MODULE 1:
Introduction to Programming
Learn the foundations of programming and how to code in Python
Activity: Write a simple application in Python
MODULE 2:
Programming for Data Science
Understand the data analysis pipeline, review the data science foundations using probability, statistics & basic data analysis, and learn classic data analysis methods such as regression and classification
Activity: Create a predictive model with a given dataset
MODULE 3:
Infrastructure and SQL
Learn how to use Linux/Bash and Docker, review SQL and relational databases
Activity: Create and query a relational database on Linux using Docker
MODULE 4:
Statistics
Review basic statistics concepts, such as probabilities, central tendency measures, and charts and graphs. Basic statistics lays the foundation for ML and Advanced Data Analysis.
Activity: Use statistical methods to analyze datasets using Jupyter Notebooks.
MODULE 5:
Machine Learning
Learn the differences between supervised and unsupervised machine learning methods, and the different families of algorithms within each group (e.g.: regression, classification, clustering).
Activity: Create a predictive model for a given labeled dataset.
MODULE 6:
Advanced-Data Science
Learn more advanced methods which deal with data types that are more complex than tables of numbers (e.g.: text, geospatial data, time-series, AB testing). In this module, we’ll cover specific methods that apply to these types of data, as well as how to pre-process and visualize them.
Activity: Use ADA methods to analyze complex datasets.
MODULE 7:
Career Preparation
Prepare for job interviews through logical puzzles, data challenges, and practice sessions. Receive career coaching & support.