This course will give a conceptual introduction, implementation, and interpretation of the data scientist’s toolbox in practice. There are three components to this course. The first is a practical introduction to the tools that will be used in the project like Python, Colab, Jupyter notebook, markdown. The second is a conceptual introduction to A/B test. The third is about Case Studies of A/B Test based on the Toolbox.

👌 What you’ll learn:

  • Understand principles behind statistical inference and A/B test;
  • Familiar with Data Science Toolbox: Python (numpy, pandas, seaborn, sklearn), Colab; Jupyter notebook, Markdown;
  • Analyze continuous and categorical data using statistics, Python programming based on Colab and software as appropriate;
  • Ability in using advanced Python tools to describe, summarize, and visualize dataset;
  • Understand and implement good coding practices, including statistical inference on A/B test, and statistical learning/prediction based on tabular data.

🏗️ Prerequisites:

  • Calculus & Linear algebra: inner product, matrix-vector product.
  • Basic Statistics: STAT1011 level statistics, basics of distributions, probabilities, conditional probability, mean, standard deviation, etc.
  • Python: basic grammar; numpy, pandas
  • ⏲️ Lectures: Wed 11:30AM - 2:15PM
  • 🎒 Lecture/Recitation Location: Y.C. Liang Hall 103
  • 💻 HW submission: BlackBoard
  • ⌨️ colab: notebook or click Open in Colab

Homepage Open In Colab

All students welcome: we are happy to have audiences in our lecture.

📋 Reference Textbooks

The following textbooks are useful, but none are exactly same with our course.