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Your 36-hour intro
to Data Science
by Y-DATA

Next batch applications will be open in March 2021
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Scope:

36 hours

Schedule:

9 meetings, 4 hours each

Format:

online (Zoom)

Course start:

16.11.2020

Teaching language:

Hebrew+English

Сoding language:

Python

Pre-requisites:

Basic coding ability

Tuition fee:

1450 NIS + VAT

About

Data is quickly becoming our world’s most valuable commodity. As its importance grows, it’s never been more important to explore, understand and communicate data concepts as part of one’s daily work. 

Many companies have the facilities to gather and analyse data and utilise it for making business-related decisions. However, there are multiple obstacles for incorporating Data Science tools and using Data Science to its full potential – mostly due to existing gaps in knowledge and understanding.

This unique course offers the tools needed to use data to its full potential – Data exploration, manipulation, visualisation, modelling, prediction and classification. The course aims to provide the knowledge and tools required to analyse data and extract insights immediately upon completion, including: formulating goals and planning approach, required resources and independent ability to use relevant Python libraries. The course emphasises hands-on practice and puts practical exercises at the core of the learning.

We offer a unique opportunity for tech professionals to gain valuable skills which can be incorporated into your professional life immediately, and can provide a foundation for future study and professional growth. 

Goals

Provide an opening to the worlds of Data Science and Artificial Intelligence

Overview of the capabilities of Deep Learning, including computer vision exercise

Practical experience using Python, with emphasis on data analysis

Understanding Machine Learning core tasks: classification, regression, clustering

Target Audience

Tech professionals with diverse experience

Academic STEM or engineering background

Preexisting knowledge of programming

Format

Live lectures with presentations

Work on pre-written Python Notebooks

Practical tutorials including hands-on coding

Team

Gleb Ivashkevich
Data Scientist and ML expert
Liad Yosef
Architect and Team Lead at Duda
Guy Shtar
Ph.D. candidate at Ben-Gurion University
Nathaniel (Nati) Shimoni
Deep Learning practitioner and co-founder at DataDudes

Syllabus

Module 01

Quick intro to AI: History, current state and perspectives
Theoretical | Duration – 2h

Environment setup: Jupyter notebook and Python packages
Hands-on | Duration – 1h

Python ecosystem for data analysis. Tabular data collection preprocessing: Intro to pandas
Theory and Hands-on | Duration – 3h

Practice session: Analysis of house prices dataset
Practice | Duration – 2h

Module 02

ML fundamentals – Part I: basic concepts and use cases
Theoretical | Duration – 2h

Statistics and probability refresher and run-through
Theoretical | Duration – 1h

Correlation, regression, data visualization (Part I)
Theory and Hands-on | Duration – 2h

Practice session: exploratory data analysis of house prices dataset and basic regression model
Hands-on | Duration – 2h

Module 03

ML fundamentals – Part II: model evaluation, metrics and validation; confusion matrix; bias and variance; over- and under-fitting, use case examples
Theoretical | Duration – 2h

Classification, classical supervised learning methods and their uses
Hands-on | Duration – 2h

Data visualization (Part II)
Hands-on | Duration – 1h

Practice session: Classification
Practice | Duration – 1h

Module 04

ML fundamentals – Part III: Unsupervised learning methods and challenges, use case examples
Theoretical | Duration – 1h

Clustering methods and uses
Hands-on | Duration – 2h

Practice session: clustering
Hands-on | Duration – 1h

Dimensionality reduction methods and uses
Hands-on
Duration – 1h

Practice session: dimensionality reduction
Hands-on | Duration – 2h

Module 05

ML State of the Art capabilities; Deep learning revolution
Theoretical | Duration – 1h

Introduction to PyTorch
Hands-on | Duration – 1h

Practice session: deep learning with PyTorch – fashion MNIST dataset
Hands-on | Duration – 2h