From Data to Decisions: Inferential Statistics & Regression in Practice
Describing data is only the start. The real value comes from using it to draw conclusions, test assumptions and predict what happens next. That's where inferential statistics and regression come in.
This practical masterclass bridges the gap between statistical theory and real-world decisions. You'll learn how to go from a sample to confident conclusions about a whole population, how to test whether a result is real or just chance, and how to build and interpret regression models that explain relationships in your data. Throughout the session, you'll see how these methods drive decisions in industries like fintech, where getting the numbers right really matters.
What you'll learn
The difference between descriptive and inferential statistics, and why it matters
Sampling, confidence intervals and how to measure uncertainty
Hypothesis testing: p-values, significance and common misinterpretations to avoid
Linear regression: building, interpreting and evaluating a model
An introduction to logistic regression for yes/no decisions, such as credit risk
How to turn statistical output into clear recommendations for stakeholders
Who should attend
Aspiring data analysts and data scientists who want a stronger statistical foundation
Bootcamp learners and students moving from data analysis to modelling
Professionals in finance, business or research who make data-backed decisions
Anyone who has run a regression but isn't sure how to interpret it
What to bring: A laptop with Python (pandas, statsmodels or scikit-learn), or a free Google Colab account. Basic familiarity with Python and descriptive statistics helps.
About the Speaker
Crystal Wanjiru, Data Scientist
Crystal is a Data Scientist with more than two years of experience deploying machine learning models into production. She specialises in end-to-end ML systems, from data pipelines and database integrations to API-driven model deployment, and uses AI and LLMs to automate solutions. Much of her work has been in fintech, where she has built scalable solutions for credit risk and decision-making.
Crystal is passionate about turning complex data into actionable insights and helping others use data and AI for real-world impact.