Program
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.
Applications to Y-DATA Program 2024-25 are open
Start your professional journey in Applied Data Science with Y-DATA
Structure
Y-DATA structure
01
Study specific topics in data analysis and machine learning in short, dedicated courses (4‑12 weeks each), covering topics across all the range from ML foundations to advanced, state‑of‑the-art applications. All the courses are taught in an applicative and hands-on manner and include extensive practice.
Courses
Choose one of the real‑life data projects offered by our industry partners, and work on it throughout the year. You will receive guidance through weekly meetings with experienced industry mentors provided by us, as well as periodic meetings with the project’s data owner.
Projects
Become familiar with the current scientific research and advancements through research seminars, where you will also engage in in‑depth discussions and exploration of the most recent advancements in the field.
Research seminars
02
03
program
Study program
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.
Probability & Statistics for Data Science
Probability & Statistics for Data Science
Supervised Learning
Supervised Learning
Unsupervised Learning & Applications
Unsupervised Learning & Applications
1 week
2
3
4
Industry Talks
5
6
7
8
9
10
11
12
13
14
3 hours * 6 weeks
4 hours * 8 weeks
3 hours * 8 weeks
4 hours * 6 weeks
Lior Sidi
Noa Lubin
Guy Shtar
Segev Arbiv
Rachel Buchuk
Shaul Solomon
Kosta Rozen
Python for Data Processing
Python for Data Processing
Fall semester
8 hours * 20 weeks
Deep Learning
Deep Learning
ML Foundations: Vision, NLP and more
ML Foundations: Vision, NLP and more
Research Seminars
Research Seminars
1 week
2
3
4
Advanced topics in ML: Generative AI
Advanced topics in ML: Generative AI
5
6
7
8
9
10
11
12
13
14
Omri Allouche
Oren Elisha
Karin Brisker
Inbar Huberman
4 hours * 14 weeks
2 hours * 14 weeks
2 hours * 6 weeks
Intro to Industry Projects
Spring semester
3 hours * 6 weeks
Inbar Huberman
Yuval Belfer
Niv Haim
Advanced topics in ML: Causal Inference
Advanced topics in ML: Causal Inference
3 hours * 6 weeks
Daniel Nevo
Advanced topics in ML: MLOps
Advanced topics in ML: MLOps
3 hours * 6 weeks
Ishai Rosenberg
Project Presentations
Career workshops
Python for Data Processing
4 hours | 6 weeks
4 hours | 6 weeks
Course Description
Python for Data Processing (Py4DP) course introduces the 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. Besides being technical, the course also covers data science processes and best practices. This includes structuring machine learning projects, doing exploratory data analysis, dealing with data and more. On a conceptual side course also covers presenting results and assuring that projects are well-organized and flow smoothly. After completion of the course, students should have at their disposal the tools required to begin diving in to the world of modelling and classic DS tasks, such as the ability to perform exploratory analysis of moderately sized dataset (up to tens of Gb), including data cleaning, analysis of individual variables, their relations, visualizations and feature construction.
Python for Data Processing (Py4DP) course introduces the 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. Besides being technical, the course also covers data science processes and best practices. This includes structuring machine learning projects, doing exploratory data analysis, dealing with data and more. On a conceptual side course also covers presenting results and assuring that projects are well-organized and flow smoothly. After completion of the course, students should have at their disposal the tools required to begin diving in to the world of modelling and classic DS tasks, such as the ability to perform exploratory analysis of moderately sized dataset (up to tens of Gb), including data cleaning, analysis of individual variables, their relations, visualizations and feature construction.
Probability & Statistics for Data Science
3 hours | 6 weeks
3 hours | 6 weeks
Course Description
This course teaches the basics of probability theory and statistics from the point of view of modern data science. It aims to develop a good intuition of random events and variables, common distributions and their properties, estimators and statistical tests, and ways of building end evaluating probabilistic models. The course assumes pre-existing knowledge of core concepts in probability and statistics and aims to build on top of these and provide the additional perspective required to handle the probabilistic nature of machine learning.
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 to use statistical methods to estimate their parameters and make predictions. The students would also be prepared to apply the methods of probability and statistics in the subsequent courses on machine learning.
This course teaches the basics of probability theory and statistics from the point of view of modern data science. It aims to develop a good intuition of random events and variables, common distributions and their properties, estimators and statistical tests, and ways of building end evaluating probabilistic models. The course assumes pre-existing knowledge of core concepts in probability and statistics and aims to build on top of these and provide the additional perspective required to handle the probabilistic nature of machine learning.
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 to use statistical methods to estimate their parameters and make predictions. The students would also be prepared to apply the methods of probability and statistics in the subsequent courses on machine learning.
Supervised Learning
4 hours | 8 weeks
4 hours | 8 weeks
Course Description
Beginning the dive into “classical” Machine Learning topics, Supervised Learning offers an in-depth meeting with major ML algorithms and tools for regression and classification: Linear and logistic regression, decision trees, ensemble models and more. In addition, the course aims to provide an understanding of the position of the tools taught in the DS process as a whole as well as practical understanding of the steps and contents of a DS task and best practices to approach one. The majority of machine learning tasks are supervised learning (SL) problems - problems in which labelled 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 major algorithm families and implement SL algorithms ourselves in order to grasp their mechanics.
Beginning the dive into “classical” Machine Learning topics, Supervised Learning offers an in-depth meeting with major ML algorithms and tools for regression and classification: Linear and logistic regression, decision trees, ensemble models and more. In addition, the course aims to provide an understanding of the position of the tools taught in the DS process as a whole as well as practical understanding of the steps and contents of a DS task and best practices to approach one. The majority of machine learning tasks are supervised learning (SL) problems - problems in which labelled 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 major algorithm families and implement SL algorithms ourselves in order to grasp their mechanics.
Unsupervised Learning & Applications
3 hours | 8 weeks
3 hours | 8 weeks
Lecturers
Guy Shtar
Machine Learning Expert at Salesforce
Segev Arbiv
Principal Data Scientist at SimilarWeb, Mentor and Lecturer
Guy Shtar
Machine Learning Expert at Salesforce
Segev Arbiv
Principal Data Scientist at SimilarWeb, Mentor and Lecturer
Course Description
Completing the dive into “classical” Machine Learning topics, Unsupervised Learning course introduces the students to the skills and tools required to tackle the ocean of unlabelled data. The course teaches how to derive insights and construct models that do not rely on the availability of pre-labelled data, covering techniques including clustering, dimensionality reduction, anomaly detection, and more.
In addition to the core algorithms of classic Unsupervised ML, this course will include several practical sessions delving into applications of the algorithms taught here and elsewhere. In a series of practical sessions, the course will provide an opportunity to tackle hands-on the challenges of unlabelled and disordered data, and how to translate it into meaningful insights and models.
Completing the dive into “classical” Machine Learning topics, Unsupervised Learning course introduces the students to the skills and tools required to tackle the ocean of unlabelled data. The course teaches how to derive insights and construct models that do not rely on the availability of pre-labelled data, covering techniques including clustering, dimensionality reduction, anomaly detection, and more.
In addition to the core algorithms of classic Unsupervised ML, this course will include several practical sessions delving into applications of the algorithms taught here and elsewhere. In a series of practical sessions, the course will provide an opportunity to tackle hands-on the challenges of unlabelled and disordered data, and how to translate it into meaningful insights and models.
Deep Learning
4 hours | 14 weeks
4 hours | 14 weeks
Course Description
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 both an understanding of the fundamental DL theory, and hands-on experience building neural networks using PyTorch using industry best practices.
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 both an understanding of the fundamental DL theory, and hands-on experience building neural networks using PyTorch using industry best practices.
ML Foundations: Vision, NLP and more
2 hours | 14 weeks
2 hours | 14 weeks
Lecturers
Inbar Huberman
PhD from The Hebrew University of Jerusalem
Oren Elisha
Data science manager at Forter
Karin Brisker
Data Scientist at Microsoft Israel
Inbar Huberman
PhD from The Hebrew University of Jerusalem
Oren Elisha
Data science manager at Forter
Karin Brisker
Data Scientist at Microsoft Israel
Course Description
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.
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.
Research Seminars
2 hours | 6 weeks
2 hours | 6 weeks
Course Description
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.
List of topics for each seminar is curated on the spot based on state-of-the-art developments and speaker availability.
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.
List of topics for each seminar is curated on the spot based on state-of-the-art developments and speaker availability.
Advanced topics in ML: Generative AI
3 hours | 6 weeks
3 hours | 6 weeks
Lecturers
Yuval Belfer
Developer Advocate at AI21 Labs
Niv Haim
Machine Learning researcher at the Weizmann Institute of Science
Yuval Belfer
Developer Advocate at AI21 Labs
Niv Haim
Machine Learning researcher at the Weizmann Institute of Science
Course Description
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.
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.
Advanced topics in ML: Causal Inference
3 hours | 6 weeks
3 hours | 6 weeks
Course Description
It is well known that correlation does not imply causation. However, often the main interest lies in the impact of an intervention. Furthermore, with the increase in the amounts of data collected, issues concerning systematic bias become more and more important in modern data science. In this course, we will first define what causal effects are, and then present a reservoir of causal inference methods to estimate these effects accompanied by real-life examples. We will also learn about basic terms as confounding and selection bias, and how to identify their potential presence and adapt our analysis plan using directed acyclic graphs.
It is well known that correlation does not imply causation. However, often the main interest lies in the impact of an intervention. Furthermore, with the increase in the amounts of data collected, issues concerning systematic bias become more and more important in modern data science. In this course, we will first define what causal effects are, and then present a reservoir of causal inference methods to estimate these effects accompanied by real-life examples. We will also learn about basic terms as confounding and selection bias, and how to identify their potential presence and adapt our analysis plan using directed acyclic graphs.
Applications to Y-DATA Program 2024-25 are open
Start your professional journey
in Applied Data Science with Y-DATA
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