SKL401 Introduction to Python (E)

• Topics

  This intensive seminar is an introduction to Python. The seminar is delivered digitally through short recorded lessons combined with practice problems. The aim is to establish a working level of programming skills so students can use Python confidently as a tool in later courses.

  The content is organized as follows:

  • Getting started and working effectively
    • Installing Python and a suitable working environment for coding on your own computer.
    • Understanding files, folders and paths.
    • Writing scripts and running code.
    • Installing and importing packages.
    • Variables and basic data types.
  • Programming fundamentals
    • Subsetting and simple transformations.
    • Control flow:
      • Conditionals
      • Iterations
    • Writing functions.
  • Data wrangling
    • Importing data and understanding common data formats
    • Cleaning and transforming data.
    • Joining data sets.
    • Creating summaries using grouping and aggregation.
    • Handling missing values and data quality issues.
  • Graphics
    • Reshaping data.
    • Visualizing data.
  • Analysis, reporting, becoming self-sufficient
    • Estimating and handle basic regression models.
    • Exporting tables and figures for use in reports and presentations.
    • Debugging, reading documentation, getting help efficiently, and appropriate use of generative AI as support.

• Learning outcome

  Skills

  On successful completion of the course, the student can:

  • set up and use Python in an appropriate coding environment for structured work
  • handle files and folders efficiently
  • write, run, and modify Python code using core language features and basic programming structures
  • import, clean, reshape, merge, and summarize data
  • create clear descriptive summaries, tables, and figures
  • estimate basic regression models and handle the resulting model objects

  General competence

  On successful completion of the course, the student can:

  • complete a small end-to-end data task in Python from raw data to results suitable for communication
  • work in a structured, reproducible way that makes analyses easy to rerun and check
  • produce readable scripts and output that other can understand and reuse

• Teaching

  The lessons are given as self-paced video lectures. Practice exercises of various levels will be provided.

• Recommended prerequisites

  Basic statistical competence equivalent to MET2.

• Credit reduction due to overlap

  Full point reduction against BAN401, BAN405 and STR467.

• Compulsory Activity

  One individual mandatory assignment.

• Assessment

  90-minute multiple-choice home exam.

• Grading Scale

  Pass-Fail.

• Computer tools

  Python and an IDE such as Positron or VS code.

• Literature

  The documentation of Python and its packages.