Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests.
Learn to perform linear and logistic regression with multiple explanatory variables.
Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.
In this course, you'll learn about the concepts of random variables, distributions, and conditioning.
In this course you'll learn techniques for performing statistical inference on numerical data.
In this course you'll learn to add multiple variables to linear models and to use logistic regression for classification.
Learn how to make predictions about the future using time series forecasting in R.
Learn the core techniques necessary to extract meaningful insights from time series data.
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
In this course you will learn to fit hierarchical models with random effects.
In this course you'll learn how to leverage statistical techniques for working with categorical data.
Master sampling to get more accurate statistics with less data.
Learn to analyze and model customer choice data in R.
Explore Linear Regression in a tidy framework.
In this course you'll learn how to perform inference using linear models.
Learn survey design using common design structures followed by visualizing and analyzing survey results.
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
Learn the language of data, study types, sampling strategies, and experimental design.
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
In this course you'll learn to analyze and visualize network data with the igraph package.
Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!
Learn how to make sense of spatial data and deal with various classes of statistical problems associated with it.
Explore latent variables, such as personality using exploratory and confirmatory factor analyses.
Analyze spatial data using the sf and raster packages.
In this course you'll learn about basic experimental design, a crucial part of any data analysis.
This course is designed to get you up to speed with the most important and powerful methodologies in statistics.
In this course you will gain an overview clinical trial designs, determine the numbers of patients needed and conduct statistical analyses.
Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
Learn to analyze, plot, and model multivariate data.
GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.
Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.
Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.
In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.
Design surveys to get actionable insights via reviewing of survey design structures and visualizing and analyzing survey results.
Learn how to analyze and visualize network data in the R programming language using the tidyverse approach.
In this course, you'll learn how to implement more advanced Bayesian models using RJAGS.
Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.
Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
In this follow-up course, you will expand your stat modeling skills from the introduction and dive into more advanced concepts.
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
Publicly release data sets with a differential privacy guarantee.
Learn to build simple models of market response to increase the effectiveness of your marketing plans.
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.
Analyze health survey data to determine how BMI is associated with physical activity and smoking.
Apply hierarchical and mixed-effect models to analyze Maryland crime rates.
Use your logistic regression skills to protect people from becoming zombies!
Analyze admissions data from UC Berkeley and find out if the university was biased against women.
Analyze the dialog and IMDB ratings of 287 South Park episodes. Warning: contains explicit language.
Apply text mining to Donald Trump's tweets to confirm if he writes the (angrier) Android half.
Get ready for Halloween by digging into a FiveThirtyEight dataset with all your favorite candy!
Examine the relationship between heart rate and heart disease using multiple logistic regression.
Examine the network of connections among local health departments in the United States.