⭐ EFIKO Original · Free micro-certificate
Python for Research
Practical programming and data analysis skills for academic and applied research
Start this free courseThis course helps you move from knowing basic Python to using it confidently in real research. Over six focused sessions, you will learn to organise research code, clean and analyse data with Pandas, run statistical tests, create clear visualisations, and produce reproducible, shareable work. Every session is designed to be learnable on a phone, works well offline, and uses practical, low-data examples drawn from African research contexts. By the end, you will have a repeatable workflow for taking raw data all the way to publication-ready results.
What you'll learn
- Organise a reproducible research project structure using virtual environments and clear file layouts
- Clean, transform, and summarise real datasets using Pandas and NumPy
- Apply appropriate descriptive and inferential statistics to answer research questions
- Create clear, publication-ready visualisations with Matplotlib and Seaborn
- Document and share research code so that others can reproduce your results
- Interpret and communicate analytical results accurately and ethically
Course sessions
- Setting Up a Reproducible Research Environment. Set up research projects with an isolated virtual environment, a frozen requirements.txt, and clear data/code/output folders so your work i…
- Working with Research Data in Pandas. The Pandas DataFrame lets you load, inspect, select, and filter research data in a few clear lines: load with read_csv/read_excel, inspect…
- Cleaning and Transforming Data. Cleaning and transforming data with pandas - handling missing values, duplicates, and inconsistencies, then reshaping and grouping - turns…
- Descriptive and Inferential Statistics. Descriptive statistics summarise your sample with mean, spread, and quartiles, while inferential tests in SciPy let you generalise responsi…
- Visualising Research Findings. Choose the chart that fits your question, label and title it clearly with Matplotlib and Seaborn, and export at dpi=300 to produce honest,…
- Reproducibility, Sharing, and Research Ethics. Reproducible research combines clear documentation, dependency pinning with Git-based sharing, and ethical handling of data and honest repo…
Sample lesson: Setting Up a Reproducible Research Environment
Reproducibility means anyone (including future you) can rerun your analysis and get the same results. This depends on knowing exactly which packages and versions you used. A virtual environment is an isolated Python workspace that keeps one project's packages separate from others, so upgrading a library for one study…
Competencies you'll gain
- Building reproducible Python research project workflows
- Cleaning, transforming, and analysing datasets with Pandas and NumPy
- Selecting and applying appropriate statistical tests and interpreting results
- Producing clear, publication-ready data visualisations
- Documenting and sharing research code ethically and reproducibly
Frequently asked questions
Is the Python for Research course free?
Yes. Python for Research is a free EFIKO Original micro-certificate course — you can learn it at no cost and earn a verifiable certificate on completion.
Do I get a certificate for Python for Research?
Yes. Pass the final assessment and you earn a verifiable EFIKO certificate with a QR code and public verification link, listing the competencies you achieved.
How long does Python for Research take?
About 6 hours across 6 short sessions, each with a lesson, quiz and flashcards. You can learn at your own pace, even offline.
Who is Python for Research for?
It is designed for university students, postgraduate researchers, and early-career professionals who already know basic python and want to apply it to research workflows, at a intermediate level. No prior experience is assumed.