Data Analytics

1. Data Analytics

• Basic and composite built-in data types; Introduction to series

• DataFrames. Indexing and slicing; Missing values

• String processing and regex; Groupby and aggregation

• Joins & concat/unions

• Visualization

• Types of Plots; Intro to Matplotlib and Seaborn

• Customize your plots

2. Career Workshops

• How to start a career in tech

• How to polish your Linkedin profile

• How to write a great CV

• How to master the Interview process.

3. Job Readiness: Soft skills through career trainings geared towards preparing you for job applications & interviews, networking events and job fairs.

4. You will learn from experts in the industry to start a career in tech.

you are able to understand and speak English

you have experience in self-study

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

you have a good understanding of Python

you registered on dataquest.io and finished the following modules:

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

• Python Intermediate: Cleaning and Preparing Data in Python

• Introduction to Pandas and NumPy (first 2 modules)

• Elements of the Command Line (first module only)

Note: Graduates from our Introduction to Python will be preferred!

Build a Data Science Pipeline with Python; Know about Data Science Tools (pandas, sklearn, ...)

Get the Data Science mindset; Tell a Data Science Story

• Career & Soft Skills Workshops, Company Visits

• ReDI Mentorship Program (mentors in the IT industry)

• ReDI Talent Pool (job listing platform)

• SkillBuild self-paced eLearning (by IBM & ReDI)

• You should expect to spend a minimum of 8 hours for career workshops or trainings during the semester.