A low-threshold introduction to quantitative social-science data analysis with Python, Jupyter Notebook and reproducible research workflows.
- Institution
- University of Turku
- Official study-guide title
- SOST0091 Data Analytics with Python for Social Sciences I
- Extent
- 5 ECTS
- Level
- Advanced studies / Master’s level
- Course period
- 3 November-18 December 2026
- Assessment
- Pass/Fail
- Teaching language
- English
- Previous Python experience
- Not required
Course overview
This five-credit University of Turku course provides a low-threshold introduction to quantitative data analysis for social science students. The course begins with research questions, concepts, variables and measurement, and then develops practical skills in data inspection, cleaning, transformation, descriptive statistics, visualisation and statistical testing.
Students learn to connect theory, data and methods and to document their analyses so that another person can understand and reproduce the workflow. Excel is used as a bridge to Python, while Jupyter Notebook is used for transparent, step-by-step analysis.
Learning outcomes
After completing the course, students should be able to:
- understand the basic logic of quantitative social research;
- connect theories and concepts with variables and measurement;
- clean, organise and analyse social science data;
- create clear tables, descriptive statistics and visualisations;
- conduct and interpret t-tests, chi-square tests and analysis of variance;
- evaluate assumptions, limitations and possible sources of bias; and
- produce transparent and reproducible analyses in Jupyter Notebook.
Course content and session sequence
| Session | Topic |
|---|---|
| 1 | From Social Science Questions to Data |
| 2 | From Research Questions to Analysable Data |
| 3 | Inspecting and Summarising Social Science Data: Excel I |
| 4 | Excel II: Group Comparison, Visualisation, Lookup and Regression Preview |
| 5 | Computer Lab I: Opening, Inspecting and Cleaning ESS Survey Data |
| 6 | Python I: Cleaning and Reshaping WDI Macro Data |
| 7 | Python II: Missing Data and Analysis-Ready GSS Records |
| 8 | Computer Lab II: Reproducibility, Debugging and Gapminder |
| 9 | Descriptive Statistics and Exploratory Data Analysis |
| 10 | Python Visualisation: Honest, Accessible Social Science Charts |
| 11 | Computer Lab III: A Mini Visual EDA Report |
| 12 | Probability and Variability |
| 13 | Hypothesis Testing with Descriptives First |
| 14 | Full Analysis Pipeline: From Raw Table to Mini-Project Draft |
| 15 | Statistical-Learning Bridge |
| 16 | Review and Consolidation: Repair the Workflow |
Course texts and materials
Students have access to instructor-prepared lecture slides, recorded teaching materials where used, Jupyter Notebook demonstrations, datasets, Moodle activities, H5P exercises, Kahoot quizzes, computer-laboratory exercises, weekly guidance, assignments, assessment criteria and final mini-project instructions. Students are not expected to purchase additional course materials.
Datasets and software
- European Social Survey, General Social Survey, World Development Indicators, World Inequality Database, Humanitarian Data Exchange, Gapminder, World Happiness Report, Freedom House, Statistics Finland and selected teaching datasets;
- Python, pandas, NumPy, Matplotlib, Jupyter Notebook, Visual Studio Code and Excel.
Teaching and learning methods
Learning takes place through lectures, computer laboratories, live demonstrations, weekly exercises, practical data-analysis tasks, guided debugging, a data-analysis report and a final mini-project. Students may use their own master’s thesis data when it is suitable for the course.
Assessment
The course is graded Pass/Fail. Assessment emphasises correct analysis, careful interpretation, transparent documentation and reproducibility.
| Assessment component | Weight |
|---|---|
| Weekly hands-on exercises | 30% |
| Data-analysis report | 40% |
| Final mini-project | 30% |
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