Our curriculum offers a well-rounded education in data analysis and machine learning, starting from the fundamental principles of classic ML and progressing towards the cutting-edge applications of Deep Learning and generative AI. To ensure our students stay ahead of the curve, our curriculum undergoes regular updates to align with market needs and reflect the latest advancements in the field.
In our program, you will engage in focused, short-duration courses lasting 4 to 14 weeks. Each course dives into specific topics, including Supervised and Unsupervised Learning, Deep Learning and advanced ML applications all the way to recent developments in generative AI. Our courses are designed to be hands-on and practical, enabling students to apply their knowledge through extensive practice.
Y-DATA offers an intensive curriculum of over 250 hours designed to equip students with the necessary skills for entry to mid-level data science positions within the Israeli tech industry.
Program
Applications to 2023-24 class are open.
Study program
Spring Semester
FALL SEMESTER
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Kosta Rozen
Kosta Rozen
Omri Allouche
Inbar Huberman
Yuval Belfer
Niv Haim
Ekaterina Artemova
Inbar Huberman
Anna Lapidus
Lior Sidi
Noa Lubin
Guy Shtar
Segev Arbiv
Rachel Buchuk
Python for Data Processing
Probability Theory and Statistics for Data Science
Supervised Learning
Industry Talks
Unsupervised Learning
Intro to Industry Projects
Deep Learning
DL Foundations: Vision, NLP and more
Research seminar
Advanced topics in ML: Generative AI
Advanced topics in ML: Dialog Systems
Project Presentations
Career workshops
Course List
Python for Data Processing
Lecturer: Kosta Rozen
Python for Data Processing (Py4DP) course introduces main tools forming Python stack for data science and machine learning. The course is focused on practical skills and core packages for data science in general and exploratory data analysis in particular: Jupyter, numpy, pandas and matplotlib. Basic understanding of scipy, sklearn, dask, tensorflow and other packages and tools is also provided, as well as very brief review of tools beyond Python, for example Scala and Spark.
After completion of the course, students should be able to configure working environment, strategically setup machine learning or data science project and efficiently perform exploratory analysis of moderately sized dataset (up to tens of Gb), including data cleaning, analysis of individual variables, their relations, visualizations and feature constructions.
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Product Analytics Lead at Waze
Course Description
3 hours | 6 weeks
Probability Theory and Statistics for Data Science
This introductory course teaches the basics of probability theory and statistics. It aims to develop a good intuition of random events and variables, common distributions and their properties, estimators and statistical tests. The emphasis is made on the tools widely applied in data science, such as maximum likelihood estimation and Bayesian inference.
Besides the classical paper-and-pencil problems, there will be assignments in the Python ecosystem. After completing the course, the students should be able to propose probabilistic models to describe randomness in life, and statistical methods to estimate their parameters. The students would also be ready to apply the learned methods the subsequent courses on machine learning.
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Lecturer: Rachel Buchuk
Statistics and Operations Researcher at the Hebrew University
Course Description
3 hours | 6 weeks
Supervised Learning
The majority of machine learning tasks are supervised learning (SL) problems - problems in which labeled datasets are used. Starting with a given dataset, for which the correct answers are known, the SL algorithm iteratively makes predictions on the training data and is corrected by the “teacher”, until it is able to make accurate predictions on data not seen before. Despite the growing popularity of deep learning, many existing tasks are solved efficiently by a wide spectrum of other algorithms and models. In the Supervised Learning course we will learn several such algorithm families and implement SL algorithms ourselves in order to grasp their mechanics. This course is preceded by two lectures of Intro to ML, which will introduce the modern ML field and learn all the required concepts which are not covered by the first three introductory courses.
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Lecturer: Lior Sidi
Lecturer: Noa Lubin
Senior Data Scientist at Wix
Director at Data Science at Fido
Course Description
4 hours | 7 weeks
Unsupervised learning
This advanced course aims to provide its students with a highly valuable skill with multiple real-world applications: Unsupervised Machine Learning teaches how to derive insights and construct models that do not rely on the availability of pre-labelled data. The course covers techniques including pattern recognition, clustering, dimensionality reduction, matrix factorization, anomaly detection, and Genetic Algorithms. The course will be accompanied by examples of cutting-edge applications in various business-oriented high-tech applications.
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Lecturer: Guy Shtar
Lecturer: Segev Arbiv
Machine Learning Expert at Salesforce
Principal Data Scientist at SimilarWeb, Mentor and Lecturer
Course Description
3 hours | 7 weeks
Industry talks
This course introduces students to one of the most popular and fast-growing fields of machine learning – deep learning. It aims to provide the students with an understanding of the underlying principles of modern neural networks, their construction and applications (including NLP and computer vision). It covers common network architectures including convolutional and recurrent networks, backpropagation, regular and variational autoencoders, embeddings and more. The course will grant students understanding of DL best practices, and DL hardware and environments, including providing familiarity with PyTorch.
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Lecturer: Omni Allouche
Head of Research at Gong.io, Data Scientist and Lecturer
Course Description
4 hours | 14 weeks
Deep Learning
This course introduces students to one of the most popular and fast-growing fields of machine learning – deep learning. It aims to provide the students with an understanding of the underlying principles of modern neural networks, their construction and applications (including NLP and computer vision). It covers common network architectures including convolutional and recurrent networks, backpropagation, regular and variational autoencoders, embeddings and more. The course will grant students understanding of DL best practices, and DL hardware and environments, including providing familiarity with PyTorch.
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Lecturer: Omni Allouche
Head of Research at Gong.io, Data Scientist and Lecturer
Course Description
4 hours | 14 weeks
DL Foundations: Vision, NLP and more
This series of talks and workshops is an ongoing companion and extension for Deep Learning course, and is a continuation of the same whole. While the core DL course provides fundamental understanding of neural networks, their theoretical foundations, capabilities and architectures, this course offers an in-depth look at the practical aspects of NNs and their uses.

Over the course of the semester, we’ll explore the basic concepts behind computer vision and NLP tasks, understand their inner workings and take in-depth look at specific use-cases and major DL-based tasks.

We will also take a practical look at using DL tools to solve a variety of problems and have sessions dedicated to best practices in CV and NLP. Over the course of the semester we will have several guest lectures by topic-specific experts who will present their know-how on the practicalities of specific use-cases and applications.

Note: due to the inter-connected nature of DL and Foundation courses, on some weeks there are switches between the times of the two tracks to accommodate specific time constraints.
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Lecturer: Inbar Huberman
Lecturer: Anna Lapidus
PhD from The Hebrew University of Jerusalem
Data Scientist and researcher
Course Description
2 hours | 14 weeks
Research seminars
The research seminars offer an opportunity for students to become familiar with current scientific research and advancements through a series of meetings, in which we’ll engage in in-depth discussions and exploration of the most recent advancements in the field. Selected researchers from the faculty of TAU, HUJI and other universities and experienced researchers from the industry will present papers representing state-of-the-art research in their field and lead a discussion of the topics covered. Students will have the opportunity to engage in-depth with papers through reading, presenting, reviewing and implementing selected topics.
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Lecturer: Inbar Huberman
PhD from The Hebrew University of Jerusalem
Course Description
3 hours | 6 weeks
Advanced topics in ML: Generative AI
This course provides a comprehensive overview of the landscape of generative AI, covering both the vision and textual domains. The syllabus includes an in-depth exploration of the fundamentals of generative models, from autoencoders to GANs and diffusion models, as well as the latest advancements in text-guided generation. The course also delves into the fundamentals of text generation with a focus on large language models, emphasizing practical usage and providing an understanding of the methods they were trained on. By the end of the course, students will have a broad understanding of generative AI and its practical applications.
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Lecturer: Yuval Belfer
Lecturer: Niv Haim
Developer Advocate at AI21 Labs
Machine Learning researcher at the Weizmann Institute of Science
Course Description
3 hours | 6 weeks
Advanced topics in ML: Dialog Systems
This advanced academic course in natural language understanding and dialogue systems is designed to provide a comprehensive overview of the key concepts, methods, and technologies that underpin modern approaches to natural language processing and conversational agents. The goal of the course is to explore and explain how neural language models are used for dialogue systems.

The course covers the subject starting from the structure and function of existing dialogue systems, delving into natural language understanding tasks such as intent recognition and slot filling, followed by exploring conversational applications such as question answering, and finally exploring recent developments – ChatGPT and issues of cross-lingual and multimodal NLU.
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Lecturer: Ekaterina Artemova
Post doctoral researcher at MaiNLP Research Lab @CIS LMU
Course Description
3 hours | 6 weeks
Join us in 2023-2024 class!
Applications to 2023-24 class are open.
Join us in 2023-2024 class!
Applications to 2023-24 class are open
Apply Now