Introduction to Statistics Course Syllabus

Overview: This beginner-friendly course from Stanford introduces core statistical concepts used in data analysis and decision-making. Designed for learners with no advanced math background, it covers descriptive and inferential statistics, probability, regression, and resampling methods. The course is self-paced with approximately 35–40 hours of content, structured across 7 modules. Each module includes video lectures, practical quizzes, and hands-on exercises to reinforce understanding and real-world application.

Module 1: Descriptive Statistics & Data Visualization

Estimated time: 5 hours

• Identify types of data and variables
• Create and interpret graphical representations (histograms, box plots)
• Calculate measures of central tendency (mean, median, mode)
• Compute measures of spread (variance, standard deviation)
• Summarize datasets using numerical and visual techniques

Module 2: Producing and Sampling Data

Estimated time: 5 hours

• Design surveys and experiments
• Apply random and stratified sampling methods
• Recognize sources of bias in data collection
• Distinguish between observational studies and randomized experiments

Module 3: Probability Concepts

Estimated time: 6 hours

• Apply basic rules of probability
• Calculate conditional probabilities and assess independence
• Work with discrete probability distributions (e.g., binomial)
• Understand continuous distributions including the normal distribution

Module 4: Sampling Distributions & Central Limit Theorem

Estimated time: 6 hours

• Describe how sample statistics vary across samples
• Construct and interpret sampling distributions
• Apply the Central Limit Theorem for inference
• Understand the role of sample size in estimation accuracy

Module 5: Regression Analysis

Estimated time: 5 hours

• Fit simple linear regression models
• Interpret slope and intercept in context
• Use correlation to measure linear association
• Analyze residuals and assess model fit

Module 6: Significance Tests

Estimated time: 6 hours

• Perform one- and two-sample t-tests
• Conduct chi-square tests for categorical data
• Interpret p-values and construct confidence intervals
• Understand Type I and Type II errors in hypothesis testing

Module 7: Resampling Techniques

Estimated time: 5 hours

• Apply bootstrapping to estimate uncertainty
• Use permutation tests for hypothesis testing
• Implement simulation-based inference methods
• Compare resampling approaches to traditional tests

Module 8: Multiple Comparisons

Estimated time: 4 hours

• Identify challenges with multiple hypothesis testing
• Apply corrections for false discovery rate
• Interpret results when conducting many tests simultaneously

Prerequisites

• Basic familiarity with arithmetic and reading comprehension
• No prior programming or advanced math required
• Willingness to engage with data conceptually and critically

What You'll Be Able to Do After

• Summarize and visualize data using appropriate statistical methods
• Design reliable data collection strategies and avoid bias
• Apply probability and sampling theory to real-world problems
• Conduct hypothesis tests and interpret results accurately
• Use regression and resampling techniques to analyze relationships in data