About this Course

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Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.
Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, Multiple Comparisons.

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Shareable Certificate

Earn a Certificate upon completion

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Beginner Level

Basic familiarity with computers and productivity software
No calculus required

Approx. 15 hours to complete

English

Subtitles: English

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Beginner Level

Basic familiarity with computers and productivity software
No calculus required

Approx. 15 hours to complete

English

Subtitles: English

Instructor

Instructor rating

4.53/5 (417 Ratings)

Guenther Walther

Professor of Statistics

Department of Statistics

141,208 Learners

1 Course

Syllabus - What you will learn from this course

Content Rating95%(7,373 ratings)

Week

1

Week 1

2 hours to complete

Introduction and Descriptive Statistics for Exploring Data

This module provides an overview of the course and a review of the main tools used in descriptive statistics to visualize information.

2 hours to complete

10 videos (Total 30 min), 4 readings, 2 quizzes

10 videos

Course Welcome2m

Meet Guenther Walther1m

Introduction1m

Pie Chart, Bar Graph, and Histograms6m

Box-and-Whisker Plot and Scatter Plot3m

Providing Context is Key for Statistical Analyses2m

Pitfalls when Visualizing Information1m

Mean and Median5m

Percentiles, the Five Number Summary, and Standard Deviation2m

[EXTRA] Industry Insight: Introduction to Andrew Radin3m

4 readings

Read First - Important Information About Your Course10m

Course Slides15m

Course Syllabus10m

Meeting You - Pre-Course Survey10m

2 practice exercises

Quick Quiz About the Requirements30m

Introduction and Descriptive Statistics for Exploring Data30m

Week

2

Week 2

1 hour to complete

Producing Data and Sampling

In this module, you will look at the main concepts for sampling and designing experiments. You will learn about curious pitfalls and how to evaluate the effectiveness of such experiments.

1 hour to complete

6 videos (Total 14 min)

1 hour to complete

Probability

In this module, you will learn about the definition of probability and the essential rules of probability that you will need for solving both simple and complex challenges. You will also learn about examples of how simple rules of probability are used to create solutions for real-life complex situations.

1 hour to complete

8 videos (Total 27 min)

Week

3

Week 3

1 hour to complete

Normal Approximation and Binomial Distribution

This module covers the empirical rule and normal approximation for data, a technique that is used in many statistical procedures. You will also learn about the binomial distribution and the basics of random variables.

1 hour to complete

10 videos (Total 27 min)

10 videos

The Normal Curve1m

The Empirical Rule2m

Standardizing Data and the Standard Normal Curve2m

Normal Approximation4m

Computing Percentiles with the Normal Approximation1m

The Binomial Setting and Binomial Coefficient4m

The Binomial Formula3m

Random Variables and Probability Histograms2m

Normal Approximation to the Binomial; Sampling Without Replacement3m

[EXTRA] Industry Insights: Opportunities in Life Sciences57s

1 practice exercise

The Normal Approximation for Data and the Binomial Distribution45m

1 hour to complete

Sampling Distributions and the Central Limit Theorem

In this module, you will learn about the Law of Large Numbers and the Central Limit Theorem. You will also learn how to differentiate between the different types of histograms present in statistical analysis.

1 hour to complete

9 videos (Total 23 min)

Week

4

Week 4

1 hour to complete

Regression

This module covers regression, arguably the most important statistical technique based on its versatility to solve different types of statistical problems. You will learn about inference, regression, and how to do regression diagnostics.

1 hour to complete

10 videos (Total 34 min)

10 videos

Prediction is a Key Task of Statistics1m

The Correlation Coefficient2m

Correlation Measures Linear Association4m

Regression Line and the Method of Least Squares2m

Regression to the Mean, The Regression Fallacy3m

Predicting y from x and x from y6m

Normal Approximation Given x2m

Residual Plots, Heteroscedasticity, and Transformations3m

Outliers and Influential Points4m

[EXTRA] Industry Insights: Challenges to Using Data Science in Medicine2m

1 practice exercise

Regression45m

Week

5

Week 5

1 hour to complete

Confidence Intervals

In this module, you will learn how to construct and interpret confidence intervals in standard situations.

1 hour to complete

4 videos (Total 15 min)

1 hour to complete

Tests of Significance

In this module, you will look at the logic behind testing and learn how to perform the appropriate statistical tests for different samples and situations. You will also learn about common misunderstandings and pitfalls in testing.

1 hour to complete

9 videos (Total 34 min)

Week

6

Week 6

1 hour to complete

Resampling

This module focuses on the two main methods used in computer-intensive statistical inference: The Monte Carlo method, and the Bootstrap method. You will learn about the theoretic principles behind these methods and how they are applied in different contexts, such as regression and constructing confidence intervals.

1 hour to complete

5 videos (Total 17 min)

1 hour to complete

Analysis of Categorical Data

This module focuses on the three important statistical analysis for categorical data: Chi-Square Goodness of Fit test, Chi-Square test of Homogeneity, and Chi-Square test of Independence.

1 hour to complete

3 videos (Total 14 min)

Week

7

Week 7

1 hour to complete

One-Way Analysis of Variance (ANOVA)

This module covers the basics of ANOVA and how F-tests work on one-way ANOVA examples.

1 hour to complete

5 videos (Total 16 min)

Week

8

Week 8

1 hour to complete

Multiple Comparisons

In this module, you will learn about very important issues that have surfaced in the era of big data: data snooping and the multiple testing fallacy. You will also explore the reasons behind challenges in data reproducibility and applicability, and how to prevent such issues in your own work.

1 hour to complete

3 videos (Total 12 min), 1 reading, 1 quiz

Reviews

4.5

223 reviews

5 stars
70.18%
4 stars
19.87%
3 stars
6.13%
2 stars
2.07%
1 star
1.72%

TOP REVIEWS FROM INTRODUCTION TO STATISTICS

by PSSep 5, 2021

Excellent course... Only improvement I can suggest is to add more problems in the lectures as well as quizzes.

by KENov 30, 2021

A very good course. Definitely a course to take for an introduction into Statistics. Also probably going to be very useful as I'm planning on taking Machine Learning.

by VPJan 15, 2022

Statistics has been introduced with minimal math, with real world examples, making the concepts easy to grasp.

by BGJan 8, 2022

Challenging but a good overview of basic principles of statistics. Will allow me to read with a more critical eye figures that are used to support arguments.

View all reviews

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Introduction to Statistics