GRA 6839 Data Analysis in Python
GRA 6839 Data Analysis in Python
(Max 25 students each semester)
Python programming is used extensively in many companies for web scrapping, data analysis, machine learning etc. The language is designed to be easy to learn and work with.
This course provides the tools to use Python programming language to extract knowledge from data. We will start with an introduction of basic concepts in programming. You will learn how to write short codes, read other people’s codes, automate tasks etc. Then, we will use the Pandas package to work with data collection, statistical and graphical analysis. We will learn the power of Dataframes, which allows you to work productively with data. You will learn techniques for loading, cleaning, combining, slicing, and transforming data. Finally, you will be able to combine your data to statistical models and present the results in tables and graphs.
- Basic programming in Python.
- Data analysis and visualization techniques.
- How to implement simple econometrics analysis.
- Skills to read, implement and create new codes in Python.
- Techniques to prepare, transform and analyse data.
- Appreciation for details in the process of data analysis using advanced programming techniques.
- Critical reflection and thinking about translating analysis into programming codes.
The course covers the following topics
- Introduction to programming.
- Techniques in scientific programming.
- Basic data analysis and visualization
- Applications to econometrics and forecasting
- Applications economics and finance
All software is open source and therefore free. We will use Jupyter and Python.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class that is not included on It's learning or text book.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Knowledge of basic calculus and statistics
| Assessments |
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Exam category: Submission Form of assessment: Written submission Weight: 100 Grouping: Group/Individual (1 - 3) Duration: 72 Hour(s) Comment: Computational project Exam code: GRA68391 Grading scale: ECTS Resit: Examination when next scheduled course |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 3 ECTS credit corresponds to a workload of at least 80 hours.