Course Objective & Scope
Misreading model metrics and A/B test results is one of the most expensive data mistakes an organisation can make. This programme builds the statistics needed for data science through intuition and practice rather than memorised formulas.
Distributions, sampling, the central limit theorem, p-values, confidence intervals, t-tests and chi-square tests, correlation versus causation, and linear and logistic regression are covered in Python (SciPy, statsmodels). In an experiment scenario adapted from the organisation, participants calculate sample size, design the test and turn the result into a decision memo ready for management.
Who Should Attend?
- Data scientists and analysts who want a stronger statistical foundation
- Product and growth teams designing A/B tests and experiments
- Machine learning engineers who interpret model results
- Prerequisite: basic Python and pandas
Key Takeaways
- Summarise a dataset with the right descriptive statistics and describe its distribution
- Choose the appropriate hypothesis test and interpret p-values correctly
- Calculate sample size and duration for an A/B test
- Report uncertainty with confidence intervals
- Evaluate regression coefficients and assumptions
- Explain the difference between correlation and causation to stakeholders
Curriculum
Descriptive Statistics and Distributions
- Central tendency, spread and skewness
- Normal, binomial and Poisson distributions
Sampling and Inferential Statistics
- Central limit theorem and standard error
- Confidence intervals and bootstrapping
Hypothesis Testing
- t-test, chi-square and ANOVA
- p-values, power analysis and the multiple testing problem
A/B Testing and Experiment Design
- Sample size and test duration
- Common pitfalls: peeking, novelty effects
Regression and Modelling
- Linear and logistic regression, assumptions
- Interpreting coefficients with statsmodels
Other Courses in This Category
-
Gradient boosting (XGBoost, LightGBM), hyperparameter optimisation, time series forecasting, SHAP explainability and MLflow experiment tracking, for experienced data scientists taking models to production.
- Live Course
- 6 modules
-
Conceptual, logical and physical data modeling, dimensional modeling (Kimball), Data Vault and analytics-oriented data science foundations, covering TDWI body-of-knowledge topics hands-on for data warehouse and BI teams.
- Live Course
- 5 modules
-
Turning business questions into analysis plans, data preparation with SQL and Excel/Python, KPI definition, dashboard design in Power BI or Tableau and data storytelling, for teams moving into or strengthening the analyst role.
- Live Course
- 5 modules
-
Querying and aggregating relational databases with SELECT, WHERE, JOIN, GROUP BY, HAVING and subqueries: hands-on SQL training for business analysts and specialists who want self-service access to raw data.
- Live Course
- 5 modules
-
Window functions, recursive CTEs, cohort and time-series queries, semi-structured (JSON) processing and query tuning with EXPLAIN ANALYZE, for analysts and engineers working on large datasets.
- Live Course
- 5 modules
-
End-to-end data engineering for RAG, LLM and machine learning systems: data contracts, medallion architecture, feature and embedding pipelines, lineage and metadata management.
- Live Course
- 5 modules
Get in touch
