00970209 Computational Learning 2 3.5 00970215 Natural Language Processing 3 00970222 Computer Vision and Applications in the Operating Room 2.5 00970225 Perturbation Methods in Machine Learning 2.5 00970248 Machine Learning in Medicine 3 00970249 Machine Learning in Sequential Decision Making 3 00970251 Strategic Aspects in Machine Learning 2.5 00970920 Topics in Research Natural Language Processing 2.5 **Course Number** **Course Name** **Credits** 00960200 Mathematical Tools for Data Science 3.5 00960335 Optimization under Uncertainty 3.5 00960336 Optimization Methods in Machine Learning 2 00960351 Polyhedral Methods in Integer Programming 2.5 00970325 Theory and Methods in Sparse Optimization 3 00970334 Algebraic Methods for Integer Computation 2.5 00970402 Selected Topics in Optimization: Projection-Free Methods 2 00980311 Optimization 1 3.5 00980312 Optimization 2 3 00980331 Linear and Combinatorial Planning 3.5 **Course Number** **Course Name** **Credits** 00960208 AI and Autonomous Systems 3.5 00960211 E-Commerce Models 3.5 00960212 Probabilistic Graphical Models 2 00960226 Computation and Game Theory 2.5 00960237 AI Agent Systems 2.5 00960265 Algorithms in Logic 3 00960291 High-Frequency Algorithmic Trading 2 00960326 Scheduling Algorithms 3.5 00960572 Advanced Topics in Game Theory 2 00960573 Auction Theory 2.5 00960576 Learning and Complexity in Game Theory 2 00960578 Social Choice and Collective Decisions 2.5 00960606 Behavioral Economics in a Technological Environment 3 00970211 Fault-Tolerant Network Protocols 3.5 00970245 Mechanism Design for Data Science 2 00970246 Social Computation Models 2.5 00970280 Algorithms in Uncertainty Scenarios 3 00970317 Cooperative Game Theory 2.5 00970329 Probabilistic Algorithms 2.5 00970921 Topics in Data Science and Decisions 3 00980312 Optimization 2 3 00980920 Topics in Human-AI Interaction 2.5 **Course Number** **Course Name** **Credits** 00960222 Language Processing, Cognition and Computation 3 00960224 Distributed Data Management 3 00960231 Mathematical Models in Advanced Information Retrieval 3 00960237 AI Agent Systems 2.5 00960262 Information Retrieval 3.5 00960290 Selected Topics in Data and Information Engineering 2.5 00960324 Service Systems Engineering 3.5 00960412 Business Process Management and Mining 3 00960586 Econometrics 3.5 00960589 Advanced Econometrics 3.5 00960693 Psychological and Cognitive Networks (with Data Project) 3 00970135 Multidisciplinary Research in Service Systems 3.5 00970200 Deep Learning, Theory and Practice 3.5 00970202 Modern Computer Vision 00970215 Natural Language Processing 3 00970216 Advanced Natural Language Processing 2.5 00970222 Computer Vision and Applications in the Operating Room 2.5 00970247 Internet of Things Technology 3 00970248 Machine Learning in Medicine 3 00970400 Introduction to Causal Inference 2.5 00970403 Selected Topics: Machine Learning for Prediction 2.5 00970405 Selected Topics: Neural Data Science and the Brain 2.5 00970920 Topics in Research Natural Language Processing 2.5 ## Requirements for completion of the degree Full completion of all course requirements Advanced English requirement Research ethics Completion and submission of a thesis Please note: The requirements that apply to the student are those defined in the year in which they were accepted for studies; however, the faculty reserves the right to define additional scholastic requirements beyond those defined at the time of admission. Thesis The main part of the Master’s degree program is completion of a 20-credit research paper. Before completing the research, the student must present it in a field seminar paper (at least a month, but no more than a year before submission).  The student must publish notice of the seminar according to the Technion’s rules in coordination with the seminar coordinator. According to the graduate school’s regulations, a 12-credit final paper can be authorized instead of a research paper or a research project. In those special cases, the student will be required to study additional courses with the permanent advisor’s authorization, of at least 8 credits. ## Doctoral Studies Students who wish to continue to doctoral studies will be required to comply with the graduate school’s procedures. ![Data Science](https://i3.ytimg.com/vi/Oj7GzTlor2k/maxresdefault.jpg) **Prof. Avigdor Gal - To be a Well-Chiseled Data Scientist.**