Data Science Fundamentals

• Python Programming for Data Science (Pandas, Numpy, Sklearn)
• Basic Statistics for Data Science and Hypothesis Testing
• Advanced Regression Techniques
• Advanced Data Manipulation and Feature Engineering
• Basics of Machine Learning
• Advanced Data Visualization with Python (Dash-Plotly)

Target Group
Experienced: Python Developers, R programmers

Basic Python including:

• Basic logic: If/else, try/except, for/while
• Data types: list, int, float, string, dict, set, etc…
• Object Oriented Programming: Functions and Classes

• Basic Familiarity with Unix-based command-line
• Basic Familiarity with Version-Control like Git (NOT Mandatory)

• API basics (NOT Mandatory)
• REST (post, get, delete, etc…)

• Database Basics (NOT Mandatory)
• Basic SQL queries
• Data Modeling / ORM

Week 1:

• Introduction to Git
• Object-Oriented Python — Revisited
• Basic Statistics for Data Science
• Introduction to Hypothesis Tests
• Introduction to Regression

Week 2:

• Regression: revisited
• Introduction to Non-Linear Regression
• Time-Series Regression
• Forecasting
• Confidence Intervals

Week 3:

• Data Manipulation and advanced Pandas functions
• Introduction to Data Visualization (plotly / dash)
• Introduction to Machine Learning
• Examples of Projects
• Feature Engineering

Week 4:

• Machine Learning Implementation
• Forecasting with Non-Linear Regression Implementation
• Advanced Data visualization
• Students finish the month with a fully functioning Dash Web-application to visualize the results of their project!

Result: Start-To-Finish Web-application for Data Science Applications