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
- Honors in B.Sc. studies (an average of 86 or higher)
- M.Sc. in Data Science and Engineering from a recognized university
- Graduates in other fields will be required to complete supplementary courses
- Experience and achievements in industry or research
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
-
Honors Students
-
Candidates with Analytical Thinking
-
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.
Prof. Avigdor Gal - To be a Well-Chiseled Data Scientist.