Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
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.
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.
Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
Learn to use essential bioconductor packages using datasets from virus, fungus, human and plants!
Learn how to produce interactive web maps with ease using leaflet.
Leverage the power of tidyverse tools to create publication-quality graphics and custom-styled reports that communicate your results.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
In this course you'll learn to build dashboards using the shinydashboard package.
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
Learn the language of data, study types, sampling strategies, and experimental design.
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
Learn to create interactive graphics entirely in R with plotly.
In this course you'll learn to analyze and visualize network data with the igraph package.
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
Learn to streamline your machine learning workflows with tidymodels.
Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!
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!
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.
Learn how to make sense of spatial data and deal with various classes of statistical problems associated with it.
Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.
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.
In this course you'll learn how to create static and interactive dashboards using flexdashboard and shiny.
Analyze spatial data using the sf and raster packages.
Learn how to efficiently import data from the web into R.
In this course you'll learn about basic experimental design, a crucial part of any data analysis.
Advance you R finance skills to backtest, analyze, and optimize financial portfolios.
Learn how to access financial data from local files as well as from internet sources.
Learn the bag of words technique for text mining with R.
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 use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
This course is designed to get you up to speed with the most important and powerful methodologies in statistics.
Learn to easily summarize and manipulate lists using the purrr package.
In this course you will gain an overview clinical trial designs, determine the numbers of patients needed and conduct statistical analyses.
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 rbokeh: a visualization library for interactive web-based plots.
Learn how to visualize time series in R, then practice with a stock-picking case study.
Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
Create and share your own R Packages!
Learn to analyze, plot, and model multivariate data.
Learn how to analyze huge datasets using Apache Spark and R using the sparklyr package.
GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.