Data Analysis and Visualization in Python
About course
The aim of the training is to provide participants with a comprehensive introduction to the Python analytical environment and to develop practical skills necessary for independent work with data. The course begins with an overview of Python’s basic syntax, data structures, and programming best practices, forming a foundation for further learning.
In the following stages, participants will learn methods for efficient data processing and analysis—from data loading, cleaning, and transformation, to exploration and interpretation of results. Particular emphasis is placed on professional data visualization, enabling the creation of clear, aesthetically pleasing, and informative charts that support decision-making processes.
An important element of the training is also learning how to create reproducible technical documents using Markdown syntax, which allows for combining code, analysis results, and commentary into cohesive reports. Participants will gain the ability to publish these reports in various formats—such as PDF, HTML, or online documents—making it easier to share results in team and business environments.
As a result of the training, participants will be prepared to independently conduct data analyses—from data acquisition, through processing and visualization, to creating professional reports and presenting results.
The course duration: is 16 hours, including 8 hours delivered as webinars.
Webinar schedule:
Webinar 1 (2h): September 7, 2026 (Monday), 11:00–12:30 AM
Webinar 2 (2h): September 14, 2026 (Monday), 11:00–12:30 AM
Webinar 3 (2h): September 21, 2026 (Monday), 11:00–12:30 AM
Webinar 4 (2h): September 28, 2026 (Monday), 11:00–12:30 AM
Learning outcomes
- Basic knowledge of PythonThe course participant understands the basic elements of Python syntax and is able to use functions and data structures such as lists, dictionaries, tuples, sets, and Pandas DataFrames.
- Understanding of data analysis methodsThe participant understands exploratory data analysis (EDA) techniques, is familiar with fundamental statistical methods, and is able to apply them in practice.
- Principles of data visualizationThe participant understands the fundamental principles of creating clear and visually effective data visualizations in Python.
- Data preparation for analysisThe participant is able to load, clean, and transform data in Python, preparing it for further analysis.
- Creating visualizationsThe participant is able to generate various types of charts (e.g., bar charts, line plots, histograms, heatmaps) using matplotlib, plotly.express, seaborn, and other libraries.
- Performing statistical analysisThe participant is able to apply methods such as descriptive statistics, correlation analysis, and regression analysis.
- Code optimization and analysis of large datasetsThe participant is able to efficiently work with large datasets, optimize code performance, and use tools for data processing.
- Critical evaluation of resultsThe participant is able to critically assess the results of analyses and correctly interpret them in the context of the given problem domain.
- TeamworkThe participant is able to collaborate with others, share analyses and work outcomes, for example in the form of reports or interactive visualizations that are easily interpretable by team members. They are also able to use Git tools and GitHub repositories.
- Independence in problem-solvingThe participant is able to independently analyze data and solve complex analytical problems using tools available in Python.
- Proficiency in Python toolsThe participant acquires advanced skills in working with various Python libraries that support data analysis and visualization.
- Proficiency in GIT toolsThe participant is able to work on a team-based analytical project using Git tools.
Sample career paths
Data Analyst Data Scientist Freelancer
Benefits
Increased competitiveness in the job market Ability to create professional reports Improved data-driven decision-making Automation and time savings Building a project portfolio
Program
In this module, participants will learn the basics of the Python programming language and set up a working environment for data analysis. Tools such as Python, PIP, PyCharm, Visual Studio Code, and Google Colab will be introduced. Participants will learn how to install packages, manage their environment, and run their first scripts and Jupyter Notebook notebooks.
This module focuses on data processing and manipulation using the pandas library. Participants will learn how to load data from various sources, as well as filter, aggregate, and transform tabular data.
In this module, participants will learn the basics of the Git version control system and collaborative work using GitHub. They will learn how to create repositories, manage code versions, work with branches, and collaborate in project teams.
This module introduces data preparation for analysis and modeling. Participants will learn data cleaning techniques, handling missing values, variable transformations, and basic methods of preparing input data.
An extension of preprocessing topics covering more advanced techniques such as encoding categorical variables, data scaling, outlier detection, and preparing datasets for machine learning models.
This module is dedicated to creating data visualizations using libraries such as matplotlib and seaborn. Participants will learn how to build clear and informative charts and how to select appropriate visualization types depending on the data and analytical goals.
In this module, participants will learn methods for exploring data, identifying patterns, and analyzing relationships between variables. They will also learn how to interpret results and draw conclusions based on data.
A continuation of EDA with a focus on more advanced analytical techniques, multivariate analysis, and data preparation for modeling. Participants will work with real datasets and build complete analytical workflows.
Instructors
dr Karol Flisikowski
Karol Flisikowski is a professor at the Gdańsk University of Technology, affiliated with the Department of Statistics and Econometrics at the Faculty of Management and Economics. He teaches statistics and conducts research in the field of social statistics, with his work focusing on socio-economic analyses and the application of statistical and econometric methods.
He graduated in Management as well as Electronics and Telecommunications at the Gdańsk University of Technology. In his earlier career, he worked on predictive models (e.g., corporate bankruptcy prediction) using machine learning methods, as well as digital signal processing. In 2015, he defended his PhD thesis on employment and wage mobility in OECD countries, combining classical statistical methods with Markov models.
Currently, his research focuses on spatial statistics and the analysis of economic mobility in sectoral and spatial terms. He has also worked abroad (Taiwan and China), teaching and conducting research at international universities.
At the Gdańsk University of Technology, he also holds organizational roles: he supervises the Data Science Club, manages postgraduate studies in artificial intelligence and business process automation, coordinates distance learning, and administers the university’s e-learning platform.
mostwiedzy.pl Rules and organization
Recruitment requirements
Error Adults aged 18-64
Rules
Warning The course is delivered on the Gdańsk University of Technology e-learning platform Warning The course can be completed at any time, except for the mandatory online webinars Warning The course completion certificate will be sent electronically
Crediting type
- Warning Online TestKnowledge test after the course.
- Warning Final ProjectProject based on course content.
Accessibility level
- Warning Partially accessibleThe course is partially adapted to the needs of people with special needs; some elements may require additional support.
Supervisor
- mgr Oliwia Bronk
oliwia.bronk@pg.edu.pl
© 2026. Ta praca jest dostępna na licencji CC BY-NC-ND 4.0
Project: "PGEDU+ Platform: Development of Adult Qualifications and Competencies" co-financed by the European Union from the European Social Fund Plus under the program European Funds for Social Development 2021 - 2027