The lectures are broken into 14 units, as shown below. These pages are also reachable from the calendar.

• Unit 1: Welcome back (Classes 1–3)
  Course overview, Data science pipeline, Python review
• Unit 2: Command line (Classes 4–7)
  Files and directories, bash commands, Piping and redirection
• Unit 3: Statistical data types (Class 8)
  Categorical, ordinal, continuous, discrete
• Unit 4: Regular expressions (Classes 9–12)
  Regex syntax, Python re, Command-line tools
• Unit 5: Error handling (Classes 13–15)
  try/except, return codes in bash
• Unit 6: Data cleaning (Classes 16–18)
  Missing data, Outliers, Preprocessing
• Unit 7: Info Challenge (Classes 19–20)
  
• Unit 8: Hardware and OS (Classes 21–23)
  CPU, Memory hierarchy, Filesystems, Role of the operating system
• Unit 9: Concurrency (Classes 24–27)
  Multithreading, Python GIL, Multiprocessing, pickle, shell job control
• Unit 10: Data Ethics (Classes 28–29)
  Principles, Case studies
• Unit 11: OOP in Python (Classes 30–33)
  Operator overloading, Inheritance, Naming conventions, Generators
• Unit 12: Typing (Classes 34–35)
  Type hints, Linters, Static vs run-time checks
• Unit 13: Machine learning with sklearn (Classes 36–38)
  Reading documentation, Classification, Regression
• Unit 14: Versions and packaging (Classes 39–40)
  git, pip, conda