Data Analysis and Visualization in Python
FIT-IZVAcad. year: 2020/2021
The aim of the course is to acquaint students with the problems of data acquisition, processing, analysis and visualization using the cross-platform scripting language Python. It has a sophisticated ecosystem offering a rich spectrum of extension libraries, either in the form of native code or in terms of performance of efficient extensions implemented in C / C ++.
During the lectures students will learn Python constructs, methods of data acquisition, storage and manipulation, possibilities of advanced computations in numerical and symbolic level and visualization of acquired data. In this course, students will also gain an overview of the properties of techniques for advanced analysis of data dependencies and their applications for various data. Finally, Python will be expanded to include custom designs and techniques to effectively overcome the disadvantages of the interpreted language for performance-oriented applications. In the practical part (project), students will go through all stages of large data processing - from the design stage, through processing to subsequent analysis and visualization.
Learning outcomes of the course unit
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.
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.
- IZP - Introduction to Programming Systems
- ILG - Linear algebra
- IPP - Principles of Programming Languages
- IPT - Probability and Statistics
Recommended optional programme components
Recommended or required reading
Mark Pilgrim: Ponořme se do Pythonu 3 (ISBN: 978-80-904248-2-1, online resource, in Czech)
Jake VanderPlas: Python Data Science Handbook (ISBN: 978-1-491-91205-8, available in online library resources)
Samir Madhavan: Mastering Python for Data Science (ISBN: 978-17-843901-5-0)
Robert Johansson: Numerical Python (2019, ISBN: 978-1-4842-4245-2)
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
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.
Minimum of 50 points earned. At least 2 points from each part of the project.
Language of instruction
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.
Specification of controlled education, way of implementation and compensation for absences
Individual project. 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.
Type of course unit
26 hours, optionally
Teacher / Lecturer
- Introduction to Language I
- Introduction to Language II
- Data acquisition and data persistence
- Effective implementation of operations over n-dimensional fields
- Tools for advanced data manipulation
- Basic approaches to data visualization
- Basic methods of data and data dependency analysis
- Advanced approaches to data visualization
- Advanced methods of data and data dependency analysis
- Work with image data and possibilities of data presentation
- Advanced operations over time series
- Symbolic domain calculations
- Code acceleration capabilities for HPC needs
13 hours, compulsory
Teacher / Lecturer