Introduction to Statistics in R
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.
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 to perform linear and logistic regression with multiple explanatory variables.
Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.
Master sampling to get more accurate statistics with less data.
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
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 about basic experimental design, a crucial part of any data analysis.
In this course, you'll learn about the concepts of random variables, distributions, and conditioning.
Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.
Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
Learn survey design using common design structures followed by visualizing and analyzing survey results.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
In this course you'll learn techniques for performing statistical inference on numerical data.
GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
In this course you'll learn how to perform inference using linear models.
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 the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.
Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
In this course you'll learn how to leverage statistical techniques for working with categorical data.
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.
Learn to analyze, plot, and model multivariate data.
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 to analyze and model customer choice data in R.
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.
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.
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
Learn to build simple models of market response to increase the effectiveness of your marketing plans.
In this course, you'll learn how to implement more advanced Bayesian models using RJAGS.
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Learn strategies for answering probability questions in R by solving a variety of probability puzzles.
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.
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.
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.
Explore habitat data using factor and survival analysis tools.
Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.
Perform a hypothesis test to determine if more goals are scored in women's soccer matches than men's!