This table includes courses given at Bar-Ilan that may be relevant to students interested in Data Science.
Note: full and accurate course details should be taken from Bar-Ilan official course catalog. We’ll do our best to keep this table updated.
(legend – see below table)
| Course # | Course Name | Department | Category | Target Audience | Points | Type | Semester | Degree | Given in TASHAPA |
|---|---|---|---|---|---|---|---|---|---|
| 13619 | Digital Humanities and the Analysis of Hebrew Texts | Jewish Literature | NLP/Text | Exposure | 2 | Lecture | Annual | Both | |
| 272000 | Introduction to Programming using python | Brain Science | ML/DL/NN | Core | 1 | Lecture | A | Undergrad | y |
| 27208 | Introduction to Probability | Brain Science | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
| 27213 | Introduction to Statistics | Brain Science | AI | Exposure | 1.5 | Lecture | B | Undergrad | y |
| 27305 | Signal Processing | Brain Science | Algorithms/Data Structures | Applied | 1 | Lecture | B | Undergrad | y |
| 27436 | Neuronal Network | Brain Science | DB/Big Data | Applied | 1 | Lecture | A | Grad | y |
| 27437 | Information Theory and Learning Methods | Brain Science | Programming | Applied | 1.5 | Lecture | B | Undergrad | y |
| 27504 | Theories on Nerve Networks and Machine Learning | Brain Science | Programming | Applied | 2 | Lecture | A | Undergrad | y |
| 27505 | Analysis of Symbols and Data | Brain Science | Data Mining / Visualization | Applied | 2 | Lecture | B | Undergrad | y |
| 278237 | Artificial intelligence: from humanoids to swarms of thinking machines | STS | DB/Big Data | Applied | 2 | Lecture | Annual | Undergrad | y |
| 35603 | Algorithms I | Information Science | ML/DL/NN | Applied | 2 | Lecture | A | Undergrad | y |
| 35605 | Big Data | Information Science | Digital Humanities | Exposure | 1 | Lecture | A | Both | y |
| 35615 | Programming Basics | Information Science | Data Mining / Visualization | Applied | 1 | Lecture | B | Undergrad | y |
| 35616 | Advanced programming | Information Science | DB/Big Data | Applied | 1 | Lecture | B | Undergrad | y |
| 35617 | Advanced data analysis | Information Science | GIS | Applied | 1 | Lecture | A | Both | y |
| 35625 | Big Data applications | Information Science | DB/Big Data | Applied | 1 | Lecture | B | Both | y |
| 35626 | Introduction to Data Science | Information Science | Medical | Applied | 1.5 | Lecture | A | Both | |
| 35633 | Data Science introduction | Information Science | Data Mining / Visualization | Applied | 1 | Lecture | B | Both | y |
| 35712 | Digital Humanities | Information Science | Programming | Applied | 1 | Lecture | A | Both | y |
| 35728 | Introduction to Databases | Information Science | Programming | Applied | 1 | Lecture | A | Both | y |
| 35733 | Introduction to Databases | Information Science | Digital Humanities | Applied | 1 | Lecture | B | Both | y |
| 35809 | Geographic Information Systems | Information Science | Algorithms/Data Structures | Applied | 1 | Lecture | B | Grad | y |
| 35810 | Introduction to Databases | Information Science | ML/DL/NN | Applied | 1 | Lecture | B | Grad | |
| 35819 | Medical Informatics | Information Science | Digital Humanities | Applied | 1 | Lecture | B | Grad | y |
| 35858 | Data Visualization | Information Science | Digital Humanities | Exposure | 1 | Lecture | B | Grad | y |
| 35867 | Introduction to Programming - Python | Information Science | Digital Humanities | Exposure | 1 | Lecture | B | Grad | y |
| 35869 | Advanced Python | Information Science | BI/User Behavior | Applied | 1 | Lecture | A | Grad | y |
| 35879 | The Semantic Web | Information Science | BI/User Behavior | Applied | 1 | Lecture | A | Grad | y |
| 35880 | Algorithms 2 | Information Science | DB/Big Data | Applied | 1 | Lecture | A | Grad | |
| 35887 | Machine Learning | Information Science | Data Mining / Visualization | Applied | 1 | Lecture | B | Grad | y |
| 35890 | Introduction to digitization of textual and graphic information | Information Science | GIS | Applied | 1 | Lecture | B | Grad | y |
| 35954 | Selected issues in Digital humanities | Information Science | Data Mining / Visualization | Applied | 1 | Seminar | B | Grad | y |
| 35955 | Semantic web applications for digital humanities | Information Science | AI | Applied | 1 | Seminar | B | Grad | y |
| 55002 | Introduction to Statistics A | Management | Data Mining / Visualization | Applied | 1 | Lecture | A | Undergrad | y |
| 55003 | Introduction to Statistics B | Management | GIS | Applied | 1 | Lecture | B | Undergrad | y |
| 55006 | Introduction to probability | Management | GIS | Applied | 1 | Lecture | B | Both | y |
| 55089 | Service Management | Management | GIS | Applied | 1 | Lecture | A | Both | y |
| 55505 | Logistic Information System Management | Management | Data Mining / Visualization | Applied | 1 | Lecture | A | Both | y |
| 55703 | Information System Management in Industry | Management | GIS | Applied | 1 | Lecture | B | Both | y |
| 60066 | sadna statistit mitkademet | Psycology | Programming | Applied | 1 | Lecture | A | Undergrad | y |
| 66153 | Introduction to Statistics I | Economics | Programming | Applied | 1 | Lecture | A | Undergrad | y |
| 66154 | Introduction to Statistics II | Economics | Data Mining / Visualization | Applied | 1 | Lecture | B | Both | y |
| 66862 | Python for Economists Introductory | Economics | Bio | Applied | 0.5 | Lecture | A | Undergrad | y |
| 66863 | Python for Economists Advanced | Economics | Bio | Applied | 0.5 | Lecture | B | Undergrad | y |
| 66880 | Econometrics of Time Series | Economics | Programming | Applied | 1 | Lecture | B | Undergrad | |
| 70647 | Text Mining | Buisness Administration | Medical | Applied | 1 | Lecture | B | Physician Programming Certificate Studies | y |
| 70648 | Big data applications in Marketing | Buisness Administration | Bio | Applied | 1 | Lecture | A | Both | y |
| 70651 | big data management technics | Buisness Administration | Bio | Applied | 1 | Lecture | B | Both | y |
| 70673 | Visualization | Buisness Administration | Programming | Applied | 1 | Lecture | B | Both | y |
| 70677 | Global Information Systems (GIS) | Buisness Administration | ML/DL/NN | Applied | 1 | Lecture | B | Both | |
| 70680 | Data Mining with R | Buisness Administration | Image | Applied | 1 | Lecture | A | Both | y |
| 70784 | Data Warehousing | Buisness Administration | Medical | Applied | 1 | Lecture | B | Both | y |
| 70798 | Storage Systems | Buisness Administration | Programming | Core | 1 | Lecture | B | Both | y |
| 70833 | Introduced to Artificial Intelligence | Buisness Administration | Algorithms/Data Structures | Core | 1 | Lecture | B | Grad | y |
| 70949 | Data Mining and Information Disclosure | Buisness Administration | Statistics/Probablity | Core | 1 | Lecture | B | Grad | y |
| 75145 | Computer Applications in Documentation and Study of Place | Geography | Programming | Core | 1 | Lecture | B | Undergrad | y |
| 75335 | Advanced GIS A | Geography | Algorithms/Data Structures | Core | 2 | Lecture | A | Undergrad | y |
| 75373 | Introduction to GIS | Geography | Signal Processing | Core | 1.5 | Lecture | A | Undergrad | y |
| 75929 | Methods of Data Analysis | Geography | Statistical Theory | Core | 1 | Lecture | B | Grad | |
| 75967 | Python Scripting for GIS | Geography | Signal Processing | Core | 1.5 | Lecture | B | Grad | y |
| 80235 | *Introduction to Programming using python | Life | Signal Processing | Core | 1 | Lecture | A | Undergrad | |
| 80303 | Advanced Methods in Medical Image Processing | Life | Bio | Applied | 1 | Lecture | B | Both | y |
| 80376 | Matlab for Biologists | Life | Bio | Applied | 1 | Lecture | A | Both | y |
| 80392 | Computational Genomics | Life | Statistical Theory | Core | 1 | Lecture | A | Both | y |
| 80397 | Statistics and Data Science | Life | Algorithms/Data Structures | Core | 0.5 | Lecture | A | Both | |
| 80512 | Computational Biology | Life | Programming | Core | 1 | Lecture | B | Both | y |
| 80513 | Bioinformatics | Life | Statistical Theory | Core | 1 | Lecture | B | Both | y |
| 80515 | Introduction to Computing | Life | ML/DL/NN | Core | 1.5 | Lecture | A | Undergrad | y |
| 80534 | Biostatistics and Introduction to Clinical Trails | Life | Networks | Core | 1 | Lecture | B | Both | y |
| 80586 | Machine learning and applications for biological data analysis | Life | Signal Processing | Core | 1 | Lecture | B | Both | y |
| 80665 | Medical Informatics | Life | Image | Core | 1 | Lecture | B | Both | |
| 80672 | Advanced Tools to Genome Analysis | Life | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
| 80675 | Clinical Informatics - Clinical Data Mining | Life | Bio | Core | 0.5 | Lecture | A | Both | y |
| 80724 | Python Programming for Scientific Research | Life | Bio | Core | 1 | Lecture | B | Both | y |
| 80725 | Deep Learning and Artificial Intelligence in Medicine | Life | Optimization | Core | 1 | Lecture | B | Both | y |
| 81936 | Digital Image Processing | Medicine | Statistical Theory | Core | 1 | Lecture | B | Grad | y |
| 81958 | Text Mining for Cancer Research | Medicine | Algorithms/Data Structures | Core | 1 | Lecture | A | Grad | y |
| 83003 | MATLAB programming and applications | Engineering | ML/DL/NN | Core | Lab | B | Undergrad | y | |
| 83214 | Tools for Numerical Analysis | Engineering | ML/DL/NN | Core | 1 | Lecture | B | Undergrad | y |
| 83216 | Introduction to Statistics and Probability | Engineering | Algorithms/Data Structures | Core | 1.5 | Lecture | A | Undergrad | y |
| 83223 | Object Oriented Programming | Engineering | Medical | Core | 1 | Lecture | A | Undergrad | y |
| 83224 | Data Structures and Algorithms II | Engineering | Signal Processing | Core | 1.5 | Lecture | B | Undergrad | y |
| 83245 | Signals and Systems | Engineering | Speech | Core | 1.5 | Lecture | B | Undergrad | y |
| 83302 | Random Signals and Noise | Engineering | ML/DL/NN | Core | 1.5 | Lecture | A | Undergrad | y |
| 83320 | Digital Signal Processing I | Engineering | Signal Processing | Core | 1.5 | Lecture | B | Undergrad | y |
| 83321 | Statistical Algorithms for Signal Processing | Engineering | Image | Core | 1.5 | Lecture | B | Undergrad | y |
| 83412 | Genetics and Molecular Biology | Engineering | Signal Processing | Core | 1 | Lecture | B | Undergrad | y |
| 83414 | Biological data science | Engineering | Signal Processing | Core | 1.5 | Lecture | B | Undergrad | y |
| 83420 | Statistical Analysis of Data | Engineering | ML/DL/NN | Core | 1.5 | Lecture | B | Undergrad | y |
| 83456 | Design and Analysis of Algorithms | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Undergrad | y |
| 83459 | Software Engineering | Engineering | Signal Processing | Core | 1 | Lecture | B | Undergrad | y |
| 83620 | Information Theory | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
| 83622 | Introduction to Machine Learning | Engineering | Privacy | Core | 1 | Lecture | B | Both | y |
| 83623 | Signal Processing for Networks | Engineering | Programming | Core | 1 | Lecture | A | Both | y |
| 83624 | Digital Signal Processing II | Engineering | Statistics/Probablity | Core | 1.5 | Lecture | A | Both | y |
| 83629 | Digital Image Processing | Engineering | ML/DL/NN | Applied | 1 | Lecture | B | Both | y |
| 83633 | Digital Geometric Processing II | Engineering | ML/DL/NN | Applied | 1 | Lecture | B | Both | y |
| 83641 | Shape Optimization & Understanding | Engineering | Programming | Applied | 1 | Lecture | B | Both | |
| 83643 | Machine learning theory | Engineering | Programming | Applied | 1 | Lecture | A | Both | y |
| 83656 | Digital Processing of Geometry | Engineering | Statistics/Probablity | Core | 1 | Lecture | A | Both | y |
| 83665 | Computational Biology | Engineering | Programming | Applied | 1 | Lecture | A | Both | y |
| 83666 | Control ofTheory for Biological Systems | Engineering | Programming | Core | 1 | Lecture | A | Both | y |
| 83674 | Quantom Machine Learning | Engineering | Statistics/Probablity | Core | 1.5 | Lecture | B | Both | y |
| 83676 | Data Mining | Engineering | Algorithms/Data Structures | Core | 1 | Lecture | B | Both | y |
| 83692 | Social networks | Engineering | Image | Core | 1 | Lecture | B | Undergrad | y |
| 83805 | Continuous and Combinatorial Optimization | Engineering | Statistics/Probablity | Core | 1.5 | Lecture | A | Teacher Certification | y |
| 83806 | Random Processes | Engineering | Finance/Econometrics | Core | 1.5 | Lecture | B | Grad | y |
| 83807 | Quantum Computing | Engineering | Statistics/Probablity | Core | 1.5 | Lecture | A | Grad | y |
| 83841 | Statistical Machine Learning | Engineering | Algorithms/Data Structures | Core | 1 | Lecture | A | Grad | y |
| 83843 | deep generative models | Engineering | Data Mining / Visualization | Core | 1 | Lecture | A | Grad | y |
| 83867 | Probabilistic Methods and Algorithms | Engineering | Statistics/Probablity | Core | 1 | Lecture | B | Grad | y |
| 83876 | Decision Support Systems in medical imaging | Engineering | ML/DL/NN | Core | 1 | Lecture | B | Grad | y |
| 83880 | Seminar/Advanced Topics in Signal Processing | Engineering | Programming | Applied | 1 | Lecture | B | Grad | y |
| 83881 | Python Programming Workshop | Engineering | Programming | Applied | 1 | Lecture | B | Grad | |
| 83882 | Deep Learning | Engineering | Data Mining / Visualization | Applied | 1 | Lecture | A | Grad | y |
| 83887 | Spatial Signal Processing | Engineering | Networks | Applied | 1 | Lecture | B | Grad | y |
| 83888 | Computer Vision | Engineering | Statistics/Probablity | Core | 1 | Lecture | B | Grad | y |
| 83889 | Advanced Topics in Statistical Signal Processing | Engineering | Statistics/Probablity | Core | 1 | Lecture | A | Grad | y |
| 83900 | Discovery Theory | Engineering | Statistics/Probablity | Core | 1 | Lecture | A | Grad | y |
| 83901 | Introduction to Data Science with Python | Engineering | Networks | Core | 1 | Lecture | B | Grad | y |
| 83905 | Seminar/Advanced Topics in Machine Learning and Data Processing | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
| 83906 | Independence-based Blind Source Separation | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Grad | |
| 83907 | Advanced topics in deep learning | Engineering | Optimization | Core | 1 | Lecture | B | Grad | y |
| 83908 | Advanced topics in differential privacy | Engineering | Algorithms/Data Structures | Core | 1 | Lecture | B | Grad | y |
| 83920 | Parallel Computation using a GPU | Engineering | Algorithms/Data Structures | Core | 1 | Lecture | A | Grad | y |
| 83979 | Statistics and Data Analysis | Engineering | Programming | Core | 1 | Lecture | B | Grad | y |
| 84107 | Statistics and Probability for Chemists (SPC) | Chemistry | Programming | Core | 1 | Lecture | A | Undergrad | y |
| 84190 | Introduction to Computers in Chemistry | Chemistry | Algorithms/Data Structures | Core | 1 | Lecture | B | Undergrad | y |
| 84328 | Computational Chemistry | Chemistry | Programming | Core | 1 | Lecture | B | Undergrad | y |
| 84846 | Introduction to Cheminformatics | Chemistry | Programming | Core | 1 | Lecture | B | Grad | y |
| 86156 | Probability and Statistics for Physicists | Physics | Algorithms/Data Structures | Core | 1.5 | Lecture | B | Undergrad | y |
| 86164 | Introduction to Computers in Physics | Physics | Networks | Core | 1 | Lecture | A | Undergrad | y |
| 86605 | Data science for physicists | Physics | Statistics/Probablity | Core | 1.5 | Lecture | A | Both | y |
| 86771 | Machine Learning for Physicists | Physics | Bio | Core | 1.5 | Lecture | B | Both | |
| 88151 | Computer Applications in Math | Math | Algorithms/Data Structures | Core | 1 | Lecture | B | Undergrad | |
| 88153 | Introduction to Mathematical Programming | Math | Programming | Core | 1 | Lecture | B | Undergrad | y |
| 88165 | Introduction to Probabilitiy and Statistics | Math | Algorithms/Data Structures | Core | 2 | Lecture | B | Undergrad | y |
| 88170 | Introduction to Computing | Math | Networks | Core | 1.5 | Lecture | A | Undergrad | y |
| 88174 | Introduction to Object Oriented Programming | Math | ML/DL/NN | Core | 1 | Lecture | B | Undergrad | y |
| 88263 | Introduction to Statistics | Math | NLP/Text | Core | 2 | Lecture | A | Grad | y |
| 88280 | Data Structures and Algorithms | Math | Game Theory | Core | 2 | Lecture | A | Grad | y |
| 88584 | Image Processing | Math | BI/User Behavior | Core | 1 | Lecture | B | Both | y |
| 88615 | Introduction to Probabilitiy and Statistics | Math | Robotics | Core | 1 | Lecture | B | Both | y |
| 886210 | Risk management and time series | Math | DB/Big Data | Core | 1 | Lecture | Summer | Both | y |
| 88622 | Introduction to Probabilitiy and Statistics | Math | NLP/Text | Core | 1.5 | Lecture | A | Grad | y |
| 88623 | Probability and Stochastic Processes | Math | NLP/Text | Core | 1.5 | Lecture | B | Grad | y |
| 88624 | Analysis of Statistical Data | Math | NLP/Text | Core | 1 | Lecture | Summer | Both | y |
| 88631 | Introduction to Probabilitiy and Statistics | Math | Image | Core | 0.5 | Lecture | A | Both | y |
| 886788 | Data Science Seminar | Math | NLP/Text | Core | 1 | Seminar | B | Grad | y |
| 886960 | Introduction to Programming using python | Math | DB/Big Data | Core | 1 | Lecture | B | Undergrad | y |
| 886961 | Python Programming Workshop | Math | AI | Core | 1 | Lecture | A | Both | y |
| 886970 | Data Processing, Analysis and Visualization | Math | DB/Big Data | Core | 1 | Lecture | B | Undergrad | y |
| 886980 | Networks and Complexity in the Real World | Math | Speech | Core | 1 | Lecture | A | Grad | y |
| 88760 | Introduction to Statistics | Math | Statistical Theory | Core | 1 | Lecture | A | Grad | y |
| 88761 | Introduction to Statistics II | Math | ML/DL/NN | Core | 1 | Lecture | B | Grad | y |
| 88775 | Statistical Theory | Math | DB/Big Data | Core | 1.5 | Lecture | B | Grad | y |
| 88778 | Network Science | Math | NLP/Text | Core | 1.5 | Lecture | B | Grad | y |
| 88779 | Random graphs and networks | Math | Robotics | Core | Lecture | B | Grad | ||
| 88780 | Supervised and Unsupervised Learning | Math | NLP/Text | Core | 1 | Lecture | A | Grad | y |
| 887810 | Introduction to artificial intelligence | Math | Statistics/Probablity | Core | 1 | Lecture | B | Both | y |
| 88784 | Optimization | Math | AI | Core | 1.5 | Lecture | A | Both | y |
| 88962 | Probability and Stochastic Processes | Math | ML/DL/NN | Exposure | 1.5 | Lecture | A | Undergrad | y |
| 889630 | Random processes on graphs | Math | NLP/Text | Core | 1.5 | Lecture | B | Undergrad | |
| 89110 | Introduction to Computer Science | Computer Science | Finance/Econometrics | Applied | 1.5 | Lecture | A | Undergrad | y |
| 89111 | Introduction to Object Oriented Programming | Computer Science | Signal Processing | Core | 1 | Lecture | B | Grad | y |
| 89120 | Data Structures | Computer Science | Bio | Applied | 1 | Lecture | B | Both | y |
| 89210 | Algorithmic Programming I | Computer Science | ML/DL/NN | Applied | 1 | Lecture | A | Both | y |
| 89211 | Algorithmic Programming II | Computer Science | Bio | Applied | 1 | Lecture | B | Grad | y |
| 89220 | Algorithms 1 | Computer Science | Bio | Applied | 1.5 | Lecture | A | Both | y |
| 89255 | Graph Theory | Computer Science | Geometry | Core | 1.5 | Lecture | A | Both | y |
| 89262 | General Probability | Computer Science | Geometry | Core | 1 | Lecture | A | Both | y |
| 89264 | Biostatistics | Computer Science | Geometry | Core | 1.5 | Lecture | A | Both | y |
| 89276 | Numeric Methods | Computer Science | ML/DL/NN | Core | 1 | Lecture | B | Undergrad | |
| 89312 | Programming in a multi-processor environment | Computer Science | Data Mining / Visualization | Core | 1 | Lecture | B | Both | y |
| 89322 | Algorithms 2 | Computer Science | ML/DL/NN | Core | 1 | Lecture | B | Both | y |
| 89350 | Introduction to Communication Networks | Computer Science | Networks | Core | 1 | Lecture | A | Both | y |
| 89362 | General Statistics | Computer Science | APPLICATIONS | Applied | 1 | Lecture | B | Both | y |
| 894043 | Advanced Seminar in natural lagquage processing | Computer Science | APPLICATIONS | Applied | 1 | Seminar | A | Both | y |
| 894044 | Research seminar in natural language processing – part 2 | Computer Science | Statistics/Probablity | Applied | 1 | Seminar | A | Undergrad | y |
| 89408 | Advanced Seminar in Algorithmic Game Theory | Computer Science | Programming | Applied | 1 | Seminar | A | Undergrad | |
| 894112 | User Behavior Machine Learning Algorithms Seminar | Computer Science | Networks | Core | 1 | Seminar | B | Undergrad | y |
| 89421 | Seminar/Strategic Planning for Robots | Computer Science | NLP/Text | Applied | 1 | Seminar | B | Undergrad | |
| 894483 | Seminar in Machine Learning and Speech Processing | Computer Science | APPLICATIONS | Exposure | 1 | Seminar | B | Undergrad | y |
| 89452 | Seminar/Web, Crowd and Big Data Management | Computer Science | DB/Big Data | Applied | 1 | Seminar | A | Undergrad | y |
| 894531 | Seminar: Learning Algorithms and Natural Language Processing | Computer Science | DB/Big Data | Applied | 1 | Seminar | B | Undergrad | y |
| 89454 | Structures for Semantics | Computer Science | Statistics/Probablity | Core | 1 | Seminar | A | Undergrad | y |
| 89460 | Seminar: From Text to Information | Computer Science | Statistics/Probablity | Applied | 1 | Seminar | B | Undergrad | |
| 89471 | Seminar in Pattern Recognition | Computer Science | Signal Processing | Core | 1 | Seminar | A | Undergrad | |
| 894851 | Advanced Seminar in Text Understanding | Computer Science | ML/DL/NN | Core | 1 | Seminar | B | Undergrad | y |
| 89493 | Seminar/Applied Machine Learning for NLP | Computer Science | Statistics/Probablity | Applied | 1 | Seminar | B | Undergrad | y |
| 89511 | Machine Learning | Computer Science | Statistics/Probablity | Applied | 1 | Lecture | A | Both | y |
| 89512 | Computational Biology | Computer Science | Statistics/Probablity | Applied | 1 | Lecture | B | Both | y |
| 89519 | Machine Learning for Healthcare | Computer Science | Statistics/Probablity | Applied | 1 | Lecture | B | Both | y |
| 89542 | Management of Big Web Data | Computer Science | Statistics/Probablity | Applied | 1 | Lecture | A | Both | y |
| 89560 | Image Processing | Computer Science | Programming | Applied | 1 | Lecture | B | Both | y |
| 89570 | Artificial Intelligence | Computer Science | Statistics/Probablity | Applied | 1 | Lecture | A | Both | y |
| 89581 | Database Systems | Computer Science | Statistics/Probablity | Applied | 1 | Lecture | B | Both | y |
| 89594 | Formal Representations for Natural Languages | Computer Science | Programming | Applied | 1.5 | Lecture | B | Both | |
| 89608 | Python for Economists Advanced | Computer Science | Programming | Applied | 1 | Lecture | B | Both | y |
| 89641 | Topics in Information Theory | Computer Science | NLP/Text | Core | 1 | Lecture | A | Both | |
| 89654 | Advanced Methods in Machine Learning | Computer Science | NLP/Text | Core | 1 | Lecture | B | Both | |
| 896541 | Practical topics in Machine Learning | Computer Science | NLP/Text | Core | 1 | Lecture | B | Both | y |
| 89679 | Workshop in Databases | Computer Science | NLP/Text | Core | 1 | Workshop | A | Both | y |
| 89680 | Natural Language Processing | Computer Science | ML/DL/NN | Core | 1.5 | Lecture | A | Both | y |
| 89685 | Introduction to Robotics | Computer Science | Bio | Core | 1 | Lecture | A | Both | y |
| 89687 | Deep Learning Methods for Texts and Sequences | Computer Science | Medical | Core | 1 | Lecture | B | Both | y |
| 896871 | Deep learning | Computer Science | Image | Core | 1 | Lecture | B | Both | y |
| 896872 | Deep Neural Network for Computer Vision | Computer Science | ML/DL/NN | Core | 1 | Lecture | B | Both | y |
| 896873 | Reinforcement Learning | Computer Science | Programming | Applied | 1 | Lecture | A | Both | y |
| 896874 | Deep learning for perception | Computer Science | Programming | Applied | 1 | Lecture | A | Grad | y |
| 89688 | Statistical Machine Translation | Computer Science | Image | Core | 1 | Lecture | B | Both | y |
| 89919 | Applied Probabilistic Models in Computer Science | Computer Science | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
| 89950 | Advanced Topics in Artificial Intelligence | Computer Science | ML/DL/NN | Core | 1 | Lecture | B | Both | y |
| 89991 | Machine Learning Colloqium | Computer Science | ML/DL/NN | Core | Colloquium | Annual | Both | y | |
| 999025 | Data Science for Jurists | Law | Medical | Applied | 1.5 | Lecture | A | Both |
LEGEND
Target Audience:
- Core: Students interested in understanding in depth how core data science methods and algorithms work, and in developing such methods.
Typical student profile: rigorous programming, mathematical and algorithmic skills. - Applied: Students interested in learning applied data science methods for the purpose of applying them extensively in various domains.
Typical student profile: basic programming and analytical skills, which may be acquired through relevant courses listed here for this audience. - Exposure: Students interested in high-level understanding of data science methods and their potential in various fields, in applying them using tools which do not require programming, and in social and ethical aspects of data science.
Typical student profile: students from broad disciplines who may not have programming or mathematical skills, or – for some courses – students interested in social, legal or ethical aspects of data science.
Points:
- Usually, each point corresponds to two hours in one semester.
Category:
- Fundamentals:
- Statistics/Probablity
- Programming
- Math
- Algorithms/Data Structures
- CORE
- Signal Processing
- ML/DL/NN – Machine Learning, Deep Learning, Neural Networks
- Optimization
- DB/Big Data
- Networks
- Data Mining / Visualization
- Statistical Theory
- AI – Artificial Intelligence (Agents, Problem Solving, Planning, Search algorithms, …)
- Privacy
- Game Theory
- APPLICATIONS
- NLP/Text
- Image – Image, Video Processing, Pattern Recognition
- Speech – Speech and Audio Processing
- Bio – BioInformatics, Computational Biology
- Finance/Econometrics –
- GIS – Geographic Information Systems (Geography)
- BI/User Behavior – Business Intelligence
- Digital Humanities
- Medical
- Robotics
Disclaimer