Statistical Data Analysis with Python Training Course

This five day, hands-on course builds the skill to analyse data in Python from the ground up, using pandas and the scientific Python ecosystem. It covers the Python environment, data wrangling with pandas, descriptive and inferential statistics, regression, and data visualisation. Participants leave able to analyse data in Python, write clear code, and produce strong charts, with no prior programming required.

Python has become the dominant language of data science and is now widely used for data analysis far beyond it. It is free, general purpose, and supported by a powerful ecosystem of libraries for working with data, of which pandas is the centre. Learning to analyse data in Python opens a door not only to analysis but to automation, machine learning and much more. Its only barrier is that it is code based, and this course is built to get non programmers over it.

It builds Python for data analysis from the ground up. It covers the Python environment and the essentials of the language, data wrangling with pandas, descriptive and inferential statistics, regression for modelling, and data visualisation. No prior programming is assumed; the course teaches the code carefully, building from first lines to a complete analysis. Participants leave able to import and wrangle data with pandas, run and interpret the main analyses, produce clear visualisations, and write reusable code. The course is hands-on, with every participant writing and running Python throughout.

• Navigate the Python environment confidently for data analysis work
• Understand core Python concepts needed for working with data
• Import, inspect and prepare datasets using pandas
• Clean, transform and manage variables for analysis
• Handle missing data, reshape datasets and combine multiple data sources
• Produce descriptive statistics and exploratory summaries in Python
• Run and interpret common inferential techniques such as t-tests, ANOVA, chi-square tests and correlation
• Build and interpret simple linear, multiple linear and introductory logistic regression models
• Produce clear and informative visualisations for exploration and reporting
• Write reusable notebooks or scripts that support reproducible analysis
• Communicate findings accurately in tables, charts and narrative form

This course is designed for professionals who want to use Python for practical data analysis, including:

• Data analysts and aspiring data scientists
• Researchers and research assistants
• Monitoring and evaluation officers and programme analysts
• Public health, epidemiology and social science professionals
• Government, NGO and development sector staff working with data
• Statistics officers, information management staff and reporting personnel
• Postgraduate students and academic researchers
• Consultants and professionals seeking a scalable, code-based analytical tool
• Anyone moving from spreadsheet-based analysis to Python-driven workflows

The course is delivered in a hands-on computer lab format using notebooks. Each technique is demonstrated and then coded immediately by participants on real and realistic datasets, building code from first lines upward. The focus is on doing and understanding, and a daily practical session takes an analysis from data to findings.
•Live demonstration and guided coding in Python
•Hands-on analysis in notebooks with real datasets
•Building code from first lines upward
•Step by step interpretation of output
•A daily practical session and a final analysis project

Organisations whose staff complete this course can expect:

• Stronger internal capability for modern, code-based data analysis
• Better quality evidence for reporting, programme management and decision-making
• Reduced dependence on manual spreadsheet processes and external analysts
• Improved reproducibility and transparency in analytical work
• Stronger use of data already being collected across programmes, operations and studies
• Better visual communication of findings through charts and structured outputs
• A foundation for more advanced analytics, automation and data science capability
• A more technically capable team able to work confidently with scalable analytical tools

Participants completing this course will gain:

• Confidence in using Python as a practical tool for data analysis
• The ability to build reusable analytical workflows in code
• Stronger data wrangling capability with pandas
• Better understanding of how to interpret statistical output in a code-based environment
• A valuable and highly marketable skill applicable across data, research and analytics roles
• Greater independence in handling datasets without relying on spreadsheet-only workflows
• A foundation for future progression into automation, machine learning and broader data science
• Stronger ability to present findings clearly through charts, summaries and model outputs

At Strategic Revenue Africa, our certification goes beyond proof of attendance—it represents practical competence and measurable capability. Upon successful completion of our training programs, participants are awarded a Certificate of Completion from Strategic Revenue Africa, recognizing their ability to apply acquired knowledge in real-world settings. As an organization focused on architecting sustainable revenue and strengthening organizational performance, our certifications signal that participants are equipped with skills that drive results, not just theory.

Basic computer literacy is required. No prior programming, Python or advanced statistics experience is assumed. The course is suitable for complete beginners as well as for participants who have some exposure to Python but want a more structured and applied approach to using it for data analysis. A basic familiarity with data, spreadsheets, survey datasets or reporting tables will be helpful but is not essential. Participants should have access to Python during the course, typically through Anaconda, Jupyter Notebook, JupyterLab or another suitable environment.