(in Polish) Data processing and analysis in Python language

optional courses

The aim of the course is to introduce its participants to programming in Python language, particularly in the context of analysis and visualization of data. Students will be familiarized with tools sufficient for efficient data preparation and processing as well as methods used for economic analysis. Consequently, each of the participants will be prepared for uptaking more advanced programming courses in future. The pace of the course and the scope of material will be adjusted to participants’ needs. A prior knowledge of programming in Python is not necessary to take part in the course.

1. Short introduction to Object-Oriented Programming. Environment installation, getting to know Ipython Notebook (1.5 h)

2. Data structures (strings, lists, tuples, dictionaries, sets, data frames) (1.5 h)

3. Control flow (if-then-else, loops) (1.5 h)

4. Basic operations on data: applying methods to objects (1.5 h)

5. NumPy basics – operations on arrays (1.5 h)

6. Functions (1.5 h)

7. Preparation and basic data processing: importing of datasets, cleaning, saving data (3 h)

8. Processing of ‘cleaned’ datasets with the use of Pandas library (4.5 h)

9. Data visualization (1.5 h)

10. Application of data analysis methods (dependent on participants needs and remaining time) (3h)

11. Detection of errors and finding ways to fix and handle them (successively during the course)

W. McKinney, 2012, Python for Data Analysis, O’Reilly Media

J. Grus, 2015, Data Science from Scratch, O’Reilly Media

Student understands the idea of object-oriented programming.

Student is able to prepare the environment necessary for using Python language.

Student knows the basics of programming in Python language.

Student is able to detect errors and find the way to fix them.

Student is able to import the data and assess their quality.

Student understands problems related with the necessity for cleaning the data of low quality and is able to solve those problems.

Student is able to process the data, depending on his/her needs and the form to which he/she needs to transform them for the purpose of further analysis.

Student knows basic methods of data analysis and visualization.

Student becomes aware of the increase of the effectiveness in working with data, thanks to programming.

1. Mid-term test evaluating knowledge gained in the first part of the course (50%).

2. Preparation of own project based on the course material (50%). The main criterium for evaluating the project is the level of usage of the tools and methods covered during the course. There is a possibility to use tools for publishing the created project (github, nbviewer), which gives the opportunity to prove gained skills in the CV. The concept of the project, depending on student’s needs, is to be consulted with lecturer. If needed, the advices and hints concerning the project’s concept may be provided by the lecturer, based on student’s interest.