Statistics is the study of how best to collect, analyze, and draw conclusions from data. A strong foundation will serve you well, no matter what industry you work in. In this beginner’s track, you'll learn the concepts, topics, and techniques used by data scientists and statisticians every day—including observational studies and experiments, correlation, regression, exploratory data analysis, and inference. You’ll also develop your stats skills by working with real-world data, including Stack Overflow surveys, real estate prices, and medical shipment data. Start this track today to learn about the power of statistics and how it can be used in your day-to-day analyses!

Discover the power of statistics in R. Learn the key statistical concepts, topics, and techniques used by data scientists and statisticians every day.

with R

Statistics Fundamentals

Summary statistics gives you the tools you need to boil down massive datasets to reveal the highlights. In this chapter, you'll explore summary statistics including mean, median, and standard deviation, and learn how to accurately interpret them. You'll also develop your critical thinking skills, allowing you to choose the best summary statistics for your data.

Summary Statistics

In this chapter, you'll learn how to generate random samples and measure chance using probability. You'll work with real-world sales data to calculate the probability of a salesperson being successful. Finally, you’ll use the binomial distribution to model events with binary outcomes.

Random Numbers and Probability

It’s time to explore one of the most important probability distributions in statistics, normal distribution. You’ll create histograms to plot normal distributions and gain an understanding of the central limit theorem, before expanding your knowledge of statistical functions by adding the Poisson, exponential, and t-distributions to your repertoire. 

More Distributions and the Central Limit Theorem

In this chapter, you'll learn how to quantify the strength of a linear relationship between two variables, and explore how confounding variables can affect the relationship between two other variables. You'll also see how a study’s design can influence its results, change how the data should be analyzed, and potentially affect the reliability of your conclusions.

Correlation and Experimental Design

Food Consumption & Carbon Footprint

Amir's Sales

World Happiness Report

Statistics is the study of how to collect, analyze, and draw conclusions from data. It’s a hugely valuable tool that you can use to bring the future into focus and infer the answer to tons of questions. For example, what is the likelihood of someone purchasing your product, how many calls will your support team receive, and how many jeans sizes should you manufacture to fit 95% of the population? In this course, you'll use sales data to discover how to answer questions like these as you grow your statistical skills and learn how to calculate averages, use scatterplots to show the relationship between numeric values, and calculate correlation. You'll also tackle probability, the backbone of statistical reasoning, and learn how to conduct a well-designed study to draw your own conclusions from data.

Data Manipulation with dplyr 

Intermediate R

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Introduction to Statistics in R

Probability & Statistics

You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients.

Simple Linear Regression

In this chapter, you’ll discover how to use linear regression models to make predictions on Taiwanese house prices and Facebook advert clicks. You’ll also grow your regression skills as you get hands-on with model objects, understand the concept of "regression to the mean", and learn how to transform variables in a dataset.

Predictions and model objects

In this chapter, you’ll learn how to ask questions of your model to assess fit. You’ll learn how to quantify how well a linear regression model fits, diagnose model problems using visualizations, and understand the leverage and influence of each observation used to create the model.

Assessing model fit

Learn to fit logistic regression models. Using real-world data, you’ll predict the likelihood of a customer closing their bank account as probabilities of success and odds ratios, and quantify model performance using confusion matrices.

Simple logistic regression

Taiwan Real Estate

Ad conversion data

Churn data

Linear regression and logistic regression are the two most widely used statistical models and act like master keys, unlocking the secrets hidden in datasets. In this course, you’ll gain the skills you need to fit simple linear and logistic regressions. Through hands-on exercises, you’ll explore the relationships between variables in real-world datasets, including motor insurance claims, Taiwan house prices, fish sizes, and more. By the end of this course, you’ll know how to make predictions from your data,  quantify model performance, and diagnose problems with model fit.

Introduction to Data Visualization with ggplot2

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R. 

Introduction to Regression in R

Extend your linear regression skills to "parallel slopes" regression, with one numeric and one categorical explanatory variable. This is the first step towards conquering multiple linear regression.

Parallel Slopes

Explore the effect of interactions between explanatory variables. Considering interactions allows for more realistic models that can have better predictive power. You'll also deal with Simpson's Paradox: a non-intuitive result that arises when you have multiple explanatory variables.

Interactions

See how modeling, and linear regression in particular, makes it easy to work with more than two explanatory variables. Once you've mastered fitting linear regression models, you'll get to implement your own linear regression algorithm.

Multiple Linear Regression

Extend your logistic regression skills to multiple explanatory variables. Understand the logistic distribution, which underpins this form of regression. Finally, implement your own logistic regression algorithm.

Multiple Logistic Regression

Taiwan real estate prices

eBay Palm Pilot auctions

Bank churn

Linear regression and logistic regression are the two most widely used statistical models and act like master keys, unlocking the secrets hidden in datasets. This course builds on the skills you gained in "Introduction to Regression in R", covering linear and logistic regression with multiple explanatory variables. Through hands-on exercises, you’ll explore the relationships between variables in real-world datasets, Taiwan house prices and customer churn modeling, and more. By the end of this course, you’ll know how to include multiple explanatory variables in a model, understand how interactions between variables affect predictions, and understand how linear and logistic regression work.

Learn to perform linear and logistic regression with multiple explanatory variables.

Intermediate Regression in R

Learn what sampling is and why it is useful, understand the problems caused by convenience sampling, and learn about the differences between true randomness and pseudo-randomness.

Bias Any Stretch of the Imagination

Learn how to and when to perform the four methods of random sampling: simple, systematic, stratified, and cluster.

Don't get theory eyed

Learn how to quantify the accuracy of sample statistics using relative errors, and measure variation in your estimates by generating sampling distributions.

The n's justify the means

Learn how to use resampling to perform bootstrapping, used to estimate variation in an unknown population. Understand the difference between sampling distributions and bootstrap distributions.

Pull Your Data Up By Its Bootstraps

Coffee ratings

Spotify song attributes

Employee attrition

Sampling is a cornerstone of inference statistics and hypothesis testing. It's tremendously important in survey analysis and experimental design. This course explains when and why sampling is important, teaches you how to perform common types of sampling, from simple random sampling to more complex methods like stratified and cluster sampling. Later, the course covers estimating population statistics, and quantifying uncertainty in your estimates by generating sampling distributions and bootstrap distributions. Throughout the course, you'll explore real-world datasets on coffee ratings, Spotify songs, and employee attrition.

Master sampling to get more accurate statistics with less data.

Sampling in R

Learn why hypothesis testing is useful, and step through the workflow for a one sample proportion test. In doing so, you'll encounter important concepts like z-scores, p-p-values, and false negative and false positive errors. The Stack Overflow survey and late medical shipments datasets are introduced.

Yum, That Dish Tests Good

Learn how to test for differences in means between two groups using t-tests, and how to extend this to more than two groups using ANOVA and pairwise t-tests.

Pass Me ANOVA Glass of Iced t

Learn how to test for differences in proportions between two groups using proportion tests, extended it to more than two groups with chi-square independence tests, and return to the one sample case with chi-square goodness of fit tests.

Letting the Categoricals Out of the Bag

Learn about the assumptions made by parametric hypothesis tests, and saw how simulation-based and rank-based non-parametric tests can be used when those assumptions aren't met.

Time to Define the Relationship

Late shipments

Late shipments bootstrap distribution

Democratic presidential candidates by county

Hypothesis testing lets you ask questions about your datasets and answer them in a statistically rigorous way. In this course you'll learn how and when to use common tests like t-tests, proportion tests, and chi-square tests. You'll gain a deep understanding of how they work, and the assumptions that underlie them. You'll also learn how different hypothesis tests are related using the "There is only one test" framework, and use non-parametric tests that let you side-step the requirements of traditional hypothesis tests. Throughout the course, you'll explore a Stack Overflow user survey, and a dataset of late shipments of medical supplies.

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests.

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Statistics Fundamentals with R

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Join over 8,200,000 learners and start Statistics Fundamentals with R today!