Intro to DS
starting on:
April 13, 2023
6 week intro to the world of data science.
Registration is open until April 9.
Book your Place
Intro to DS
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 in recent months.
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.
What is it about?
Overview of the world of Data Science
Understanding the DS Industry: roles, goals, data lifecycle
In-depth analysis of core data science algorithms
Learning through real use-cases and jupyter notebooks
Current developments in AI – generative models and ChatGPT
Target Audience
Who is this for?
Data Curious
If you keep seeing Machine Learning and Deep Learning pop up everywhere and want to know what the hype is all about.
Getting Started
If you are thinking about studying Data Science but want to get a general understanding of the field before committing to a full-scale study program.
Course Structure
18 hours
Next class starts
6 meetings * 3 hours
Live lectures in hybrid format
Shaul Solomon
Shaul Solomon has a B.Sc. in Computer Science and pivoted into Data Science. He has spent the past few years as Lead Data Scientist at DockTech, a maritime-based Israeli Startup, focusing on the processing of sensor data and spatio-temporal feature engineering.
Dr. Adir Solomon
Adir has recently completed his Ph.D. in the Department of Software and Information Systems Engineering at Ben-Gurion, focusing in his research on the fields of machine learning, deep learning, data mining, and NLP, as well as their applications in the areas of user modeling, computational criminology, and recommender systems.
DS toolbox part I
DS toolbox part II
DS toolbox part III
Classic ML. Fundamentals of Supervised Learning; Anatomy of a model; Use-case: Linear Regression.
Common Tasks and Tools. Supervised vs. Unsupervised learning; Classification, Clustering and Ensemble models; Use-case: Decision Trees.
Deep Learning and beyond. The Deep Learning revolution; understanding neural nets; Use-case: CNN.
Summary + Data Sceince Industry
Current advancement and ML capabilities; DS in production; Data Roles and ways to get started.
APRIL 20 17:30-20:30
Basic tools & concepts
Workings and life-cycle of DS project in the industry; EDA - How to approach data and understand it.
APRIL 27 17:30-20:30
MAY 4 17:30-20:30
MAY 11 17:30-20:30
MAY 18 17:30-20:30
APRIL 13 17:30-20:30
Background and motivation
What is Data Science? What can it accomplish? Overview of DS domain and paths to approach it.
Download Full Syllabus
How it was
How it was
How it was
How it was
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