Statistical Analysis & Data Science with Python

Course overview

A practical, project-based programme designed to strengthen the statistical and analytical skills required for modern data science. Students learn how to work with real-world datasets using Python, perform exploratory data analysis, apply descriptive and inferential statistics, test hypotheses, analyse relationships between variables and communicate findings effectively. The course provides a strong foundation for advanced analytics and machine learning.

Who it's for: Data analysts, aspiring data scientists, researchers, business professionals, graduates and Python users who want to strengthen their statistical analysis and data science skills.

What you will learn

• Understand the data science workflow from data collection to insight generation
• Use Python to load, clean and analyse real-world datasets
• Perform exploratory data analysis using Pandas and NumPy
• Apply descriptive statistics to understand datasets
• Understand probability and common probability distributions
• Perform statistical hypothesis tests
• Interpret p-values, confidence intervals and statistical significance
• Analyse relationships between variables using correlation and regression
• Identify patterns, trends, outliers and anomalies in datasets
• Create effective statistical visualisations with Python
• Communicate statistical findings to technical and non-technical audiences
• Apply statistical techniques to real-world business problems
• Build reproducible data analysis workflows using Python and Jupyter
• Develop a portfolio-ready statistical data science project

Curriculum

Prerequisites

• • Basic Python knowledge is recommended
• • Basic understanding of spreadsheets is helpful
• • Basic mathematics is recommended
• • No previous statistics experience required