Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
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.
Learn survey design using common design structures followed by visualizing and analyzing survey results.
Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
This course will show you how to combine and merge datasets with data.table.
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Learn to effectively convey your data with an overview of common charts, alternative visualization types, and perception-driven style enhancements.
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.
In this course you'll learn to build dashboards using the shinydashboard package.
Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
Leverage the tools in the tidyverse to generate, explore and evaluate machine learning models.
Learn how to use plotly in R to create interactive data visualizations to enhance your data storytelling.
Create and share your own R Packages!
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Advance you R finance skills to backtest, analyze, and optimize financial portfolios.
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
Learn to analyze and model customer choice data in R.
Learn to easily summarize and manipulate lists using the purrr package.
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
Gain an overview of all the skills and tools needed to excel in Natural Language Processing in R.
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!
Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.
Work with risk-factor return series, study their empirical properties, and make estimates of value-at-risk.
Learn the bag of words technique for text mining with R.
Learn to use the Bioconductor package limma for differential gene expression analysis.
Practice your Shiny skills while building some fun Shiny apps for real-life scenarios!
Learn how to access financial data from local files as well as from internet sources.
Apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
Learn sentiment analysis by identifying positive and negative language, specific emotional intent and making compelling visualizations.
GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.
Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
Learn how to run big data analysis using Spark and the sparklyr package in R, and explore Spark MLIb in just 4 hours.
Learn the basics of cash flow valuation, work with human mortality data and build life insurance products in R.
In this course you'll learn how to create static and interactive dashboards using flexdashboard and shiny.
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 dimensionality reduction techniques in R and master feature selection and extraction for your own data and models.
In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.
Manipulate text data, analyze it and more by mastering regular expressions and string distances in R.
Learn to analyze, plot, and model multivariate data.
Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
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.
Learn to create interactive dashboards with R using the powerful shinydashboard package. Create dynamic and engaging visualizations for your audience.
Learn how to perform advanced dplyr transformations and incorporate dplyr and ggplot2 code in functions.
Strengthen your knowledge of the topics covered in Manipulating Time Series in R using real case study data.
Learn how to visualize big data in R using ggplot2 and trelliscopejs.