The lectures are broken into 10 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: OOP in Python (Classes 4–9)
  Operator overloading, Inheritance
• Unit 3: Command line (Classes 10–13)
  Files and directories, bash commands, Piping and redirection
• Unit 4: Regular expressions (Classes 14–16)
  Regex syntax, Python re, Command-line tools
• Unit 5: Error handling (Classes 17–20)
  try/except, return codes, bash if statement
• Unit 6: Data cleaning (Classes 21–22)
  Missing data, Outliers, Preprocessing, Merging dataframes
• Unit 7: Hardware and OS (Classes 23–25)
  CPU, Memory hierarchy, Filesystems, Compiled vs Interpreted code, Role of the operating system
• Unit 8: Concurrency (Classes 26–30)
  Multithreading, Python GIL, Multiprocessing, bash job control
• Unit 9: Versions and packaging (Classes 31–34)
  git, github, pip, pixi
• Unit 10: Data Ethics (Classes 35–37)
  Principles, Case studies