## Introduction to R

Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.

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140 results ## Introduction to R

## Intermediate R

## Introduction to the Tidyverse

## Data Manipulation with dplyr

## Introduction to Data Visualization with ggplot2

## Introduction to Statistics in R

## Introduction to Importing Data in R

## Introduction to Regression in R

## Exploratory Data Analysis in R

## Joining Data with dplyr

## Introduction to R for Finance

## Intermediate Data Visualization with ggplot2

## Reporting with R Markdown

## Cleaning Data in R

## Supervised Learning in R: Classification

## Hypothesis Testing in R

## Intermediate Regression in R

## Modeling with Data in the Tidyverse

## Introduction to Writing Functions in R

## Reshaping Data with tidyr

## Foundations of Probability in R

## Writing Efficient R Code

## Sampling in R

## Machine Learning with caret in R

## Intermediate R for Finance

## Intermediate Importing Data in R

## Manipulating Time Series Data with xts and zoo in R

## Case Study: Exploratory Data Analysis in R

## Building Web Applications with Shiny in R

## Time Series Analysis in R

## Supervised Learning in R: Regression

## Working with Dates and Times in R

## String Manipulation with stringr in R

## Foundations of Inference

## Visualization Best Practices in R

## Unsupervised Learning in R

## Cluster Analysis in R

## Importing and Managing Financial Data in R

## Web Scraping in R

## Communicating with Data in the Tidyverse

## Forecasting in R

## Introduction to Text Analysis in R

## Linear Algebra for Data Science in R

## Fundamentals of Bayesian Data Analysis in R

## Analyzing Survey Data in R

## RNA-Seq with Bioconductor in R

## Modeling with tidymodels in R

## Data Manipulation with data.table in R

## Generalized Linear Models in R

## Visualizing Geospatial Data in R

## Object-Oriented Programming with S3 and R6 in R

## Factor Analysis in R

## Hierarchical and Mixed Effects Models in R

## Introduction to Bioconductor in R

## Machine Learning in the Tidyverse

## Credit Risk Modeling in R

## ARIMA Models in R

## Categorical Data in the Tidyverse

## Machine Learning with Tree-Based Models in R

## Dealing With Missing Data in R

## Introduction to Portfolio Analysis in R

## Joining Data with data.table in R

## Case Studies: Building Web Applications with Shiny in R

## Interactive Maps with leaflet in R

## Network Analysis in R

## Text Mining with Bag-of-Words in R

## Visualizing Time Series Data in R

## Structural Equation Modeling with lavaan in R

## Foundations of Functional Programming with purrr

## Practicing Machine Learning Interview Questions in R

## Support Vector Machines in R

## HR Analytics: Predicting Employee Churn in R

## Quantitative Risk Management in R

## Survival Analysis in R

## Interactive Data Visualization with plotly in R

## Building Dashboards with shinydashboard

## Introduction to Feature Engineering in R

## Intermediate Portfolio Analysis in R

## Experimental Design in R

## Spatial Analysis with sf and raster in R

## Introduction to Natural Language Processing in R

## Introduction to A/B Testing in R

## Introduction to Statistical Modeling in R

## Designing and Analyzing Clinical Trials in R

## Programming with dplyr

## GARCH Models in R

## Nonlinear Modeling with Generalized Additive Models (GAMs) in R

## Introduction to Spark with sparklyr in R

## Spatial Statistics in R

## Handling Missing Data with Imputations in R

## Inference for Linear Regression in R

## Intermediate Regular Expressions in R

## Analyzing Social Media Data in R

## Building Dashboards with flexdashboard

## Differential Expression Analysis with limma in R

## Life Insurance Products Valuation in R

## ChIP-seq with Bioconductor in R

## Inference for Categorical Data in R

## Bond Valuation and Analysis in R

## Parallel Computing in R

## Sentiment Analysis in R

## Case Study: Analyzing City Time Series Data in R

## Machine Learning for Marketing Analytics in R

## Financial Trading in R

## Hyperparameter Tuning in R

## Inference for Numerical Data in R

## Defensive R Programming

## Survey and Measurement Development in R

## Fraud Detection in R

## Introduction to TensorFlow in R

## Business Process Analytics in R

## Analyzing Election and Polling Data in R

## Bayesian Regression Modeling with rstanarm

## Bayesian Modeling with RJAGS

## Forecasting Product Demand in R

## Optimizing R Code with Rcpp

## Scalable Data Processing in R

## HR Analytics: Exploring Employee Data in R

## Equity Valuation in R

## Market Basket Analysis in R

## Building Response Models in R

## Multivariate Probability Distributions in R

## Intermediate Statistical Modeling in R

## Topic Modeling in R

## Analyzing US Census Data in R

## Intermediate Functional Programming with purrr

## R For SAS Users

## Network Analysis in the Tidyverse

## Practicing Statistics Interview Questions in R

## Predictive Analytics using Networked Data in R

## Intermediate Interactive Data Visualization with plotly in R

## Introduction to Anomaly Detection in R

## Probability Puzzles in R

## Advanced Dimensionality Reduction in R

## Visualizing Big Data with Trelliscope in R

## Choice Modeling for Marketing in R

## Mixture Models in R

## Case Studies: Network Analysis in R

## Data Privacy and Anonymization in R

Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.

4 hoursProgrammingJonathan Cornelissencourses

Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.

6 hoursProgrammingFilip Schouwenaarscourses

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.

4 hoursProgrammingDavid Robinsoncourses

Learn to transform and manipulate your data using dplyr.

4 hoursData ManipulationDataCamp Content Creatorcourses

Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.

4 hoursData VisualizationRick Scavettacourses

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

4 hoursProbability & StatisticsMaggie Matsuicourses

In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.

3 hoursImporting & Cleaning DataFilip Schouwenaarscourses

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

4 hoursProbability & StatisticsRichie Cottoncourses

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

4 hoursProbability & StatisticsAndrew Braycourses

Learn to combine data across multiple tables to answer more complex questions with dplyr.

4 hoursData ManipulationDataCamp Content Creatorcourses

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

4 hoursApplied FinanceLore Dirickcourses

Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.

4 hoursData VisualizationRick Scavettacourses

R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.

4 hoursReportingAmy Petersoncourses

Develop the skills you need to go from raw data to awesome insights as quickly and accurately as possible.

4 hoursImporting & Cleaning DataMaggie Matsuicourses

In this course you will learn the basics of machine learning for classification.

4 hoursMachine LearningBrett Lantzcourses

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests.

4 hoursProbability & StatisticsRichie Cottoncourses

Learn to perform linear and logistic regression with multiple explanatory variables.

4 hoursProbability & StatisticsRichie Cottoncourses

Explore Linear Regression in a tidy framework.

4 hoursProbability & StatisticsAlbert Y. Kimcourses

Take your R skills up a notch by learning to write efficient, reusable functions.

4 hoursProgrammingRichie Cottoncourses

Transform almost any dataset into a tidy format to make analysis easier.

4 hoursData ManipulationJeroen Boeyecourses

In this course, you'll learn about the concepts of random variables, distributions, and conditioning.

4 hoursProbability & StatisticsDavid Robinsoncourses

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

4 hoursProgrammingColin Gillespiecourses

Master sampling to get more accurate statistics with less data.

4 hoursProbability & StatisticsRichie Cottoncourses

This course teaches the big ideas in machine learning like how to build and evaluate predictive models.

4 hoursMachine LearningZachary Deane-Mayercourses

Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.

5 hoursApplied FinanceLore Dirickcourses

Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.

3 hoursImporting & Cleaning DataFilip Schouwenaarscourses

The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.

4 hoursData ManipulationDataCamp Content Creatorcourses

Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.

4 hoursCase StudiesDavid Robinsoncourses

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.

4 hoursProgrammingkaelen medeiroscourses

Learn the core techniques necessary to extract meaningful insights from time series data.

4 hoursProbability & StatisticsDavid S. Mattesoncourses

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

4 hoursMachine LearningJohn Mountcourses

Learn the essentials of parsing, manipulating and computing with dates and times in R.

4 hoursProgrammingCharlotte Wickhamcourses

Learn how to pull character strings apart, put them back together and use the stringr package.

4 hoursProgrammingCharlotte Wickhamcourses

Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

4 hoursProbability & StatisticsJo Hardincourses

Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.

4 hoursData VisualizationNicholas Strayercourses

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

4 hoursMachine LearningHank Roarkcourses

Develop a strong intuition for how hierarchical and k-means clustering work and learn how to apply them to extract insights from your data.

4 hoursMachine LearningDmitriy Gorenshteyncourses

Learn how to access financial data from local files as well as from internet sources.

5 hoursApplied FinanceJoshua Ulrichcourses

Learn how to efficiently collect and download data from any website using R.

4 hoursImporting & Cleaning DataTimo Grossenbachercourses

Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.

4 hoursData VisualizationTimo Grossenbachercourses

Learn how to make predictions about the future using time series forecasting in R.

5 hoursProbability & StatisticsRob J. Hyndmancourses

Analyze text data in R using the tidy framework.

4 hoursData ManipulationMarc Dotsoncourses

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.

4 hoursProbability & StatisticsEric Eagercourses

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

4 hoursProbability & StatisticsRasmus Bååthcourses

Learn survey design using common design structures followed by visualizing and analyzing survey results.

4 hoursProbability & StatisticsKelly McConvillecourses

Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.

4 hoursOtherMary Pipercourses

Learn to streamline your machine learning workflows with tidymodels.

4 hoursMachine LearningDavid Svancercourses

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.

4 hoursData ManipulationMatt Dowlecourses

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

4 hoursProbability & StatisticsRichard Ericksoncourses

Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.

4 hoursData VisualizationCharlotte Wickhamcourses

Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.

4 hoursProgrammingRichie Cottoncourses

Explore latent variables, such as personality using exploratory and confirmatory factor analyses.

4 hoursProbability & StatisticsJennifer Brussowcourses

In this course you will learn to fit hierarchical models with random effects.

4 hoursProbability & StatisticsRichard Ericksoncourses

Learn to use essential bioconductor packages using datasets from virus, fungus, human and plants!

4 hoursOtherPaula Martinezcourses

Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.

5 hoursMachine LearningDmitriy Gorenshteyncourses

Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.

4 hoursApplied FinanceLore Dirickcourses

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

4 hoursProbability & StatisticsDavid Stoffercourses

Get ready to categorize! In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.

4 hoursData ManipulationEmily Robinsoncourses

Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.

4 hoursMachine LearningSandro Raabecourses

Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.

4 hoursImporting & Cleaning Datacourses

Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.

5 hoursApplied FinanceKris Boudtcourses

This course will show you how to combine and merge datasets with data.table.

4 hoursData ManipulationScott Ritchiecourses

Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!

4 hoursReportingDean Attalicourses

Learn how to produce interactive web maps with ease using leaflet.

4 hoursData VisualizationRich Majeruscourses

In this course you'll learn to analyze and visualize network data with the igraph package.

4 hoursProbability & StatisticsJAMES CURLEYcourses

Learn the bag of words technique for text mining with R.

4 hoursMachine LearningTed Kwartlercourses

Learn how to visualize time series in R, then practice with a stock-picking case study.

4 hoursData VisualizationArnaud Amsellemcourses

Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.

4 hoursProbability & StatisticsErin Buchanancourses

Learn to easily summarize and manipulate lists using the purrr package.

4 hoursProgrammingDataCamp Content Creatorcourses

Prepare for your upcoming machine learning interview by working through these practice questions that span across important topics in machine learning.

4 hoursMachine LearningRafael Falconcourses

This course will introduce the support vector machine (SVM) using an intuitive, visual approach.

4 hoursMachine LearningKailash Awaticourses

Predict employee turnover and design retention strategies.

4 hoursCase StudiesAnurag Guptacourses

Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.

5 hoursApplied FinanceAlexander J. McNeilcourses

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!

4 hoursProbability & StatisticsHeidi Seiboldcourses

Learn to create interactive graphics entirely in R with plotly.

4 hoursData VisualizationAdam Loycourses

In this course you'll learn to build dashboards using the shinydashboard package.

4 hoursReportingLucy D’Agostino McGowancourses

Learn a variety of feature engineering techniques to develop meaningful features that will uncover useful insights about your machine learning models.

4 hoursMachine LearningJose Hernandezcourses

Advance you R finance skills to backtest, analyze, and optimize financial portfolios.

5 hoursApplied FinanceRoss Bennettcourses

In this course you'll learn about basic experimental design, a crucial part of any data analysis.

4 hoursProbability & StatisticsDataCamp Content Creatorcourses

Analyze spatial data using the sf and raster packages.

4 hoursProbability & StatisticsZev Rosscourses

Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.

4 hoursMachine LearningKasey Jonescourses

Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.

4 hoursProbability & StatisticsDataCamp Content Creatorcourses

This course is designed to get you up to speed with the most important and powerful methodologies in statistics.

4 hoursProbability & StatisticsDaniel Kaplancourses

In this course you will gain an overview clinical trial designs, determine the numbers of patients needed and conduct statistical analyses.

4 hoursProbability & StatisticsTamuno Alfredcourses

Learn how to perform advanced dplyr transformations and incorporate dplyr and ggplot2 code in functions.

4 hoursData ManipulationDr. Chester Ismaycourses

Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.

4 hoursApplied FinanceKris Boudtcourses

GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.

4 hoursProbability & StatisticsDataCamp Content Creatorcourses

Learn how to analyze huge datasets using Apache Spark and R using the sparklyr package.

4 hoursProgrammingRichie Cottoncourses

Learn how to make sense of spatial data and deal with various classes of statistical problems associated with it.

4 hoursProbability & StatisticsBarry Rowlingsoncourses

Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.

4 hoursData ManipulationMichał Oleszakcourses

In this course you'll learn how to perform inference using linear models.

4 hoursProbability & StatisticsJo Hardincourses

Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.

4 hoursData ManipulationBenja Zehrcourses

Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.

4 hoursData ManipulationSowmya Vivekcourses

In this course you'll learn how to create static and interactive dashboards using flexdashboard and shiny.

4 hoursReportingElaine McVeycourses

Learn to use the Bioconductor package limma for differential gene expression analysis.

4 hoursOtherJohn Blischakcourses

Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.

4 hoursApplied FinanceKatrien Antoniocourses

Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.

4 hoursOtherPeter Humburgcourses

In this course you'll learn how to leverage statistical techniques for working with categorical data.

4 hoursProbability & StatisticsAndrew Braycourses

Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.

4 hoursApplied FinanceClifford Angcourses

This course covers in detail the tools available in R for parallel computing.

4 hoursProgrammingHana Sevcikovacourses

Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.

4 hoursMachine LearningTed Kwartlercourses

Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.

4 hoursCase StudiesLore Dirickcourses

In this course you'll learn how to use data science for several common marketing tasks.

4 hoursMachine LearningVerena Pfliegercourses

This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.

5 hoursApplied FinanceIlya Kipniscourses

Learn how to tune your model's hyperparameters to get the best predictive results.

4 hoursMachine LearningShirin Elsinghorst (formerly Glander)courses

In this course you'll learn techniques for performing statistical inference on numerical data.

4 hoursProbability & StatisticsMine Cetinkaya-Rundelcourses

Learn defensive programming in R to make your code more robust.

4 hoursProgrammingColin Gillespiecourses

Design surveys to get actionable insights via reviewing of survey design structures and visualizing and analyzing survey results.

4 hoursProbability & StatisticsGeorge Mountcourses

Learn to detect fraud with analytics in R.

4 hoursMachine LearningBart Baesenscourses

Learn how to use TensorFlow, a state-of-the-art machine learning framework that specializes in the ability to develop deep learning neural networks.

4 hoursMachine LearningColleen Bobbiecourses

Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.

4 hoursProbability & StatisticsGert Janssenswillencourses

Learn R for data science by wrangling, visualizing, and modeling political data like polls and election results.

4 hoursCase StudiesG Elliott Morriscourses

Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.

4 hoursProbability & StatisticsJake Thompsoncourses

In this course, you'll learn how to implement more advanced Bayesian models using RJAGS.

4 hoursProbability & StatisticsAlicia Johnsoncourses

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.

4 hoursProbability & StatisticsAric LaBarrcourses

Use C++ to dramatically boost the performance of your R code.

4 hoursProgrammingTeam ThinkRcourses

Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.

4 hoursProgrammingMichael Kanecourses

Manipulate, visualize, and perform statistical tests on HR data.

5 hoursCase StudiesBen Teuschcourses

Learn the fundamentals of valuing stocks.

4 hoursApplied FinanceClifford Angcourses

Explore association rules in market basket analysis with R by analyzing retail data and creating movie recommendations.

4 hoursData ManipulationChristopher Bruffaertscourses

Learn to build simple models of market response to increase the effectiveness of your marketing plans.

4 hoursProbability & Statisticscourses

Learn to analyze, plot, and model multivariate data.

4 hoursProbability & StatisticsSurajit Raycourses

In this follow-up course, you will expand your stat modeling skills from the introduction and dive into more advanced concepts.

4 hoursProbability & StatisticsDaniel Kaplancourses

Learn how to fit topic models using the Latent Dirichlet Allocation algorithm.

4 hoursMachine LearningPavel Oleinikovcourses

Learn to rapidly visualize and explore demographic data from the United States Census Bureau using tidyverse tools.

4 hoursOtherKyle Walkercourses

Continue learning with purrr to create robust, clean, and easy to maintain iterative code.

4 hoursProgrammingColin FAYcourses

Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.

4 hoursProgrammingMelinda Higginscourses

Learn how to analyze and visualize network data in the R programming language using the tidyverse approach.

4 hoursProbability & StatisticsMassimo Franceschetcourses

In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.

4 hoursProbability & StatisticsZuzanna Chmielewskacourses

Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network

4 hoursProbability & StatisticsBart Baesenscourses

Learn to create animated graphics and linked views entirely in R with plotly.

4 hoursData VisualizationAdam Loycourses

Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.

4 hoursProbability & StatisticsDataCamp Content Creatorcourses

Learn strategies for answering probability questions in R by solving a variety of probability puzzles.

4 hoursProbability & StatisticsPeter Chicourses

Learn how to apply advanced dimensionality techniques such as t-SNE and GLRM.

4 hoursMachine LearningFederico Castanedocourses

Learn how to visualize big data in R using ggplot2 and trelliscopejs.

4 hoursData VisualizationRyan Hafencourses

Learn to analyze and model customer choice data in R.

4 hoursProbability & StatisticsElea McDonnell Feitcourses

Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.

4 hoursProbability & StatisticsVictor Medinacourses

Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.

4 hoursProbability & StatisticsTed Hartcourses

Publicly release data sets with a differential privacy guarantee.

4 hoursProbability & StatisticsClaire Bowencourses