Course Summary
This course explores the fundamentals of statistics, including probability theory, statistical inference, and hypothesis testing. Students will learn to apply statistical methods to real-world problems and interpret the results.Key Learning Points
- Learn the basics of probability theory and statistical inference
- Apply statistical methods to real-world problems
- Interpret statistical results and draw conclusions
Job Positions & Salaries of people who have taken this course might have
- Data Analyst
- USA: $62,453 - $96,000
- India: ₹292,000 - ₹1,329,000
- Spain: €25,000 - €50,000
- Statistician
- USA: $57,000 - $124,000
- India: ₹184,000 - ₹1,531,000
- Spain: €20,000 - €60,000
- Data Scientist
- USA: $85,000 - $150,000
- India: ₹647,000 - ₹2,650,000
- Spain: €25,000 - €70,000
Related Topics for further study
Learning Outcomes
- Understand the basics of probability theory and statistical inference
- Apply statistical methods to real-world problems
- Interpret statistical results and draw conclusions
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of algebra and calculus
- Familiarity with programming concepts (recommended)
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
Similar Courses
- Data Science Essentials
- Introduction to Data Science in Python
Related Education Paths
Notable People in This Field
- Nate Silver
- Hadley Wickham
Related Books
Description
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.
Outline
- Introduction and Descriptive Statistics for Exploring Data
- Course Welcome
- Meet Guenther Walther
- Introduction
- Pie Chart, Bar Graph, and Histograms
- Box-and-Whisker Plot and Scatter Plot
- Providing Context is Key for Statistical Analyses
- Pitfalls when Visualizing Information
- Mean and Median
- Percentiles, the Five Number Summary, and Standard Deviation
- [EXTRA] Industry Insight: Introduction to Andrew Radin
- Read First - Important Information About Your Course
- Course Slides
- Course Syllabus
- Meeting You - Pre-Course Survey
- Quick Quiz About the Requirements
- Introduction and Descriptive Statistics for Exploring Data
- Producing Data and Sampling
- Introduction
- Simple Random Sampling and Stratified Random Sampling
- Bias and Chance Error
- Observation vs. Experiment, Confounding, and the Placebo Effect
- The Logic of Randomized Controlled Experiments
- [EXTRA] Industry Insights: Filing a Patent for twoXAR
- Producing Data and Sampling
- Probability
- The Interpretation of Probability
- Complement, Equally Likely Outcomes, Addition, and Multiplication
- Four Rules Example: How to Deal with "At Least One"
- Solving Problems by Total Enumeration
- Bayes' Rule
- Bayesian Analysis
- Warner's Randomized Response Model
- [EXTRA] Industry Insights: Drug Discovery at twoXAR
- Probability
- Normal Approximation and Binomial Distribution
- The Normal Curve
- The Empirical Rule
- Standardizing Data and the Standard Normal Curve
- Normal Approximation
- Computing Percentiles with the Normal Approximation
- The Binomial Setting and Binomial Coefficient
- The Binomial Formula
- Random Variables and Probability Histograms
- Normal Approximation to the Binomial; Sampling Without Replacement
- [EXTRA] Industry Insights: Opportunities in Life Sciences
- The Normal Approximation for Data and the Binomial Distribution
- Sampling Distributions and the Central Limit Theorem
- Parameter and Statistic
- Expected Value and Standard Error
- EV and SE of Sum, Percentages, and When Simulating
- The Square Root Law
- The Sampling Distribution
- Three Histograms
- The Law of Large Numbers
- The Central Limit Theorem
- When does the Central Limit Theorem Apply?
- Sampling Distributions and the Central Limit Theorem
- Regression
- Prediction is a Key Task of Statistics
- The Correlation Coefficient
- Correlation Measures Linear Association
- Regression Line and the Method of Least Squares
- Regression to the Mean, The Regression Fallacy
- Predicting y from x and x from y
- Normal Approximation Given x
- Residual Plots, Heteroscedasticity, and Transformations
- Outliers and Influential Points
- [EXTRA] Industry Insights: Challenges to Using Data Science in Medicine
- Regression
- Confidence Intervals
- Interpretation of a Confidence Interval
- Using the Central Limit Theorem to Find a Confidence Interval
- Estimating the Standard Error with the Bootstrap Principle
- More About Confidence Intervals
- Confidence Intervals
- Tests of Significance
- The Idea Behind Testing Hypotheses
- Setting Up a Test Statistic
- p-values as Measures of Evidence
- Distinguishing Coke and Pepsi by Taste
- The t-test
- Statistical Significance vs. Importance
- The Two-Sample z-test
- Matched Pairs
- [EXTRA] Industry Insights: Hiring Data Science Talent
- Tests of Significance
- Resampling
- Using Computer Simulations in Place of Calculations
- Using the Law of Large Numbers to Approximate Quantities of Interest
- Plug-in Principle
- The Parametric Bootstrap and Bootstrap Confidence Intervals
- Bootstrapping in Regression
- Resampling
- Analysis of Categorical Data
- Relationships Between Two Categorical Variables
- The Color Proportions of M&Ms
- The Chi-Square Test for Homogeneity and Independence
- Analysis of Categorical Data
- One-Way Analysis of Variance (ANOVA)
- Comparing Several Means
- The Idea of Analysis of Variance
- Using the F Distribution to Evaluate ANOVA
- More on ANOVA
- [EXTRA] Industry Insights: Starting Your Career in Data Science
- One-Way Analysis of Variance
- Multiple Comparisons
- Data Snooping and the Multiple Testing Fallacy, Reproducibility and Replicability
- Bonferroni Correction, False Discovery Rate, and Data Splitting
- Summary
- Thank You and Course Evaluation
- Multiple Comparisons
Summary of User Reviews
Discover the fundamentals of statistics with Stanford University. This course is highly recommended by many users due to its comprehensive content and interactive approach. Students are able to learn at their own pace and receive support from peers and instructors. One key aspect of the course that many users found to be good is the clear and concise explanation of complex topics.Pros from User Reviews
- Comprehensive content
- Interactive approach
- Learn at your own pace
- Support from peers and instructors
- Clear and concise explanation of complex topics
Cons from User Reviews
- Some technical issues reported
- Lack of personal interaction with instructors
- Quizzes can be too difficult
- Limited practical application of concepts
- Not suitable for advanced learners
Guenther Walther
4.5 Raiting
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