Course starts June 9th 2024

Registration is open now

Intro to Data Science

This introductory course lays the groundwork for anyone interested in exploring the fundamental concepts surrounding data science and AI, gaining understanding of the ideas and principles behind the technological terms and providing an access point to get started in the field.
About the course
6 weeks


This course aims to provide an opening to the world of Data Science by offering an entry-level perspective on a wide range of DS and ML topics. The course provides an introduction and hands-on experience with multiple common DS tools, as well as understanding of core concepts of modelling and working with data.
Over the course of 6 weeks, Intro to DS course will provide practical experience and understanding of core ML tasks such as classification, regression, and clustering, as well as overview of the capabilities of Deep Learning and state-of-the-art developments.
The course lays the groundwork for anyone interested in the field or looking to get started by introducing and exploring the fundamental concepts behind data science and the data industry.
Sundays 17:30-20:30
Hybrid format
Introductory course

What will we provide

Overview of the world of Data Science
Learning through real use-cases and jupyter notebooks
In-depth analysis of core data science algorithms
Understanding the DS Industry: roles, goals, data lifecycle
Current developments in AI - generative models & chatGPT

Course Lecturer

Shaul Solomon
Lead Data Scientist at DockTech
course plan


Week 1
June 9th, 17:30-20:30
Background and motivation
What is Data Science? What can it accomplish? Overview of DS domain and paths to approach it
Week 2
June 14 / 16:00-19:00
Tel Aviv University campus
Probability and statistics
Bayesian priors, distributions, descriptive statistics, and EDA
Week 3
June 14 / 16:00-19:00
Tel Aviv University campus
DS toolbox part I
What is Data Science? What can it accomplish? Overview of DS domain and paths to approach it
Week 4
June 14 / 16:00-19:00
Tel Aviv University campus
DS toolbox part II
What is Data Science? What can it accomplish? Overview of DS domain and paths to approach it
Week 5
June 14 / 16:00-19:00
Tel Aviv University campus
DS toolbox part III
What is Data Science? What can it accomplish? Overview of DS domain and paths to approach it
Week 6
June 14 / 16:00-19:00
Tel Aviv University campus
Summary+Data Science Industry
What is Data Science? What can it accomplish? Overview of DS domain and paths to approach it
Registration is open now
Course starts June 9th 2024

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.
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.
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
popular questions


Still have any questions?
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