Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.
Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.
Get started on the path to exploring and visualizing your own data with the tidyverse, a powerful and popular collection of data science tools within R.
Learn to transform and manipulate your data using dplyr.
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.
Learn to combine data across multiple tables to answer more complex questions with dplyr.
Learn the language of data, study types, sampling strategies, and experimental design.
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
In this course you will learn the basics of machine learning for classification.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.
Take your R skills up a notch by learning to write efficient, reusable functions.
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests.
Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
Shiny is an R package that makes it easy to build interactive web apps directly in R, allowing your team to explore your data as dashboards or visualizations.
Learn to perform linear and logistic regression with multiple explanatory variables.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Learn the essentials of parsing, manipulating and computing with dates and times in R.
Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.
Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Learn how to make predictions about the future using time series forecasting in R.
Transform almost any dataset into a tidy format to make analysis easier.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
In this course you'll learn to add multiple variables to linear models and to use logistic regression for classification.
Learn the core techniques necessary to extract meaningful insights from time series data.
Master sampling to get more accurate statistics with less data.
The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.
Learn to work with data using tools from the tidyverse, and master the important skills of taming and tidying your data.
Learn how to pull character strings apart, put them back together and use the stringr package.
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
In this course you will learn to fit hierarchical models with random effects.
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
In this course, you'll learn about the concepts of random variables, distributions, and conditioning.
Learn to easily summarize and manipulate lists using the purrr package.
Analyze text data in R using the tidy framework.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
In this course you'll learn how to leverage statistical techniques for working with categorical data.
In this course you will gain an overview clinical trial designs, determine the numbers of patients needed and conduct statistical analyses.
Learn survey design using common design structures followed by visualizing and analyzing survey results.
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Explore latent variables, such as personality using exploratory and confirmatory factor analyses.
This course will show you how to combine and merge datasets with data.table.
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!
Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
Learn to streamline your machine learning workflows with tidymodels.
In this course you'll learn to analyze and visualize network data with the igraph package.
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Apply your importing and data cleaning skills to real-world soccer data.
Discover the top tools Kaggle participants use for data science and machine learning.
Use tree-based machine learning methods to identify the characteristics of legendary Pokémon.
Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.
Load, clean, and explore Super Bowl data in the age of soaring ad costs and flashy halftime shows.
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!
Predict the impact of climate change on bird distributions using spatial data and machine learning.
Use cluster analysis to glean insights into cryptocurrency gambling behavior.
Apply unsupervised learning techniques to help plan an education program in Argentina.
Use data science to catch criminals, plus find new ways to volunteer personal time for social good.
Explore the salary potential of college majors with a k-means cluster analysis.
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.
Experiment with clustering algorithms to help doctors inform treatment for heart disease patients.
Explore acoustic backscatter data to find fish in the U.S. Atlantic Ocean.
Write functions to forecast time series of food prices in Rwanda.
Apply text mining to Donald Trump's tweets to confirm if he writes the (angrier) Android half.
Use regression trees and random forests to find places where New York taxi drivers earn the most.
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Apply your skills from "Working with Dates and Times in R" to breathalyzer data from Ames, Iowa.
Create and explore interactive maps using Leaflet to determine where to open the next Chipotle.
How can we find a good strategy for reducing traffic-related deaths?
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.
Analyze the relative popularity of programming languages over time based on Stack Overflow data.
Explore a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
Flex your data manipulation muscles on breath alcohol test data from Ames, Iowa, USA.
Compare life expectancy across countries and genders with ggplot2.
Analyze data from the hit mobile game, Candy Crush Saga.
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Analyze the relative popularity of programming languages over time based on Stack Overflow data.
Write functions to forecast time series of food prices in Rwanda.