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.
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.
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 how to use graphical and numerical techniques to begin uncovering the structure of your data.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.
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 write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.
Learn to clean data as quickly and accurately as possible to help your business move from raw data to awesome insights.
Take your R skills up a notch by learning to write efficient, reusable functions.
Master sampling to get more accurate statistics with less data.
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.
Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
Learn to perform linear and logistic regression with multiple explanatory variables.
Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.
The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.
Parse data in any format. Whether it's flat files, statistical software, databases, or data right from the web.
Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.
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.
In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.
This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.
Use data manipulation and visualization skills to explore the historical voting of the United Nations General Assembly.
Learn the core techniques necessary to extract meaningful insights from time series data.
This course will show you how to combine and merge datasets with data.table.
Analyze text data in R using the tidy framework.
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
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.
In this course, you'll learn about the concepts of random variables, distributions, and conditioning.
Learn to use essential Bioconductor packages for bioinformatics using datasets from viruses, fungi, humans, and plants!
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.
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 to streamline your machine learning workflows with tidymodels.
Learn how to use plotly in R to create interactive data visualizations to enhance your data storytelling.
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Learn how to pull character strings apart, put them back together and use the stringr package.
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
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 visualize time series in R, then practice with a stock-picking case study.
Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.
Analyze spatial data using the sf and raster packages.
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Learn survey design using common design structures followed by visualizing and analyzing survey results.
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 to build dashboards using the shinydashboard package.
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 create static and interactive dashboards using flexdashboard and shiny.
In this course you'll learn how to perform inference using linear models.
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.
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 to use the Bioconductor package limma for differential gene expression analysis.
Learn to easily summarize and manipulate lists using the purrr package.
Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
In this course you'll learn how to leverage statistical techniques for working with categorical data.
In this course you'll learn techniques for performing statistical inference on numerical data.
Learn the bag of words technique for text mining with R.
Create and share your own R Packages!
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.
Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.
Learn how to make sense of spatial data and deal with various classes of statistical problems associated with it.
This course is designed to get you up to speed with the most important and powerful methodologies in statistics.
In this course, you'll learn how to implement more advanced Bayesian models using RJAGS.
Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!
Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.
Learn the fundamentals of valuing stocks.
Advance you R finance skills to backtest, analyze, and optimize financial portfolios.
In this course you will gain an overview clinical trial designs, determine the numbers of patients needed and conduct statistical analyses.
Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.
Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.
Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.
Continue learning with purrr to create robust, clean, and easy to maintain iterative code.
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 how to access financial data from local files as well as from internet sources.
Learn how to tune your model's hyperparameters to get the best predictive results.
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.
Learn how to perform advanced dplyr transformations and incorporate dplyr and ggplot2 code in functions.
Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.
Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.
Learn how to analyse and interpret ChIP-seq data with the help of Bioconductor using a human cancer dataset.
Learn defensive programming in R to make your code more robust.
Learn to detect fraud with analytics in R.
Prepare for your upcoming machine learning interview by working through these practice questions that span across important topics in machine learning.
This course covers in detail the tools available in R for parallel computing.
Learn dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.
Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.
Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.
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 create animated graphics and linked views entirely in R with plotly.
Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.
In this course you'll learn how to use data science for several common marketing tasks.
Explore association rules in market basket analysis with R by analyzing retail data and creating movie recommendations.
Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.
Learn R for data science by wrangling, visualizing, and modeling political data like polls and election results.
Predict employee turnover and design retention strategies.
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
Diagnose, visualize and treat missing data with a range of imputation techniques with tips to improve your results.
Learn to rapidly visualize and explore demographic data from the United States Census Bureau using tidyverse tools.
Learn how to use TensorFlow, a state-of-the-art machine learning framework that specializes in the ability to develop deep learning neural networks.
Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network
Learn how to analyze and visualize network data in the R programming language using the tidyverse approach.
Learn to analyze, plot, and model multivariate data.
Learn how to visualize big data in R using ggplot2 and trelliscopejs.
Use C++ to dramatically boost the performance of your R code.
Learn how to write scalable code for working with big data in R using the bigmemory and iotools packages.
Design surveys to get actionable insights via reviewing of survey design structures and visualizing and analyzing survey results.
In this follow-up course, you will expand your stat modeling skills from the introduction and dive into more advanced concepts.
Learn how to fit topic models using the Latent Dirichlet Allocation algorithm.
Learn to analyze and model customer choice data in R.
Learn a variety of feature engineering techniques to develop meaningful features that will uncover useful insights about your machine learning models.
Learn how to apply advanced dimensionality techniques such as t-SNE and GLRM.
Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.
Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.
Learn to build simple models of market response to increase the effectiveness of your marketing plans.
Publicly release data sets with a differential privacy guarantee.
Learn strategies for answering probability questions in R by solving a variety of probability puzzles.