Learn
Practice
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at Nebius Academy

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OUR COURSE

What's inside the course

12,500₪
4 months
AI-Powered
Data Science
4 months
Starts October 28
160 lecture hours + assignments
Classes at the Tel Aviv University campus with live streaming available
Focus on statistical thinking, data exploration, feature engineering, classical ML models and pipelines, GenAI applications and tools
Ideal for analysts, STEM graduates, software developers looking to build a strong foundation in data science
Price: 12,500₪
ABOUT

About us

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Nebius Academy offers an applied program in machine learning and AI
Founded by the AI infrastructure company Nebius, it bridges the gap between short-term online courses and full-time MSc studies. More than 300 of our graduates now work at major companies in Israel and worldwide.

Since 2018, our program — formerly known as Y-DATA — has been taught by experts from leading universities and the tech industry in hybrid format at the Tel Aviv University campus. It combines hands-on training with real practice using both Nebius and industry-standard tools.
Target audience

Who is this for?

Software developers and engineers
Researchers and advanced degree graduates
Professionals from IDF tech units
Fresh university graduates looking for DS roles
Advantages

Our advantages

We offer a unique combination of advantages not found in other programs
Career support
Career support
Career guidance and soft-skills workshops built for real hiring needs — 92% of grads now work in DS/ML
Real
projects
Real projects
Real-world industry
project for your portfolio
Cloud resources
Cloud resources
Access cloud computing resources with Nebius
Professional community
Professional community
Stay connected through ongoing involvement with a network of experts
Advantage
Theoretical foundations
Real-world practice
Latest AI research
Careful candidate selection
Job-friendly format
Industry mentors
Compute access
Up-to-date course
Nebius Academy
Online courses
Bootcamps
Advanced degrees
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This comparison is a general overview — some private offline programs or advanced academic degrees may offer more than what’s listed here. However, the table provides a fair look at the different paths into a data science career.
Admission process

What do we offer?

01
Strong theoretical foundations
  • Understanding from the ground up of core ML principles.
  • Specialized courses (6–14 weeks) covering topics from ML foundations to advanced, state-of-the-art applications.
02
Practical experience through real-world projects
  • Full-cycle data science project in the industry.
  • Experience working with real data in real industry environments.
  • Proven industry experience.
03
Extensive hands-on practice
  • Learning through hands-on application of common tools and concepts.
  • Weekly coding assignments for in-depth understanding.
04
Community and networking
  • Access to our active community of graduates and industry mentors.
  • Career and innovation opportunities — meeting industry insiders.
05
Fluency in Python and all common DS tools and methods
  • Python basics: Scikit-learn, pandas, matplotlib, numpy and more.
  • Extensive experience in neural networks and their applications in NLP and computer vision, up to and including generative models.
06
Selective admission process to create a winning team
  • Diverse student community where people learn from each other and find opportunities.
  • Strict entry prerequisites for a solid starting position and quick progress.
admission scheme

Admission process

We ensure a motivating and supportive learning environment through a careful selection process.

To achieve results, we make sure our candidates have the time and ability to succeed in the intensive program we offer.
01
Application
The application steps are the same for all tracks — just choose the one that best fits your learning goals.

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.
See the new course
02
Online test
Take an online test assessing your analytical and programming skills.
03
Interview
Meet the 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.
feedback

Our alumni

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Andrey Nikitin
Data Science Manager, Cyera
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.
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Liad Yosef
Principal Software Engineer, Shopify
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 AI-Powered Data Science (formerly 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.
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Lior Tabori
Senior Data Scientist, Stampli
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.
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Rachel Shalom
Principal Data Scientist, Dell Technologies
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, AI-Powered Data Science (formerly Y-DATA) looked realistic. I chose AI-Powered Data Science (formerly Y-DATA) because one year is better in terms of understanding things. Also, I could combine it with my previous work.
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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 AI-Powered Data Science (formerly 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.
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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.
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Amit Alon
Data Scientist at KHealth
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 AI-Powered Data Science (formerly 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 AI-Powered Data Science (formerly Y-DATA) gave me a really good background so I can bring a lot to the table in addition to my research background.
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Arseny Levin
Fraud Detection Lead at DoubleVerify
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 AI-Powered Data Science (formerly Y-DATA) courses we had exactly the right balance of practice and theory.
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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.
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Jonathan Ohnona
Data Scientist at eToro
I'm an Engineer. I studied math and physics, and financial engineering. I choose AI-Powered Data Science (formerly 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 AI-Powered Data Science (formerly 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.
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Tal Ben-Yehuda Heletz
Deep Learning Researches at Trigo
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.

AI-Powered Data Science (formerly Y-DATA) was exactly right for me - it let me combine my background with computer science and strong data science foundations.
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OUR PROJECTS
Industry projects
For bundle students only
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Global Text Edits & Feedback
in Wordtune AI Writing Assistant
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Population based
book model
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Automated Slide Retrieval, Adaptation
and Presentation Generation
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Anomalous SAR Image
Detection
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PHI Detection in Medical
Imaging (DICOM)
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AI-Powered Knowledge
Enhancement for Hospitals
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Developing Fairness and Reliability Metrics in LLM Embedding Space
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Cellular insights into Tertiary Lymphoid Structures via single-cell and spatial omics
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Time to Cancel
Prediction
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Text-to-SQL Conversion for WAF Data Analysis
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Entity & Sentiment Extraction
for Support Conversations
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Medical procedure
extraction from documents
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Enhancing ETA and energy consumption prediction through driving pattern analysis
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A comparative analysis for xai methods package
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Automatic product comprehension with llms
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Document validation and extraction
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Text clustering for care management quality performance
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Grinvision foundation model
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Genomic profile representation for prediction of drug response
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Generative creation of newsletter campaigns
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Client application classification using exposed information
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Behavioral cross-session user identification
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SQL injection detection in real time
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Healthscope: medical classification and contextual analysis
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Content recommendation engine for emails
Team

Our team

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Elena Bunina
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Professor, Department of Mathematics, Bar Ilan University, Member of Nebius Board of Directors
Read More
Scientific Advisor
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Dr. Inbar Huberman
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PhD from The Hebrew University of Jerusalem
Read More
Lecturer at Deep Learning & GenAI Hand on Applications course
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Karin Brisker
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Data Scientist at Microsoft Israel
Read More
Lecturer at Deep Learning & GenAI Hand on Applications course
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Kosta Rozen
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Product Analytics Team Lead at Google
Read More
Lecturer at Python for Data Processing course
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Lior Sidi
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Co-Founder & CTO at stealth startup
Read More
Lecturer at Classical ML course
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Noa Lubin
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Director of Data Science at Fido
Read More
Lecturer at Classical ML course
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Dr. Omri Allouche
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Head of Research at Gong.io, Data Scientist and Lecturer
Read More
Lecturer at Deep Learning & GenAI Foundation course
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Segev Arbiv
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Principal Data Scientist at SimilarWeb, Mentor and Lecturer
Read More
Lecturer at Classical ML course
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Dr. Tomer Gazit
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Data Science Team Lead at Hello Heart
Read More
Lecturer at Probability & Statistics
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Serj Smorodinsky
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Data Science Team Lead at Loris.ai
Read More
Lecturer at Data Science in Production course
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Kosta Rozen
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Product Analytics Lead at Waze
Lecturer at Python for Data Processing course
Partners

Our partners

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The Association of Engineers, Architects and Graduates in Technological Sciences in Israel
Intro to DS course in partnership with AEAI.
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College of Management Academic Studies
Academic courses to MBA students in the field of data science.
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Nebius
Is a leading AI-centric public cloud platform designed to support the entire machine learning lifecycle.
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Tel Aviv University
Partnership with Blavatnik school of Computer Science.
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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.
popular questions

FAQ

How does the application process work?
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What level of math knowledge is expected from the candidates?
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What level of statistics and probability knowledge is expected from the candidates?
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What level of coding skills is required to enter the program? Do you require knowledge of specific languages?
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Can I apply with zero coding experience?
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What is the time commitment for this program? Can I combine it with work or academic studies?
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What is the cost of the program?
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What is the language of the program?
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Where does the program take place?
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Is there a referral program?
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What is Nebius?
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What is Nebius’ role in AI education?
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Powered by Nebius
Nebius builds AI infrastructure that accelerates innovation globally and at scale. Based in Europe, listed on Nasdaq, operating globally.
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Kosta Rozen
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Product Analytics Lead at Waze
Lecturer at Python for Data Processing course