BAN436 Introduction to Python

• Topics

  Python has in recent years become one of the most popular programming languages, and it has found many applications in both business and scientific research. Unlike many other programming languages used in scientific research, Python is not developed specifically for statistical analysis. Instead, it is a general-purpose programming language.

  This one-week intensive seminar will focus on getting you started with using Python for data analysis. Data analysis is an important task for both businesses and researchers. However, the data that we need to analyze is often organized in a way that is unsuitable for analysis. The course will focus on how to use Python to convert raw data into tidy data sets that we can use for data analysis. You will also learn how to use Python to summarize and communicate the information in tidy data sets through basic data analysis and visualization.

  The course will start with a general introduction to Python. The rest of the course will focus on how to use Python for cleaning and analyzing data. The course is intended for students without any prior knowledge of Python, and for students with some prior knowledge of Python and who wish to learn how to use Python for data analysis.

  The course consists of three modules:

  1. Getting started - introduction to Python and Jupyter Notebook
  2. Importing and cleaning data
  3. Analyzing and visualizing data

  After successful completion of the course, you will be able to perform data analysis in Python. The course will also give you the foundation that you need for continuing to learn Python and how you can use it to solve a large variety of problems encountered in your academic and professional life.

• Learning outcome

  During the course, students will learn how to do basic programming in Python, and how to tidy and analyze data in Python. After successful completion of the course, students will have:

  the practical skills to:

  • write, modify and execute Python code in Jupyter Notebook.
  • distinguish between the different data types and structures in Python (e.g. list, dictionary, array, data frame).
  • create functions and loops.
  • load, manipulate and save data.
  • perform simple data analysis (e.g. descriptive statistics, correlation analysis).
  • visualize data.
  • perform simple web scraping.
  • search package documentation and online sources for help with coding.

  and the general knowledge to

  • identify the appropriate format of data sets with regards to data analysis (i.e. tidy data).
  • conduct reproducible research in Jupyter Notebook.

• Teaching

  This is a one-week intensive course that consists of daily lectures and in-class assignments to be solved in groups.

• Recommended prerequisites

  The course introduces the students to Python, and therefore it requires no previous knowledge of Python or programming.

  However, basic statistical knowledge as provided by MET2 is helpful.

• Credit reduction due to overlap

  BAN436 may be taken separately, or as the first part of "BAN438 Application Development in Python".

  Please note that because BAN436 is identical with the first part of BAN438, there is a full credit reduction between the two courses. If you have already passed BAN436 and wish to take BAN438 at a later point, you will be awarded with a total of 7.5 ECTS for the two courses combined.

• Compulsory Activity

  Approved homework assignments.

• Assessment

  Assigned, group term paper (2-3 students in each group) in which the group will demonstrate the tools and concepts learned in the course.

  The group assignment will be given at the end of the course, and the students will have two weeks (weeks 3 and 4) to complete the assignment.

• Grading Scale

  Pass-Fail

• Computer tools

  Python, Jupyter Notebook. I recommend downloading the Anaconda distribution for Python. More details regarding the required software will be provided at the beginning of the course.

• Literature

  To be announced in Canvas.