Introduction to R
Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.
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.
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.
Delve further into the Tidyverse by learning to transform and manipulate data with dplyr.
Learn to clean data as quickly and accurately as possible to help your business move from raw data to awesome insights.
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
Learn to perform linear and logistic regression with multiple explanatory variables.
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.
In this course you will learn the basics of machine learning for classification.
Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.
Take your R skills up a notch by learning to write efficient, reusable functions.
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.
Transform almost any dataset into a tidy format to make analysis easier.
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.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.
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 how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
This course will show you how to combine and merge datasets with data.table.
Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
In this course you will learn to fit hierarchical models with random effects.
Analyze text data in R using the tidy framework.
Learn how to efficiently collect and download data from any website using R.
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
Learn how to use plotly in R to create interactive data visualizations to enhance your data storytelling.
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
In this course you'll learn about basic experimental design, a crucial part of any data analysis.
Learn how to produce interactive web maps with ease using leaflet.
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.
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!
Learn the essentials of parsing, manipulating and computing with dates and times in R.
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.
Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.
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.
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.
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!
Predict the impact of climate change on bird distributions using spatial data and machine learning.
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.
Experiment with clustering algorithms to help doctors inform treatment for heart disease patients.
Write functions to forecast time series of food prices in Rwanda.
Use regression trees and random forests to find places where New York taxi drivers earn the most.
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.
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.
Build a regression model for a DVD rental firm to predict rental duration. Evaluate models to recommend the best one.
Explore habitat data using factor and survival analysis tools.
Help a fast food chain save money and place more accurate orders by building a model to predict food sales.
Flex your data manipulation muscles on breath alcohol test data from Ames, Iowa, USA.
Cluster NBA players by performance stats in the 2022-2023 season.
Analyze grocery store data to identify shopping patterns and preferences.
Exploring flight data from NYC using advanced data joining techniques in R.
Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.
Apply your knowledge of data types and categorical data to prepare a big dataset for modeling!
Reanalyze the data behind one of the most important discoveries of modern medicine: handwashing.
Explore a dataset containing a century's worth of Nobel Laureates. Who won? Who got snubbed?
Analyze athletics data to find new ways to scout and assess jumpers and throwers.
Perform a hypothesis test to determine if more goals are scored in women's soccer matches than men's!
Compare life expectancy across countries and genders with ggplot2.
Clean customer data and use logistic regression to predict whether people will make a claim on their car insurance!
Apply your importing and cleaning data and data manipulation skills to explore New York City Airbnb data.
Analyze data from the hit mobile game, Candy Crush Saga.
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.