Learn key statistical concepts and techniques like exploratory data analysis, correlation, regression, and inference. 

Learn the key statistical concepts and techniques used by statisticians and data scientists every day. 

with R

Statistics Fundamentals

This chapter introduces terminology of datasets and data frames in R.

Language of data

In this chapter, you will learn about observational studies and experiments, scope of inference, and Simpson's paradox.

Study types and cautionary tales

This chapter defines various sampling strategies and their benefits/drawbacks as well as principles of experimental design.

Sampling strategies and experimental design

Apply terminology, principles, and R code learned in the first three chapters of this course to a case study looking at how the physical appearance of instructors impacts their students' course evaluations.

Case study

Course evaluation

UC Berkeley admissions

US state regions

Scientists seek to answer questions using rigorous methods and careful observations. These observations—collected from the likes of field notes, surveys, and experiments—form the backbone of a statistical investigation and are called data. Statistics is the study of how best to collect, analyze, and draw conclusions from data. It is helpful to put statistics in the context of a general process of investigation: 1) identify a question or problem; 2) collect relevant data on the topic; 3) analyze the data; and 4) form a conclusion. In this course, you'll focus on the first two steps of the process.

Introduction to the Tidyverse

Taught by Dr. Mine of Duke University, this course introduces the first part of statistical analysis: identifying a question and collecting relevant data.

Introduction to Data in R

Learn the language of data, study types,  sampling strategies, and experimental design.

Probability & Statistics

In this chapter, you will learn how to create graphical and numerical summaries of two categorical variables.

Exploring Categorical Data

In this chapter, you will learn how to graphically summarize numerical data.

Exploring Numerical Data

Now that we've looked at exploring categorical and numerical data, you'll learn some useful statistics for describing distributions of data.

Numerical Summaries

Apply what you've learned to explore and summarize a real world dataset in this case study of email spam.

Case Study

Cars data

Comics data

Immigration data

Raw life expectancy data

Names data

Raw U.S. income data

When your dataset is represented as a table or a database, it's difficult to observe much about it beyond its size and the types of variables it contains. In this course, you'll learn how to use graphical and numerical techniques to begin uncovering the structure of your data. Which variables suggest interesting relationships? Which observations are unusual? By the end of the course, you'll be able to answer these questions and more, while generating graphics that are both insightful and beautiful.

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

Exploratory Data Analysis in R

In this chapter, you will learn techniques for exploring bivariate relationships.

Visualizing two variables

This chapter introduces correlation as a means of quantifying bivariate relationships.

Correlation

With the notion of correlation under your belt, we'll now turn our attention to simple linear models in this chapter.

Simple linear regression

This chapter looks at how to interpret the coefficients in a regression model.

Interpreting regression models

In this final chapter, you'll learn how to assess the "fit" of a simple linear regression model.

Model Fit

Ultimately, data analysis is about understanding relationships among variables. Exploring data with multiple variables requires new, more complex tools, but enables a richer set of comparisons. In this course, you will learn how to describe relationships between two numerical quantities. You will characterize these relationships graphically, in the form of summary statistics, and through simple linear regression models.

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically through simple linear regression models.

Correlation and Regression in R

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.

In this chapter you'll learn about the class of linear models called "parallel slopes models." These include one numeric and one categorical explanatory variable.

Parallel Slopes

This chapter covers model evaluation. By looking at different properties of the model, including the adjusted R-squared, you'll learn to compare models so that you can select the best one. You'll also learn about interaction terms in linear models.

Evaluating and extending parallel slopes model

This chapter will show you how to add two, three, and even more numeric explanatory variables to a linear model.

Multiple Regression

In this chapter you'll learn about using logistic regression, a generalized linear model (GLM), to predict a binary outcome and classify observations.

Logistic Regression

Explore the relationship between price and the quality of food, service, and decor for Italian restaurants in NYC.

Case Study: Italian restaurants in NYC

Average SAT scores by state

New York City Zagat restaurant reviews

In this course you'll take your skills with simple linear regression to the next level. By learning multiple and logistic regression techniques you will gain the skills to model and predict both numeric and categorical outcomes using multiple input variables. You'll also learn how to fit, visualize, and interpret these models. Then you'll apply your skills to learn about Italian restaurants in New York City!

In this course you'll learn to add multiple variables to linear models and to use logistic regression for classification.

Multiple and Logistic Regression in R

An introduction to key parts of experimental design plus some power and sample size calculations.

Introduction to Experimental Design

Explore the Lending Club dataset plus build and validate basic experiments, including an A/B test.

Basic Experiments

Use the NHANES data to build a RCBD and BIBD experiment, including model validation and design tips to make sure the BIBD is valid.

Randomized Complete (& Balanced Incomplete) Block Designs

Evaluate the NYC SAT scores data and deal with its missing values, then evaluate Latin Square, Graeco-Latin Square, and Factorial experiments.

Latin Squares, Graeco-Latin Squares, & Factorial experiments

sample of Lending Club data

NHANES Body Measures

NHANES Demographics

NHANES final combined dataset

NHANES Medical Conditions

NYC SAT Scores

Experimental design is a crucial part of data analysis in any field, whether you work in business, health or tech. If you want to use data to answer a question, you need to design an experiment! In this course you will learn about basic experimental design, including block and factorial designs, and commonly used statistical tests, such as the t-tests and ANOVAs. You will use built-in R data and real world datasets including the CDC NHANES survey, SAT Scores from NY Public Schools, and Lending Club Loan Data. Following the course, you will be able to design and analyze your own experiments!

In this course you'll learn about basic experimental design, a crucial part of any data analysis.

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Statistics Fundamentals with R

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Loved by learners at thousands of companies

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Join over .css-y34wa5{color:#03ef62;}6,970,000 learners and start Statistics Fundamentals with R today!

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Join over 6,970,000 learners and start Statistics Fundamentals with R today!