The lectures are broken into 11 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: Regular expressions (Classes 8–11)
  Regex syntax, Python re, Command-line tools
• Unit 4: Error handling (Classes 12–14)
  try/except, return codes, bash if statement
• Unit 5: Versions and packaging (Classes 15–16)
  git, github, pip, mamba
• Unit 6: Data cleaning (Classes 17–18)
  Missing data, Outliers, Preprocessing, Merging dataframes
• Unit 7: Hardware and OS (Classes 19–21)
  CPU, Memory hierarchy, Filesystems, Compiled vs Interpreted code, Role of the operating system
• Unit 8: Concurrency (Classes 22–26)
  Multithreading, Python GIL, Multiprocessing, bash job control
• Unit 9: OOP in Python (Classes 27–30)
  Operator overloading, Inheritance
• Unit 10: Data Ethics (Classes 31–33)
  Principles, Case studies
• Unit 11: Machine learning with sklearn (Classes 34–37)
  Statistical data types, Reading documentation, Classification, Regression, Clustering