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. In most data science projects, we must extract raw data and convert it to a format that is suitable for analysis. In this course, you will learn how to use Python to wrangle raw data into tidy data sets, and how to perform basic data analysis in Python with summary statistics 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 but 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 simple data analytics in Python. The course will also give you the foundation that you need for continuing to learn how to use Python to solve other tasks encountered in your academic and professional life.

• Learning outcome

  In this course, students will learn how to do basic programming in Python, and how to tidy and analyze data in Python.

  Knowledge

  After successful completion of the course, students

  • understand the importance of documentation when coding.
  • understand how Python can be used in businesses and scientific research.

  Skills

  After successful completion of the course, students can

  • write, modify and execute Python code.
  • 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.

  General competence

  After successful completion of the course, students can

  • identify the appropriate format of data sets with regards to data analysis (i.e. tidy data).
  • conduct reproducible research in Jupyter Notebook.
  • use package documentation and online sources for help with coding.

• Teaching

  This is a one-week intensive course that consists of daily lectures with small assignments to be solved in-class. Participation in the lectures is not mandatory, but highly recommended.

• 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 term paper in which the students will demonstrate the tools and concepts learned in the course. The term paper can be submitted either individually or in groups of 2-3 students.

  The term paper is 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.