Data Analysis and Visualization in Python

• 26 hrs lectures
• 13 hrs projects

• 100 pts projects

The aim of the course is to acquaint students with the issue of data acquisition, processing and analysis in Python. Within the course, Python will be introduced as a tool for efficient data manipulation.
Students will gain a general overview of basic and advanced methods of data analysis and basic and advanced aspects of Python, which they will learn to use with modern mathematical libraries and libraries for advanced data analysis and modeling. They will understand how the techniques implemented in these libraries work in general and learn what technique is appropriate for what data.
In addition to general knowledge of basic data processing techniques, the student will gain an overview of the effective execution of critical parts of the program, extension of the language with its own modules written in C / C ++ or the problematic of installing libraries in an isolated environment or containers.
At the end of the course students should understand how to effectively obtain, analyze and visualize data of various extent. The knowledge can then be used to solve non-trivial engineering and scientific tasks or to evaluate data for management and decision-making purposes.

• Principles of Programming Languages (IPP)
• Introduction to Programming Systems (IZP)
• Linear Algebra (ILG)
• Probability and Statistics (IPT)

Basic knowledge of imperative programming and algorithmization, knowledge of basic concepts and operations from linear algebra (vectors, work with matrices, linear operations, etc.) and statistics.

• Mark Pilgrim: Ponořme se do Pythonu 3 (ISBN: 978-80-904248-2-1, dostupné online)
• Jake VanderPlas: Python Data Science Handbook (ISBN: 978-1-491-91205-8, dostupné v online zdrojích knihovny)
• Samir Madhavan: Mastering Python for Data Science (ISBN: 978-17-843901-5-0)
• Robert Johansson: Numerical Python (2019, ISBN: 978-1-4842-4245-2)

1. Introduction to Language I
2. Introduction to Language II
3. Data acquisition and data persistence
4. Effective implementation of operations over n-dimensional fields
5. Tools for advanced data manipulation
6. Basic approaches to data visualization
7. Basic methods of data and data dependency analysis
8. Advanced approaches to data visualization
9. Advanced methods of data and data dependency analysis
10. Work with image data and possibilities of data presentation
11. Advanced operations over time series
12. Symbolic domain calculations
13. Code acceleration capabilities for HPC needs

The aim of the project is to create a script that obtains data from publicly available sources, analyzes and presentes it in the form of a report. Project evaluation will take into account the quality of the code, the resulting analysis and the generated reports.

The evaluation is based on individual implementation of a project whose implementation consists of three parts (data acquisition, data preprocessing and analysis, report generation). Each part will be evaluated separately. Students will receive feedback on their work, which they will incorporate into the final solution. The first two parts are submitted during the semester and can be awarded up to 20 points each. Up to 60 points can be earned for the final solution.