About the track
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 requirements
- 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?
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Honors students
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Candidates with analytical thinking
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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. The student must select one subject from each of the following lists.
| 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 |
| 00960450 | Multiple Comparisons | 2.5 |
| 00970414 | Statistics 2 | 3 |
| 00970449 | Nonparametric Statistics | 2.5 |
| 00970470 | Semiparametric Models | 2 |
| 00980413 | Stochastic Processes | 3.5 |
| 00980414 | Theory of Statistics | 3 |
| 00970400 | Causal Inference | 2.5 |
| 00980455 | Probability and Stochastic Processes 2 | |
| 00980460 | Applied Multivariate Analysis | 3.5 |
| Course Number | Course Name | Pts. |
|---|---|---|
| 00960292 | Predictive Analytics in Fintec | 3 |
| 00960293 | Machine Learning in Portfolio Selection | 2.5 |
| 00960336 | Optimization Methods in Machine Learning | 2 |
| 00970200 | Deep Learning | 3.5 |
| 00970920 | Methods in Natural Language Processing | 3.5 |
| 00970248 | Machine Learning For Healthcare | 3 |
| 00970215 | Methods in Natural Language Processing | 3 |
| 00970209 | Machine Learning 2 | 3.5 |
| 00970225 | Perturbation Methods in Machine Learning | 2.5 |
| Course Number | Course Name | Pts. |
|---|---|---|
| 00960335 | Optimization Under Uncertainty | 3.5 |
| 00960336 | Optimization Methods in Machine Learning | 2 |
| 00960351 | Polyhedral Methods For Integer Programing | 2.5 |
| 00970334 | Algebriac Methods For Integer Progrmming | 2.5 |
| 00970325 | Sparse Optimizzation: Theory and Methods | 3 |
| 00980311 | Optimization 1 | 3.5 |
| 00980312 | Optimization 2 | 3 |
| 00980331 | Linear and Combinatorial Programming | 3.5 |
| 00960200 | Mathematical Tools For Data Science | 3.5 |
| Course Number | Course Name | Pts. |
|---|---|---|
| 00960208 | Automatic Planning | 3.5 |
| 00970921 | Selected Topics Indata and Decision Sciences | 3 |
| 00960211 | Electronic Commerce Models | 3.5 |
| 00960212 | Probabilistic Graphical Models | 2 |
| 00960226 | Computation | 2.5 |
| 00960265 | Algorithms in Logic | 3 |
| 00960291 | Algorithmic and High-frequency Trading | 2 |
| 00960326 | Algorithms in Scheduling | 3.5 |
| 00960572 | Advanced Topics in Game Theory | 2 |
| 00980920 | Topics in Human-al Interaction | 2.5 |
| 00960573 | Auction Theory | 2.5 |
| 00960578 | Social Choice and Preference Aggregation | 2.5 |
| 00960606 | Behavioral Econ. in Technological Env. | 3 |
| 00970211 | Fault Tolerant Networks Protocols | 3.5 |
| 00970245 | Mechanism Design For Data Science | 2 |
| 00970246 | Social Computing Models | |
| 00970280 | Algorithms in Uncertain Scenarios | 3 |
| 00970317 | Cooperative Game Theory | 2.5 |
| 00970329 | Probablistic Algorithms | 2.5 |
| 00980312 | Optimization 2 | 3 |
ניתן להגיש לראש התכנית בקשה להכרה בקורס אשר מכיל פרויקט עתיר נתונים כקורס נתונים.
| Course Number | Course Name | Pts. |
|---|---|---|
| 00960222 | Language | 3 |
| 00970200 | Deep Learning | 3.5 |
| 00960412 | Business Process Management and Mining | 3 |
| 00960224 | Distributed Data Management | 3 |
| 00960231 | Math Models in Advanced Info.retrieval | 3 |
| 00960262 | Information Retrieval | 3.5 |
| 00960290 | Selected Topics in Data Science | 2.5 |
| 00960324 | Service Engineering | 3.5 |
| 00960586 | Econometrics | 3.5 |
| 00960693 | Psychological and Cognitive Networks | 3 |
| 00970135 | Multidisciplinary Research in Service | 3.5 |
| 00970215 | Methods in Natural Language Processing | 3 |
| 00970216 | Natural Language Processing | 2.5 |
| 00970247 | The Internet of Things (iot) | 3 |
| 00970248 | Machine Learning For Healthcare | 3 |
| 00970400 | Causal Inference | 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.