Statistics and Data Analysis Course for Beginners | Interactive Lessons, PDF eBook, Excel Workbook and Python

Learn statistics from scratch and use it with confidence. Statistics and Data Analysis: From Basic Concepts to Practical Applications is a complete beginner course. It takes you from your first average to analysing real-looking datasets and writing clear, honest reports. You only need basic maths.

What you get

• Interactive HTML course: 15 modules, 90 quiz questions with instant feedback, 62 exercises with answers, progress tracking, bookmarks and a 158-term glossary. It works offline on desktop and mobile.
• 9 interactive tools:descriptive statistics calculator
• probability calculators
• confidence intervals, plus a simulation of what “95% confidence” really means
• central limit theorem simulator
• hypothesis-testing calculator covering 7 tests
• 8 graded testing exercises
• chart explorer
• regression visualiser
• “Choose the right statistical method” decision tool

• 157-page PDF eBook: every module with charts, 51 formulas with every symbol explained, step-by-step worked examples, and an answers appendix.

• Editable Excel workbook: example and blank versions. It covers data cleaning, descriptive statistics, frequency tables, probability, confidence intervals, hypothesis tests, regression, a project template and a dashboard. It uses live formulas and no macros.

• Python package: 7 tested Jupyter notebooks and scripts using pandas, SciPy and Matplotlib, with their outputs already included.

• 7 real-world projects: household spending, a customer survey, sales over time, student performance, advertising and sales, an A/B test, and a dashboard. Each one goes from question to final report.
• 8 printable bonus resources:formula sheet
• chart selection guide
• method decision tree
• data cleaning checklist
• report template
• dataset worksheet
• glossary
• final project checklist

What you’ll learn

• Mean, median, standard deviation and quartiles
• Charts that tell the truth, and how to spot misleading ones
• Probability, the normal and binomial distributions, and sampling
• Confidence intervals and p-values, interpreted correctly
• t-tests, chi-square tests, ANOVA, correlation and regression
• Data cleaning in Excel and Python

The course also explains:

• why correlation isn’t causation
• why “significant” doesn’t always mean important
• how to report results honestly

Good to know

• All datasets are simulated for teaching, so every number can be checked.
• This is an educational course. It is not a professional qualification.
• Instant digital download.