Data & AnalyticsIntermediate
Statistical Analysis
Understand and apply statistics to make better decisions from data.
22 hoursDr. Sarah Chen4.7 (4,200 learners)
About this course
A practical statistics course using Python (pandas, scipy, and statsmodels). You will go from descriptive statistics through to hypothesis testing, regression, and Bayesian basics — with a focus on applying each concept to real business and research problems rather than abstract proofs.
Target audience: Data analysts, researchers, product managers, scientists
What you will learn
- Descriptive statistics
- Hypothesis testing
- Regression
- Probability
- Python for statistics
Course syllabus
10 modules · video + exercises
- 1Descriptive statistics: mean, median, variance, skew, and kurtosis
- 2Probability distributions: normal, binomial, Poisson, and t-distribution
- 3Sampling, confidence intervals, and margin of error
- 4Hypothesis testing: null hypothesis, p-values, and significance
- 5Z-tests and t-tests: comparing means
- 6Chi-square tests: categorical data analysis
- 7ANOVA: comparing multiple groups
- 8Correlation and simple linear regression
- 9Multiple regression: coefficients, R², and model diagnostics
- 10Introduction to Bayesian thinking
Prerequisites
- –Python basics
- –High-school maths
Frequently asked questions
Do I need strong maths to take this course?
High-school level maths is sufficient. The course focuses on intuition and application rather than mathematical proofs. Calculus is not required.
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