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 about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
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 streamline your machine learning workflows with tidymodels.
Learn to easily summarize and manipulate lists using the purrr package.
Learn to use essential bioconductor packages using datasets from virus, fungus, human and plants!
Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.
Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
In this course you'll learn how to leverage statistical techniques for working with categorical 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 the bag of words technique for text mining with R.
Learn how to visualize time series in R, then practice with a stock-picking case study.
Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!
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 techniques for performing statistical inference on numerical data.
Learn survey design using common design structures followed by visualizing and analyzing survey results.
Extract and visualize Twitter data, perform sentiment and network analysis, and map the geolocation of your tweets.
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Make it easy to visualize, explore, and impute missing data with naniar, a tidyverse friendly approach to missing data.
Learn how to produce interactive web maps with ease using leaflet.
In this course you'll learn to build dashboards using the shinydashboard package.
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.
Analyze spatial data using the sf and raster packages.
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
This course will introduce the support vector machine (SVM) using an intuitive, visual approach.
This course will show you how to combine and merge datasets with data.table.
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
Create and share your own R Packages!
In this course you'll learn how to create static and interactive dashboards using flexdashboard and shiny.
Continue learning with purrr to create robust, clean, and easy to maintain iterative code.
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.
Explore association rules in market basket analysis with R by analyzing retail data and creating movie recommendations.
Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.
Learn how to access financial data from local files as well as from internet sources.
Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.
Learn to use the Bioconductor package limma for differential gene expression analysis.
In this course you'll learn how to use data science for several common marketing tasks.
Learn how to manipulate, visualize, and perform statistical tests through a series of HR analytics case studies.
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.
Advance you R finance skills to backtest, analyze, and optimize financial portfolios.
Learn to detect fraud with analytics in R.
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
Learn defensive programming in R to make your code more robust.
Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.
Prepare for your upcoming machine learning interview by working through these practice questions that span across important topics in machine learning.