Applications to Y-DATA Program 2025-26 are open
Start your journey in Applied Data Science with Y-DATA’s 56-day course
Learn Practice Launch
Practice
Real projects by top companies
Format
Frontal studies in Tel-Aviv
NIS Tuition [discount options available]
29,000
Schedule
Tue and Fri
Weekly hours [workload]
20-25
Next Class
2025 Oct
About
It bridges the gap between short-term online courses and a full-time MSc-level program. Y-DATA is designed by top-notch experts from the academy and the industry and taught at Tel Aviv University campus. The program is localized to enhance the Israeli tech community and the global AI ecosystem.
Y-DATA is an intensive one-year career advancement program in data science
What is Y-DATA
What
is Y-DATA
Target audience
Fresh university graduates looking for DS roles
Professionals from tech IDF units
Researchers and advanced degree graduates
Code professionals from IT industry
Who is this for?
Advantages
ongoing involvement with a community of experts
Networking and more
Access to cloud computation resources
Cloud resources
Real-world industry project for your portfolio
Real projects
and soft-skills workshops tailored to industry needs
Dedicated career center
rate in DS/ML positions
92% employement
employed in top tech companies
250+ graduates
Over 6 years’ proven experience in Israel
6 years
of intensive in-person training
250 hours
What will we provide
Applications to Y-DATA Program 2025-26 are open
Start your professional journey
in Applied Data Science with Y-DATA
Our projects
Industry projects
Web technology detection
Finding Genes with Similar Functional Homology
Text Classification Modeling Improvements
Dynamic Range Prediction for Vehicles
Diastole/Systole Indetification in ECG
Wix Restaurants Segmentation
LLM Agent-Managed Interface for Business Monitoring
Detecting Faulty Sensor Data
Hourly Demand Prediction
Product Clustering and Fraud Risk Assessment
Oral Anomaly Detection
Botnets Site Clustering
Multi-domain Digital Pathology Stain Style Transfer
Classification and Prediction of Epileptic Activity in EEG
Multi-domain digital pathology styletransfer
Admission scheme
August - September 2025
Meet Y-DATA team in person or online and tell us more about your background, experience, and interests, as well as your motivation and goals for the program. A few technical questions might be asked during the interview.
Interview
July-August 2025
Take an online test assessing your analytical and programming skills. Next online tests will take place in July-August 2025.
Online Test
Submit your application by filling the form. You will receive an email providing further information about the test and the following stages of the process.
Application
03
02
01
To achieve our results, we need to ensure our candidates have the availability and capability of succeeding in the intense study program we offer.
Y-DATA employs a rigorous selection process to ensure
a motivating and encouraging learning environment.
Admission process
Feedback
Our Alumni
Great experience so far! Personally, for me, the course exceeded my expectations. I usually stay away from courses since I'm a self learner. Courses usually spend too much time on the unimportant parts (too much history, too much theory, repetitive exercises etc.)
However, during Y-DATA courses we had exactly the right balance of practice and theory.
However, during Y-DATA courses we had exactly the right balance of practice and theory.
Arseny Levin
The course is great, I think it's the best professional course I have taken and for me personally, it's a good substitution for a master's degree (for now). Even though I'm already working as a Data Scientist i still learn new things, there are always fields that I'm less proficient in and the course fills the gap.
Fraud Detection Lead at DoubleVerify
Tal Ben-Yehuda Heletz
It was obvious to me that math is the field for me. I did my B.Sc and M.Sc in math. In the industry, you can do a lot with math, but you must have knowledge in computer science as well.
Y-Data was exactly right for me - it let me combine my background with computer science and strong data science foundations.
Y-Data was exactly right for me - it let me combine my background with computer science and strong data science foundations.
Deep Learning Researches at Trigo
Andrey Nikitin
Data scientist at Wix
Liad Yosef
Client Architect at Duda
You know they say go with your passion, right? I've been programming since I was a kid, but I never really dealt with Data Science or Machine Learning before Y-Data. I already knew the math part of the introductory courses but they were so fast-paced that I wasn't bored and quickly enough we got into supervised learning and deep learning. This gave me the tools to do things that I couldn't have done before, and let me explore and widen the area of my thoughts.
I'm an Engineer. I studied math and physics, and financial engineering. I choose Y-DATA because I wanted a better understanding of the algorithms. When you have access to machine learning techniques, you have access to more tools, allowing you to do more things. For instance, in my field, in time-series analysis, you want to better predict and better focus. Studying in Y-DATA is like building a muscle. You need to work on a muscle to be a better, stronger person. It's a very good program because it shows many things.
Jonathan Ohnona
Data Scientist at eToro
Nir Aviv
Software Engineer and Data Scientist at Fiverr
For me, the most important aspect of the program is the industry project. There's nothing like working on a real problem with experts in the field. I feel that the classes prepared me well for this kind of hands-on data science work. In particular, the variety of lecturers from tech and academia is definitely an advantage of the program.
Lior Tabori
Data Scientist at Agoda
I wanted to get into the world of data and data science. I had a feeling that this field is mine. That was my main purpose, to get the most out of this program and out of the industry project. I think our learning group was most important in my experience. It was small but diverse. Everyone is a specialist in something a little bit different so we really helped each other. There are very good students in this program.
I realized that as a product manager in a travel tech startup, I needed heavy tools to analyze data, do predictions and more. So I started checking all kinds of data science boot camps, and machine learning academies, but unlike most of them, Y-DATA looked realistic. I chose Y-DATA because one year is better in terms of understanding things. Also, I could combine it with my previous work.
Rachel Shalom
Data Scientist at Owlytics
Yechiel Levy
CTO at OptimalQ
In a young startup like the one I own, we are doing a bit of everything, from big data to DevOps to data science. As we grow bigger. algorithms get more complicated. I joined Y-DATA to understand my data team better. Now I can understand their work better, know how they're approaching the problem. It helps us move along much faster and bridges the gap between management, engineering and data science teams.
Ido Nissim
Data Engineer at AllCloud
I think the very best thing about the course is the people. The selection of the students for the course was really good. Heterogeneous people from all kinds of fields and different backgrounds - that's really good. We had some projects together, and worked as groups, which was a good way to get to know other people. We were all sitting in the classroom together, talking and trying to figure out how to do the homework later on. It's great.
Amit Alon
I was looking for the best place to get ML Background, to learn more techniques, better and wider knowledge, especially in deep learning, which I didn’t know everything about. I chose Y-Data because it was presented as a program that can mediate the gap between academia and industry. This was exactly what I was looking for. I don’t have professional experience in ML but Y-Data gave me a really good background so I can bring a lot to the table in addition to my research background.
Data Scientist at KHealth
Team
Our Team
Eden Shochat
/team/eden-shochat
Elena Bunina
/team/elena-bunina
Ira Cohen
/team/ira-cohen
Kira Radinsky
/team/kira-radinsky
Lior Rokach
/team/lior-rokach
Daniel Nevo
/team/daniel-nevo
Guy Shtar
/team/guy-shtar
Inbar Huberman
/team/inbar-huberman
Karin Brisker
/team/karin-brisker
Kosta Rozen
/team/kosta-rozen
Lior Sidi
/team/lior-sidi
Niv Haim
/team/niv-haim
Noa Lubin
/team/noa-lubin
Omri Allouche
/team/omri-allouche
Oren Elisha
/team/oren-elisha
Rachel Buchuk
/team/rachel-buchuk
Segev Arbiv
/team/segev-arbiv
Shaul Solomon
/team/shaul-solomon
Shuki Cohen
/team/shuki-cohen
Talia Tron
/team/talia-tron
Yoel Zeldis
/team/yoel-zeldis
Yuval Belfer
/team/yuval-belfer
Dina Bavli
/team/dina-bavli
Efrat Egozi-Levi
/team/efrat-egozi-levi
Ishai Rosenberg
/team/ishai-rosenberg
Maya Malamud
/team/maya-malamud
Moshe Mash
/team/moshe-mash
Netta Lieber
/team/netta-lieber
Nir Ben Zvi
/team/nir-ben-zvi
Shani Kotler
/team/shani-kotler
Tom Haramaty
/team/tom-haramaty
Tomer Ahrak
/team/tomer-ahrak
Yair Meidan
/team/yair-meidan
Eden Shochat
Equal Partner at Aleph
Equal Partner at Aleph
Eden is an equal partner at Aleph, an early stage venture capital fund with $344 million under management, focused on serving Israeli entrepreneurs who want to build big, scalable, global businesses.
Prior to founding Aleph, Eden was a general partner at Genesis where he focused on seed-stage investments and was a co-founder of face.com, the world leader in massive, web-scale face recognition in social networks, acquired by Facebook. He also co-founded and was the CTO of Aternity, acquired by Riverbed, where he led the strategic product direction for its dominant user-experience monitoring platform.
Entrepreneurship is a passion for Eden, who started The Junction that was voted as the #1 startup program in Israel. He is one of the organizers of the annual Geekcon geeks conference and also teaches at the IDC (Herzliya interdisciplinary center) Zell entrepreneurship program.
Prior to founding Aleph, Eden was a general partner at Genesis where he focused on seed-stage investments and was a co-founder of face.com, the world leader in massive, web-scale face recognition in social networks, acquired by Facebook. He also co-founded and was the CTO of Aternity, acquired by Riverbed, where he led the strategic product direction for its dominant user-experience monitoring platform.
Entrepreneurship is a passion for Eden, who started The Junction that was voted as the #1 startup program in Israel. He is one of the organizers of the annual Geekcon geeks conference and also teaches at the IDC (Herzliya interdisciplinary center) Zell entrepreneurship program.
Elena Bunina
Professor, Department of Mathematics, Bar Ilan University
Professor, Department of Mathematics, Bar Ilan University
Y-DATA Scientific Advisor
Ira Cohen
Co-Founder, Chief Data Scientist at Anodot
Co-Founder, Chief Data Scientist at Anodot
Ira is the chief data scientist at Anodot, working on learning algorithms for analyzing time series signals at large scale — from anomaly detection, clustering and forecasting.
Prior to Anodot, Ira was Chief Data Scientist at HP Software, defining and developing machine learning & big data initiatives. Before that Ira was a senior researcher at HP Labs, leading R&D in machine learning and data mining for analyzing large scale event streams.
He is the author of numerous patents and publications and holds a PhD in Electrical and Computer Engineering from the University of Illinois at Urbana Champaign.
Prior to Anodot, Ira was Chief Data Scientist at HP Software, defining and developing machine learning & big data initiatives. Before that Ira was a senior researcher at HP Labs, leading R&D in machine learning and data mining for analyzing large scale event streams.
He is the author of numerous patents and publications and holds a PhD in Electrical and Computer Engineering from the University of Illinois at Urbana Champaign.
Kira Radinsky
ChairWoman & Chief Technology Officer at Diagnostic Robotics
ChairWoman & Chief Technology Officer at Diagnostic Robotics
Dr. Kira Radinsky is a chairman and CTO of Diagnostic Robotics where the most advanced technologies in the field of artificial intelligence are harnessed to make healthcare better, cheaper, and more widely available. She also co-founded SalesPredict, acquired by eBay in 2016, that was the leader in the field of predictive marketing building solutions leveraging large-scale data mining to predict sales conversions.
One of the up-and-coming voices in the data science community, she is pioneering the field of medical data mining. Dr. Radinsky gained international recognition for her work at Microsoft Research, where she developed predictive algorithms that recognized the early warning signs of globally impactful events, including political riots and disease epidemics. In 2013, she was named to the MIT Technology Review’s 35 Young Innovators Under 35, in 2015 as Forbes 30 under 30 rising stars in enterprise technology, and in 2016 selected as "woman of the year" by Globes.
She is a frequent presenter at global tech events, including TEDx, Wired, Strata Data Science, Techcrunch and academic conferences, and she publishes in the Harvard Business Review.
Radinsky serves is a board member in: Israel Security Authorities, Maccabi Research Institute, and technology board of HSBC bank.
Dr. Radinsky also serves as visiting professor at the Technion, Israel’s leading science and technology institute, where she focuses on the application of predictive data mining in medicine.
Lior Rokach
Professor of software and information systems at Ben-Gurion University
Professor of software and information systems at Ben-Gurion University
Lior Rokach is a data scientist and a Full Professor of Information Systems and Software Engineering. He currently serves as the chair of the Department of Software and Information System Engineering at the Ben-Gurion University of the Negev. His research interests lie in the areas of Data Science, Machine Learning, Big Data, Deep Learning and Data Mining.
Prof. Rokach received a B.Sc. (summa cum laude, 1998), M.Sc. (cum laude, 1999) and PhD (2004) from Tel Aviv University. Since 2005 he has been a faculty member at Ben-Gurion University. In addition, he has had several visiting positions - the most recent at the College of Information Sciences and Technology at the Pennsylvania State University, University Park, PA.
Prof. Rokach is the author of over 300 peer reviewed papers in leading journals, conference proceedings, patents, and book chapters. He has established the machine learning lab and the big data lab which promote innovative adaptations of machine learning and data science methods to create the next generation of intelligent systems.
Prof. Rokach received a B.Sc. (summa cum laude, 1998), M.Sc. (cum laude, 1999) and PhD (2004) from Tel Aviv University. Since 2005 he has been a faculty member at Ben-Gurion University. In addition, he has had several visiting positions - the most recent at the College of Information Sciences and Technology at the Pennsylvania State University, University Park, PA.
Prof. Rokach is the author of over 300 peer reviewed papers in leading journals, conference proceedings, patents, and book chapters. He has established the machine learning lab and the big data lab which promote innovative adaptations of machine learning and data science methods to create the next generation of intelligent systems.
Daniel Nevo
Senior Lecturer at the Department of Statistics at Tel Aviv University
Senior Lecturer at the Department of Statistics at Tel Aviv University
Lecturer at Causal Inference course
Dr. Daniel Nevo is a Senior Lecturer at the Department of Statistics and Operations Research at Tel Aviv University since 2018. Dr. Nevo’s research focuses on causal inference in widespread domains, and specifically on developing and implementing causal inference methods for real-life problems.
Dr. Daniel Nevo is a Senior Lecturer at the Department of Statistics and Operations Research at Tel Aviv University since 2018. Before that he was a postdoctoral fellow at Harvard Departments of Biostatistics and Epidemiology (2016-2018). Daniel received his BA (Statistics and Economics, 2009) and MA (Statistics, 2011), both Magna Cum Laude, from the Hebrew University of Jerusalem. He received his PhD in Statistics from the Hebrew University of Jerusalem (2016).
Daniel’s research focuses on causal inference in widespread domains, and specifically on developing and implementing causal inference methods for real-life problems. As of 2022, he published 35 peer-reviewed papers.
Daniel has been collaborating with clinicians, epidemiologists, economists, and computer scientists in academia and health organizations to reach conclusions about causal effects from rich datasets.
Dr. Daniel Nevo is a Senior Lecturer at the Department of Statistics and Operations Research at Tel Aviv University since 2018. Before that he was a postdoctoral fellow at Harvard Departments of Biostatistics and Epidemiology (2016-2018). Daniel received his BA (Statistics and Economics, 2009) and MA (Statistics, 2011), both Magna Cum Laude, from the Hebrew University of Jerusalem. He received his PhD in Statistics from the Hebrew University of Jerusalem (2016).
Daniel’s research focuses on causal inference in widespread domains, and specifically on developing and implementing causal inference methods for real-life problems. As of 2022, he published 35 peer-reviewed papers.
Daniel has been collaborating with clinicians, epidemiologists, economists, and computer scientists in academia and health organizations to reach conclusions about causal effects from rich datasets.
Guy Shtar
Machine Learning Expert at Salesforce
Machine Learning Expert at Salesforce
Lecturer in Unsupervised Learning course
Guy Shtar holds a Ph.D in Machine learning as well as an M.Sc. degree in Software and Information Systems Engineering from the Ben-Gurion University of the Negev in 2016.
Guy Shtar holds a Ph.D in Machine learning as well as an M.Sc. degree in Software and Information Systems Engineering from the Ben-Gurion University of the Negev in 2016.
His research interests include machine learning and deep learning in the medical and cybersecurity fields, as well as geographical information systems.
Guy Shtar holds a Ph.D in Machine learning as well as an M.Sc. degree in Software and Information Systems Engineering from the Ben-Gurion University of the Negev in 2016.
His research interests include machine learning and deep learning in the medical and cybersecurity fields, as well as geographical information systems.
Inbar Huberman
PhD from The Hebrew University of Jerusalem
PhD from The Hebrew University of Jerusalem
Lecturer at Deep Learning course
Inbar has just finished her PhD at the Hebrew University of Jerusalem, her research is focused on image processing and machine learning.
Inbar received her Ms.c in computer science from the Hebrew University and her Bs.c in applied mathematics from Bar-Ilan University, both graduated cum laude. She published several papers in computer vision top-tier conferences.
During her PhD studies, she was titled best teacher assistant.
Inbar received her Ms.c in computer science from the Hebrew University and her Bs.c in applied mathematics from Bar-Ilan University, both graduated cum laude. She published several papers in computer vision top-tier conferences.
During her PhD studies, she was titled best teacher assistant.
Karin Brisker
Data Scientist at Microsoft Israel
Data Scientist at Microsoft Israel
Lecturer at ML foundations
Kosta Rozen
Product Analytics Lead at Waze
Product Analytics Lead at Waze
Lecturer at Python for Data Processing course
Kosta holds a B.Sc and M.Sc. in Industrial Engineering and Management from Ben Gurion Universirty (Summa Cum Laude both). He won several academic awards and scholarships, and has published multiple research papers in academic journals, including best paper of 2018 in IEEE Transactions on Semiconductor Manufacturing. Kosta is also a Certified Scrum Master and Project Management Professional (PMP).
In his role as Product Analytics Lead at Waze, Kosta uses data to improve every aspect of Waze - one of most popular navigation apps in the world.
Before joining Waze, Kosta worked as a Data Scientist at Intel and as a Lecturer at Ben Gurion University.
In his role as Product Analytics Lead at Waze, Kosta uses data to improve every aspect of Waze - one of most popular navigation apps in the world.
Before joining Waze, Kosta worked as a Data Scientist at Intel and as a Lecturer at Ben Gurion University.
Lior Sidi
Senior Data Scientist at Wix
Senior Data Scientist at Wix
Lecturer at Supervised Learning course
Lior has years of experience and works closely with companies to improve their product and operations using data science.
He co-founded Braincast to help product managers to make impactful decisions by explaining causality in meaningful events.
Lior holds BSc and MSc in software and information engineering department from Ben Gurion University, where he researched the appliance of deep learning, embedding, and adversarial learning in the fraud and cybersecurity domains.
He co-founded Braincast to help product managers to make impactful decisions by explaining causality in meaningful events.
Lior holds BSc and MSc in software and information engineering department from Ben Gurion University, where he researched the appliance of deep learning, embedding, and adversarial learning in the fraud and cybersecurity domains.
Niv Haim
Machine Learning researcher at the Weizmann Institute of Science
Machine Learning researcher at the Weizmann Institute of Science
Lecturer at Generative AI course
Niv Haim is a computer vision and machine learning researcher at the Weizmann Institute of Science, where he earned his PhD under the guidance of Prof. Michal Irani.
Niv's research focuses on developing algorithms and models to analyze and interpret visual data, with a particular interest in understanding memorization in neural networks and the intersection of theory and practice in deep learning. His works were published in top-tier machine learning conferences, including NeurIPS, ICML, ICCV, and ECCV.
Prior to his PhD studies, Niv received an MSc in theoretical astrophysics under the guidance of Prof. Boaz Katz and worked with Prof. Yaron Lipman on Geometric Deep Learning. Niv holds a BSc in computer science and physics from the Technion, where he was a member of the Lapidim excellence program.
Niv's research focuses on developing algorithms and models to analyze and interpret visual data, with a particular interest in understanding memorization in neural networks and the intersection of theory and practice in deep learning. His works were published in top-tier machine learning conferences, including NeurIPS, ICML, ICCV, and ECCV.
Prior to his PhD studies, Niv received an MSc in theoretical astrophysics under the guidance of Prof. Boaz Katz and worked with Prof. Yaron Lipman on Geometric Deep Learning. Niv holds a BSc in computer science and physics from the Technion, where he was a member of the Lapidim excellence program.
Noa Lubin
Director of Data Science at Fido
Director of Data Science at Fido
Lecturer in Supervised Learning course
Noa is a Data Scientist, currently working as Director of Data Science at Fido. Formerly worked as a researcher at NASA, at Diagnostic Robotics, Amazon, Elbit and IAI. Her main topics of specialisation and focus are NLP, healthcare and space.
Noa is an AI leader, social entrepreneur, lecturer and space enthusiast.
Volunteering as CTO at Tod’aers - combining AI and space research for sustainable technological developments.
Previously worked as Machine Learning Team Lead at Diagnostic Robotics, at Amazon, NASA, Elbit Systems and the Israeli Aerospace Industry.
Computer Science Master's degree, Bar-Ilan University (Magna Cum Laude), NLP thesis advised by Prof. Yoav Goldberg.
Electrical Engineering Bachelor's degree, Technion (Summa Cum Laude).
Omri Allouche
Head of Research at Gong.io, Data Scientist and Lecturer
Head of Research at Gong.io, Data Scientist and Lecturer
Lecturer at Deep Learning course
Dr. Omri Allouche heads the Research department at Gong.io, helping sales organizations improve their performance by providing actionable, data-driven insights using machine learning. He also teaches Applied Data Science at Bar Ilan University.
Dr. Omri Allouche heads the Research department at Gong.io, helping sales organizations improve their performance by providing actionable, data-driven insights using machine learning.
He also teaches Applied Data Science at Bar Ilan University.
Omri holds a Ph.D. in Computational Ecology from the Hebrew University (cum laude). He won several academic awards and scholarships, including the Clore fund, and his research papers had been cited over 2,000 times.
Dr. Omri Allouche heads the Research department at Gong.io, helping sales organizations improve their performance by providing actionable, data-driven insights using machine learning.
He also teaches Applied Data Science at Bar Ilan University.
Omri holds a Ph.D. in Computational Ecology from the Hebrew University (cum laude). He won several academic awards and scholarships, including the Clore fund, and his research papers had been cited over 2,000 times.
Oren Elisha
Data science manager at Forter
Data science manager at Forter
Lecturer at ML foundations
Rachel Buchuk
Statistician at SZMC
Statistician at SZMC
Lecturer at Probability Theory and Statistics for Data Science
Rachel is a statistician in the IBD Epidemiology Research group at Shaare Zedek Hospital. She uses advanced methods in survival analysis and causal inference to gain a deeper knowledge about inflammatory bowel diseases (Crohn's disease and ulcerative colitis).
Rachel holds an MA Cum Laude in Statistics and Operations Research from the Hebrew University. In her thesis, she focused on statistical methodology for estimating hospital-acquired infections.
During her Master’s, she taught multiple courses ranging from basic probability and linear algebra to statistical inference and advanced probability, for which she received an award for outstanding TA.
Rachel has also worked for the Bank of Israel, doing statistical research and benchmarking for consumer goods across the retail industry.
Rachel holds an MA Cum Laude in Statistics and Operations Research from the Hebrew University. In her thesis, she focused on statistical methodology for estimating hospital-acquired infections.
During her Master’s, she taught multiple courses ranging from basic probability and linear algebra to statistical inference and advanced probability, for which she received an award for outstanding TA.
Rachel has also worked for the Bank of Israel, doing statistical research and benchmarking for consumer goods across the retail industry.
Segev Arbiv
Principal Data Scientist at SimilarWeb, Mentor and Lecturer
Principal Data Scientist at SimilarWeb, Mentor and Lecturer
Lecturer at Unsupervised Learning course
Segev Arbiv is a Data Scientist with expertise in the fields of Machine Learning and Computer Vision. He has a BSc. in Electrical Engineering from the Technion, and pursuing an MSc in these fields.
During the last few years, Segev is working at SimilarWeb in which he applies Deep Learning techniques and various statistical inference tools to overcome day to day challenges, such as NLP and estimations.
Prior to that, he was a researcher in the Image Processing\Computer Vision fields at AOL. Segev is passionate about everything related to numbers, lecturing and interacting with other passionate personas.
During the last few years, Segev is working at SimilarWeb in which he applies Deep Learning techniques and various statistical inference tools to overcome day to day challenges, such as NLP and estimations.
Prior to that, he was a researcher in the Image Processing\Computer Vision fields at AOL. Segev is passionate about everything related to numbers, lecturing and interacting with other passionate personas.
Shaul Solomon
Lead Data Scientist at DockTech
Lead Data Scientist at DockTech
Lecturer at Probability & Statistics
Shuki Cohen
Data Scientist at AI21 Labs
Data Scientist at AI21 Labs
Guest Lecturer
Shuki Cohen is a Data Scientist at AI21 Labs, focusing on advanced NLP algorithms to develop writing and reading AI technologies. Prior to that, Shuki worked at SimilarWeb, applying machine learning and statistical algorithms to predict and estimate users behavior. Shuki holds an M.Sc. degree in IE majoring in Data Science from Tel Aviv University.
Talia Tron
Data Scientist & NLP researcher @ K-Health
Data Scientist & NLP researcher @ K-Health
Lecturer at project workshops
Yoel Zeldis
Algorithm Developer
Algorithm Developer
Lecturer at ML Tools and Applications
Yuval Belfer
Developer Advocate at AI21 Labs
Developer Advocate at AI21 Labs
Lecturer at Generative AI course
Yuval is a Developer Advocate at AI21 Labs, deeply involved in advancing the frontiers of Natural Language Processing.
He holds an MSc in Computer Science from the Weizmann Institute of Science, underscoring his firm grounding in the field. Prior to his role at AI21 Labs, he served as a Chip Designer at Amazon, honing his ability to deliver efficient, impactful solutions.
Yuval’s professional passion lies in the realm of generative AI. He actively contributes to making this intricate field more comprehensible and accessible to diverse audiences. With an engaging style and in-depth expertise, he navigates the complexities of AI, bringing clarity and insight to both peers and newcomers in the field.
He holds an MSc in Computer Science from the Weizmann Institute of Science, underscoring his firm grounding in the field. Prior to his role at AI21 Labs, he served as a Chip Designer at Amazon, honing his ability to deliver efficient, impactful solutions.
Yuval’s professional passion lies in the realm of generative AI. He actively contributes to making this intricate field more comprehensible and accessible to diverse audiences. With an engaging style and in-depth expertise, he navigates the complexities of AI, bringing clarity and insight to both peers and newcomers in the field.
Dina Bavli
Data Scientist & consultant
Data Scientist & consultant
Project mentor
Efrat Egozi-Levi
Data science consultant and ML expert
Data science consultant and ML expert
Project mentor
Ishai Rosenberg
Co-Founder and CTO
Co-Founder and CTO
Lecturer at MLops course
Maya Malamud
Senior Data Scientist and Researcher at Timna
Senior Data Scientist and Researcher at Timna
Project mentor
Moshe Mash
AI researcher
AI researcher
Project mentor
Netta Lieber
Data Science Team Leader
Data Science Team Leader
Project mentor
Nir Ben Zvi
Deep Learning and Computer Vision Consultant
Deep Learning and Computer Vision Consultant
Project mentor
Shani Kotler
Data / AI Consultant
Data / AI Consultant
Project mentor
Tom Haramaty
Machine Learning Consultant
Machine Learning Consultant
Project mentor
Tomer Ahrak
Data Science Researcher at Neosec
Data Science Researcher at Neosec
Project mentor
Tomer is passionate about learning new domains and techniques, from building scalable data pipelines and algorithmic solutions for mapping and automotive apps to data modeling and analysis for financial risk assessment. As a self-learner with multidisciplinary R&D experience, he specialises at tackling hard problems in new domains and working on innovative engineering solutions for products at scale.
Yair Meidan
Data Scientist / Applied ML Researcher
Data Scientist / Applied ML Researcher
Project mentor
Partners
Our Partners
The Association of Engineers, Architects and Graduates in Technological Sciences in Israel
Intro to DS course in partnership with AEAI
College of Management Academic Studies
Academic courses to MBA students in the field of data science by Y-DATA
Nebius
Is a leading AI-centric public cloud platform designed to support the entire machine learning lifecycle.
Tel Aviv University
Partnership with Blavatnik school of Computer Science.
Helmholtz Information & Data Science Academy
HIDA connects and serves as the roof to 6 newly founded data science research schools linked by a network of 14 national research centers and 17 top-tier universities across Germany.
Amazon Web Services
AWS credits for Y-DATA students
popular questions
FAQ
The application process consists of three steps. First, candidates apply online through our website. Second, candidates take an online test. The test assesses analytical and basic programming skills and contains undergraduate level statistics and probability questions, data analysis questions, and short coding challenges (candidates can select from a few popular programming languages that our platform supports). The test must be completed within three and a half hours.
Y-DATA will offer two separate 4-day time windows to take the online test during July-August. Candidates who pass the online test will be invited to an in-person interview with Y-DATA team members. The interview is an opportunity for our team to learn more about candidates' background, experiences, and interests.
We assume our students have at least a bachelor's STEM degree or its equivalent.
We, therefore, expect all the candidates to have full knowledge of the first-year university-level material in math. In order to ensure a suitable level of pre-existing knowledge, we require all candidates to complete the Mathematics for Machine Learning specialization on Coursera prior to the beginning of their studies.
Candidates who are accepted to the program will have the cost of the course deducted from their tuition fee.
We assume our students have at least a bachelor's STEM degree or its equivalent.
We, therefore, expect all the candidates to have full knowledge of the first-year university-level material in probability and statistics. You may want to review basic topics in those subjects before taking our online test.
During the program, we won’t teach these topics from scratch, but we will provide a quick recap before diving into the more advanced topics required for later ML courses.
We require some experience in at least one of the common programming languages and an understanding of common data structures. Programming tasks are a large portion of the online test and assume existing programming background. During the program itself, the courses and home assignments will be in Python.
Candidates should have a basic knowledge of Python before taking the test. Some potential resources for people with no prior Python knowledge to get started include:
- Programming for Everybody (Getting Started with Python)
- Python Data Structures
- Python Essential Training
Candidates without any coding experience won’t be able to take part in our program. If you have no coding experience but are looking to get yourself started in this domain, you can start by taking online coding courses and establishing some baseline capability in Python before applying to Y-DATA.
The weekly workload consists of 9 hours of frontal lessons (one mid-week evening + Friday morning) and approximately 20 hours of independent work on assignments and projects. We expect at least 80% attendance at lectures and seminars.
Due to the workload, in addition to the 9 hours of frontal lectures, we require our participants to have at least one full weekday available for work on classwork and industry project. Therefore, candidates are required to reduce full-time positions to 80% at most, and we strongly recommend reduction to 50% for the program's duration.
In some exceptional cases and based on individual assessment, we may allow select students to take the program without an industry project while maintaining a full-time job.
Participating in the program while working towards an academic degree is possible but depends greatly on the intensity of individual programs.
The cost of the Y-DATA program is NIS 29,000.
In order to make the program affordable, we also offer success-based model of tuition (SBE). Full cost of the program in SBE is NIS 45,000.
The program is taught in English in its entirety. We have international students and faculty, and thus expect all our students to be fluent in English.
Y-DATA classes take place in Tel Aviv, on TAU campus.
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