Data Science

• Work fluently in Python for data analysis
• Apply statistical inference and experiment design correctly
• Build, tune and evaluate supervised and unsupervised models
• Communicate model results and their limits honestly
• Ship a full data science project end to end

Curriculum

1. 01

   Python for data science

   • Python fundamentals
   • NumPy and pandas
   • Data wrangling
   • Reproducible notebooks
2. 02

   Statistics and inference

   • Probability
   • Hypothesis testing
   • Regression
   • Experiment design
3. 03

   Machine learning

   • Supervised learning
   • Model evaluation and validation
   • Feature engineering
   • Unsupervised learning
4. 04

   Applied projects

   • Time series
   • NLP basics
   • Model communication
   • Capstone project

Projects you will build

• Credit risk model with full evaluation write-up
• Demand forecasting for a retail dataset
• Customer segmentation study