Practical data analytics, statistical modelling, visualisation and applied project work with Python and real-world datasets.
- Institution
- Metropolia University of Applied Sciences
- Location
- Helsinki, Finland
- Last taught
- Spring 2025
- Extent
- 3 ECTS
- Course code
- TX00FV84-3006
- Teaching language
- English
- Prerequisite
- Basic Python knowledge recommended but not mandatory
Course overview
In the Data Analytics & Statistics in Python course, students developed practical skills in data analytics, statistical modelling and data visualisation using Python. They worked with real-world datasets and learned to apply data-driven decision-making techniques.
The course covered descriptive and inferential statistics and hands-on programming with key Python libraries including Pandas, NumPy, Matplotlib and Seaborn. Students applied analytics techniques to datasets from finance, healthcare, environmental science and social-media domains. By the end of the course, students could manipulate data, conduct and interpret statistical analyses, create visualisations for data storytelling, apply introductory machine-learning and predictive-analytics methods, and complete a final applied project.
Course texts and materials
Instructor materials: Students had access to lecture slides, video files and hands-on coding notebooks through Moodle.
Recommended reading list
- VanderPlas, J. (2016). Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media. Open resource.
- Bruce, A., Bruce, P., & Gedeck, P. (2020). Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python. O’Reilly Media.
- McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter. O’Reilly Media.
- Severance, C. (2016). Python for Everybody: Exploring Data Using Python 3. Open resource.
- Vohra, M., & Patil, B. (2021). A Walk Through the World of Data Analytics. IGI Global.
Additional learning materials
Materials and participation requirements
Students required access to a laptop or PC with an internet connection and software such as Jupyter Notebook and Python through Anaconda, or equivalent tools for coding exercises, data analysis and project work.
Participants were expected to take part actively in group discussions, complete weekly hands-on exercises and successfully finish a final project, which they presented at the end of the course.
Course assessment
| Assessment component | Weight |
|---|---|
| Hands-on exercises | 30% |
| Final project | 70% |
Grading scale
| Score | Grade |
|---|---|
| 90-100 | 5 |
| 80-89 | 4 |
| 70-79 | 3 |
| 60-69 | 2 |
| 41-59 | 1 |
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