Master the basics of data analysis by manipulating common data structures such as vectors, matrices and data frames.
Continue your journey to become an R ninja by learning about conditional statements, loops, and vector functions.
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and data.table.
Learn to explore your data so you can properly clean and prepare it for analysis.
Learn to train and assess models performing common machine learning tasks such as classification, regression and clustering.
Master fundamental techniques for data manipulation using the select, mutate, filter, arrange, and summarise functions in dplyr.
Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.
Strengthen your knowledge of the topics you learned in Intermediate R with a ton of new and fun exercises.
This course provides a comprehensive introduction to working with base graphics in R.
Learn the fundamentals of writing functions in R so you can make your code more readable and automate repetitive tasks.
Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.
This course was designed to get you up to speed with the most important and powerful methodologies in statistics.
Learn how to parse data in any format. Whether it's flat files, statistical software, databases, or date right from the web.
This course provides a basic introduction to clustering and dimensionality reduction in R from a machine learning perspective.
Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.
Learn the language of data, study types, sampling strategies, and experimental design.
Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
Learn how to access financial data from local files as well as from internet sources.
Master core concepts in data manipulation such as subsetting, updating, indexing and joining your data using data.table.
This course teaches the big ideas in machine learning like how to build and evaluate predictive models.
This course will show you how to combine data sets with dplyr's two table verbs.
Use your 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.
In this series of four case studies, you'll revisit key concepts from our courses on importing and cleaning data in R.
Learn to create interactive analyses and automated reports with R Markdown.
Take your data visualization skills to the next level with coordinates, facets, themes, and general best practices in ggplot2.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
Learn to apply statistical modeling in a real-life setting using logistic regression and decision trees to model credit risk.
The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.
Learn about how dates work in R, and explore the world of if statements, loops, and functions. You'll practice this knowledge u...
Learn to read, explore, and manipulate spatial data then use your skills to create informative maps using R.
Learn the basics of the important features of the RStudio IDE.
This course covers some advanced topics including strategies for handling large data sets and specialty plots.
Learn the practice of drawing conclusions about a population from a sample of data, a process known as statistical inference.
This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.
Further your knowledge of RStudio and learn how to integrate Git, LaTeX, and Shiny
Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
Learn to create interactive graphs to display distributions, relationships, model fits, and more using ggvis.
Learn to use R to develop models to evaluate and analyze bonds as well as protect them from interest rate changes.
In this follow-up course, you will expand your stat modeling skills from part 1 and dive into more advanced concepts.
Manage the complexity in your code using object-oriented programming with the S3 and R6 systems.
Strengthen your knowledge of the topics covered in Manipulating Time Series in R with xts and zoo using real case study data.
Advance you R finance skills to backtest, analyze, and optimize financial portfolios.
Learn how to pull character strings apart, put them back together and use the stringr package.
Learn to visualize multivariate datasets using lattice plotting.