Applied Statistical Analysis & Modelling with Python
Build a rigorous, practical foundation in applied statistics — from describing data and drawing inferences to building and interpreting regression models — and prepare yourself for advanced work in Data Science, Machine Learning, research, and analytics.
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December 14, 2026
The statistics you need to reason with data
Data Science, Machine Learning & AI, research, analytics, and evidence-based decision-making all depend on a solid foundation in applied statistics. Without it, models are fragile and conclusions are unreliable.
You will learn through structured instruction, guided demonstrations, and practical exercises designed to build confidence and turn statistical knowledge into practical skills. Strong emphasis is placed on applying the right method to the right problem; interpreting results responsibly; and presenting findings clearly through publication-ready tables and visualizations.
What you will learn
A comprehensive, structured curriculum covering the essential statistical concepts, methods, and techniques needed for practical data analysis and modelling in Python.
Learn how to summarize, describe, and communicate the key characteristics of real-world datasets. You'll compute measures of central tendency, spread, and shape, and learn how to turn raw data into clear, interpretable statistical summaries using Pandas and NumPy.
Move beyond raw DataFrame output. Learn how to design clean, publication-ready statistical tables that communicate results clearly to technical and non-technical audiences, using Pandas styling and modern Python table libraries.
Learn how to draw reliable conclusions from sample data. You'll build a solid understanding of probability, sampling distributions, confidence intervals, and hypothesis testing, and apply them using Python's scientific computing stack.
Learn how to quantify relationships between variables and build interpretable regression models. You'll cover correlation, simple and multiple linear regression, model diagnostics, and how to communicate results responsibly.
Learn how to compare means across multiple groups and design experiments that yield reliable conclusions. You'll cover one-way and two-way ANOVA, post-hoc testing, and the assumptions that make ANOVA valid.
Learn how to model binary outcomes and interpret results in terms of odds and probabilities. You'll cover logistic regression, model evaluation, and how to apply it to classification problems in research, business, and the sciences.
Built for your next step
Applied statistics is at the core of data science, analytics, research, and evidence-based decision-making. Strong skills in descriptive statistics, inference, regression, ANOVA, and logistic modelling provide the foundation needed to analyze data, test hypotheses, build interpretable models, and progress into more specialized technical roles.
Strengthen your statistical foundation for modeling, experimentation, and applied machine learning.
Move from describing data to drawing reliable inferences and building interpretable models.
Analyze research data rigorously, test hypotheses, and produce defensible, evidence-based findings.
Apply classical statistical methods end-to-end in Python with clean, reproducible workflows.
Develop practical statistical modelling skills that support academic study, research, and teaching.
Understand the statistics behind the dashboards, reports, and decisions you work with every day.
What you will be able to do
Summarize and describe real-world datasets with the right descriptive statistics.
Present statistical results in clean, publication-ready tables.
Apply probability, sampling distributions, and confidence intervals correctly.
Design and interpret hypothesis tests with confidence.
Quantify relationships using correlation and linear regression.
Compare group means using ANOVA and appropriate post-hoc tests.
Model binary outcomes using logistic regression.
Communicate statistical findings clearly to support decision-making.
Learn Skills That Move Your Career Forward
We don't just teach theory. We build job-ready professionals. Every lesson is hands-on, project-based, and designed around the real problems you face in your day-to-day work. Our instructors are industry experts who have developed real products at scale, people who've solved the exact problems you'll encounter.
Questions about this course?
Is this a Data Science or Machine Learning course?
No. This is a practical applied statistics and statistical modelling course. It builds the statistical reasoning, inference, and modelling skills you need before progressing into more advanced Data Science, Machine Learning, AI, or research-focused training.
Who is this course designed for?
It is designed for Data Scientists and ML Engineers, Data and Business Analysts, researchers and M&E professionals, statisticians, university and TVET students and lecturers, and any professional who works with data and wants a rigorous, practical foundation in applied statistics with Python.
Do I need prior Python or statistics experience?
No. The course starts with descriptive statistics and progressively builds up to inference and modelling. Some familiarity with Python is helpful, but the emphasis is on applying statistical concepts in Python, so learners with a basic programming background will be comfortable.
How is the course delivered?
Classes are held in person at our training center in Upper Hill, Nairobi, led by experienced instructors. Each session combines live demonstrations, hands-on exercises, and structured learning. Sessions run Monday to Friday from 8:00 AM to 4:30 PM (EAT).
What do I need to participate in the course?
Just bring a laptop capable of running Python and the required development tools, plus a willingness to participate in hands-on exercises. If you're using a work-issued laptop, make sure Python is already installed or that you have admin rights to install it yourself.
Can this course be offered online?
Yes — absolutely. Our standard format is in-person, but we also run the full course online through live, instructor-led sessions. You get the same curriculum, exercises, and mentorship, just delivered remotely.
Can the course be offered at our premises, at our own pace?
Yes. We offer fully customized on-site training at your premises, paced to suit your team. Content, schedule, and depth are all tailored to your organization.
Will I receive a certificate after completing the course?
Yes. Participants who successfully complete the course requirements receive a certificate of completion issued by STEM RESEARCH.
Learn from an experienced industry practitioner
John Indika
John is a Senior Software Engineer and Data Scientist with extensive experience in Python, R, scientific computing, data science, machine learning, and software engineering. He is the developer of the Stemfard API, a step-by-step mathematics computation platform designed to make mathematical and STEM computations programmable and accessible. He has also trained professionals, researchers, and students across universities, government institutions, NGOs, and other organizations both locally and internationally.
His approach combines software engineering, mathematical and scientific computing, and practical data skills, with an emphasis on clear explanations, hands-on learning, and solving real-world technical problems. He focuses on writing clean, efficient, maintainable, and high-performance code designed to scale and meet production requirements.
Build your statistical foundation.
Start with the applied statistics and modelling skills you need to reason with data and make better decisions.