Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.
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 produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.
Delve further into the Tidyverse by learning to transform and manipulate data with dplyr.
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.
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
Learn to combine data across multiple tables to answer more complex questions with dplyr.
Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
Learn to clean data as quickly and accurately as possible to help your business move from raw data to awesome insights.
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
In this course you will learn the basics of machine learning for classification.
Learn to perform linear and logistic regression with multiple explanatory variables.
Take your R skills up a notch by learning to write efficient, reusable functions.
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
Master sampling to get more accurate statistics with less data.
Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.
Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Transform almost any dataset into a tidy format to make analysis easier.
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.
The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.
Analyze text data in R using the tidy framework.
Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Learn to streamline your machine learning workflows with tidymodels.
Learn survey design using common design structures followed by visualizing and analyzing survey results.
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
In this course you'll learn how to leverage statistical techniques for working with categorical data.
Learn how to efficiently collect and download data from any website using R.
Learn the essentials of parsing, manipulating and computing with dates and times in R.
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Learn the core techniques necessary to extract meaningful insights from time series data.
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!
In this course you will learn to fit hierarchical models with random effects.
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 pull character strings apart, put them back together and use the stringr package.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
This course will show you how to combine and merge datasets with data.table.
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
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'll learn about the concepts of random variables, distributions, and conditioning.
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.
Visualize the rise of COVID-19 cases globally with ggplot2.
Use text mining to analyze Jeopardy! data.
Wrangle and visualize musical data to find common chords and compare the styles of different artists.
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.
Discover how the US bond yields behave using descriptive statistics and advanced modeling.
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.
Check what passwords fail to conform to the National Institute of Standards and Technology password guidelines.
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 R to make art and create imaginary flowers inspired by nature.
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.
If you have never done a DataCamp project, this is the place to start!
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.
Analyze athletics data to find new ways to scout and assess jumpers and throwers.
Compare life expectancy across countries and genders with ggplot2.
Analyze data from the hit mobile game, Candy Crush Saga.
Use data management and exploratory data analysis skills to help analyze the UK Dairy Industry.
Apply data importing and cleaning skills to extract insights about the New York City Airbnb market.
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.