Python Intermediate

Virtual Environments, Git

Recap: Data Types, Variables, Conditionals

Recap: Functions, Dicts, Data Manipulation

SQL Introduction

Numerical Calculations with numpy

Data Handling with pandas

Data Preparation (cleaning, ...)

Data Visualisation (matplotlib, seaborn, plotly)

Object Oriented Programming

Networking: Web Scraping, REST APIs (create and consume)

You have an interest in data and what to do with it

You have a good understanding of basic Python programming or completed the ReDI course Python Foundations

You can commit at least 10 hours a week (classes + self-study), 80% attendance of the course is required for graduation.

You can commit at least 8 hours for career workshops or trainings during the semester.

You are able to understand and speak English.

Register on dataquest.io and finish the following modules:

• Python for Data Science: Fundamentals (first 3 modules only)

• Cleaning and Preparing Data in Python

• Pandas and NumPy Fundamentals (first 2 modules only)

You will have built a Data Science Pipeline with Python

You know about Data Science Tools (Pandas, Sklearn,...)

You get the Data Science mindset and can tell a story with data

You are prepared to continue with the Machine Learning course for the Data Analytics Track

Career & Soft Skills Workshops, Company Visits

ReDI Mentorship Program (mentors in the IT industry)

ReDI Talent Pool (job listing platform)

SkillBuild Self-paced learning (by IBM & ReDI School)