This practical course builds the statistical foundation you need for confident, data-driven decisions. You will learn descriptive statistics (mean, median, standard deviation), probability and Bayes’ Theorem, key probability distributions (Binomial, Poisson, Normal), the Central Limit Theorem, and hypothesis testing (one-sample Z and T-tests). All lessons include Python-based analysis and visualisation. No prior statistics or advanced programming experience required.
Core skills and statistical concepts you will master
Descriptive statistics: Calculate and interpret mean, median, mode and standard deviation to summarise any dataset. Learn to identify outliers, manage missing values and create clear summaries that reveal underlying patterns.
Probability and conditional reasoning: Apply probability rules and calculate conditional probability. Use the powerful Bayes’ Theorem to solve real-world conditional probability problems and interpret results in practical contexts.
Probability distributions and inference: Model real-world scenarios using the Binomial, Poisson and Normal distributions. Understand and explain the core concepts of statistical inference and the Central Limit Theorem, which underpins sampling and confidence intervals.
Hypothesis testing: Formulate null and alternative hypotheses and perform one-sample Z and T-tests for population means. Construct and interpret confidence intervals, execute T-tests in practical cases, and learn how to validate decisions using statistical evidence.
Practical Python application, course outcomes and audience
Python for analysis and visualisation: Analyse and summarise datasets using Python to compute statistics and create data visualisations. Perform hypothesis testing (like T-tests) in Python to make data-driven decisions and validate results.
- Hands-on practice: Move beyond spreadsheets — write code to compute descriptive statistics, run Z and T-tests, handle data quality issues, and visualise results.
- Real-world readiness: Apply conditional probability and Bayes’ Theorem to practical problems and model scenarios with common distributions for decision-making.
- Who should take it: Beginners in Data Science or Machine Learning, analysts, students, aspiring Data Scientists, Python developers seeking statistical skills, business analysts moving beyond Excel, and professionals across finance, marketing and engineering.
Course provider and instructor: This course is offered by MTF Institute of Management, Technology and Finance — a global educational and research institute and official partner of IBM, Intel and Microsoft, present in 218 countries. Dr. Alex Amoroso leads the program with extensive academic and industry experience in research methodologies, product development and strategy.
Next steps: Enroll now, watch free preview lectures, and begin building the quantitative skills employers demand.
Conclusion: Mastering descriptive statistics, probability (including Bayes’ Theorem), probability distributions, the Central Limit Theorem, and hypothesis testing equips you to make reliable, data-driven decisions. This hands-on course uses Python to bridge the gap from data user to data analyst, emphasizing practical application, data quality handling and real-world examples. Enroll today with MTF Institute to build a rock-solid statistical foundation.
