In this introduction to R course, you'll master the basics of this widely used open source language&mdash;including vectors, factors, lists, and data frames. With the coding skills you'll gain in this course, you'll be ready to undertake your own data analysis in R. There are millions of R users worldwide, cementing it as a leading programming language in statistics and data science. More and more organizations are using R, so begin your coding journey in one of DataCamp's most popular courses today!

Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.

DataCamp's Into to R training course teaches you how to use R programming for data science at your own pace with video tutorials & interactive challenges.

Introduction to R Online

Introduction to R

Gain a 360° overview of exploring and using Power BI to build impactful reports. In this course, you’ll go from zero to hero as you discover how to use this popular business intelligence platform through hands-on exercises. Before diving into creating visualizations using Power BI's drag-and-drop functionality, you’ll first learn how to confidently load and transform data using Power Query and the importance of data models. You’ll also learn to drill down into reports and make your reports fully interactive. You've got the power!

Gain a 360° overview of how to explore and use Power BI to build impactful reports.

Learn how to use the Power BI platform to load and transform data, create visualzations, run DAX formulas, and create reports through hands-on exercises.

Introduction to Power BI

Intermediate R is the next stop on your journey in mastering the R programming language. In this R training, you will learn about conditional statements, loops, and functions to power your own R scripts. Next, make your R code more efficient and readable using the apply functions. Finally, the utilities chapter gets you up to speed with regular expressions in R, data structure manipulations, and times and dates. This course will allow you to take the next step in advancing your overall knowledge and capabilities while programming in R.

Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.

Discover conditional statements, loops, and functions to power your own R scripts, and learn to make your R code more efficient using the apply functions.

Intermediate R

Tableau is a widely used business intelligence (BI) and analytics software trusted by companies like Amazon, Experian, and Unilever to explore, visualize, and securely share data in the form of Workbooks and Dashboards. With its user-friendly drag-and-drop functionality it can be used by everyone to quickly clean, analyze, and visualize your team’s data.

You’ll learn how to navigate Tableau’s interface and connect and present data using easy-to-understand visualizations. By the end of this training, you’ll have the skills you need to confidently explore Tableau and build impactful data dashboards. Let’s dive in.

Get started with Tableau, a widely used business intelligence (BI) and analytics software to explore, visualize, and securely share data.

Learn to navigate Tableau’s interface, connect and present data, and build easy-to-understand visualizations to build impactful dashboards for business.

Introduction to Tableau

This is an introduction to the programming language R, focused on a powerful set of tools known as the Tidyverse. You'll learn the intertwined processes of data manipulation and visualization using the tools dplyr and ggplot2. You'll learn to manipulate data by filtering, sorting, and summarizing a real dataset of historical country data in order to answer exploratory questions. You'll then learn to turn this processed data into informative line plots, bar plots, histograms, and more with the ggplot2 package. You’ll get a taste of the value of exploratory data analysis and the power of Tidyverse tools. This is a suitable introduction for those who have no previous experience in R and are interested in performing data analysis.

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.

Discover the fundamentals of the Tidyverse, and learn all about renaming and reordering variables, while becoming familiar with binomial distribution.

Introduction to the Tidyverse

Say you've found a great dataset and would like to learn more about it. How can you start to answer the questions you have about the data? You can use dplyr to answer those questions—it can also help with basic transformations of your data. You'll also learn to aggregate your data and add, remove, or change the variables. Along the way, you'll explore a dataset containing information about counties in the United States. You'll finish the course by applying these tools to the babynames dataset to explore trends of baby names in the United States.

Learn to transform and manipulate your data using dplyr. 

Learn how to use dplyr to transform and aggregate data, then add, remove, or change variables. You'll then apply your skills to a real-world case study.

Data Manipulation with dplyr

Take your Tableau skills up a notch with advanced analytics and visualizations. In this course, you’ll learn how to create detail-rich map visualizations, configure date and time fields to show trends over time, and extend your data using Calculated Fields. You’ll also apply your new skills to complete a customer analytics case study. Through hands-on activities, you’ll learn how to create bins, customize filters and interactions, and apply quick table calculations. Finally, you’ll learn power user techniques, including how to slice and dice data and apply dynamic sets and groups—bringing you one step closer to being Tableau Desktop Specialist certification-ready.

Take your Tableau skills up a notch with advanced analytics and visualizations.

Analyzing Data in Tableau

Dashboards are a must-have tool in today’s data-driven world. By combining curated data visualizations, dashboards allow users to interpret and ask their own questions of data. By reducing the time to insight and empowering business analysts, dashboards can have a big impact on business performance.

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Tableau's dashboarding abilities are powerful and easy to use. In this course, you'll learn to apply dashboard-composition best practices, add interactive or explanatory elements, and use dashboard actions to make your dashboard interactive. Additionally, you'll learn to modify an existing dashboard layout for mobile devices to share as an image or a PDF. Finally, you'll learn how to share your data story through Tableau's story functionality.

Dashboards are a must-have in a data-driven world. Increase your impact on business performance with Tableau dashboards.

Creating Dashboards in Tableau

Great analysis begins with great data. But how do you connect Tableau to your databases and other file types, so that you can build a view and analyze your data? In this course, you'll learn how to use connectors in Tableau to create a live connection to CSV and Excel files. Through hands-on exercises, you’ll also learn how to combine multiple data tables with joins, unions, and relationships. Finally, you'll learn to manage different data properties, like renaming data fields, assigning aliases, changing data types, and changing default properties for a data field.

Learn to connect Tableau to different data sources and prepare the data for a smooth analysis.

Connecting Data in Tableau

If you've ever applied for a credit card or loan, you know that financial firms process your information before making a decision.  This is because giving you a loan can have a serious financial impact on their business.  But how do they make a decision?  In this course, you will learn how to prepare credit application data.  After that, you will apply machine learning and business rules to reduce risk and ensure profitability.  You will use two data sets that emulate real credit applications while focusing on business value. Join me and learn the expected value of credit risk modeling!

Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.

Credit Risk Modeling in Python

This course will teach you how to evaluate basic portfolio risk and returns like a quantitative analyst on Wall Street. This is the most critical step towards being able to fully automate your portfolio construction and management processes. Discover what factors are driving your portfolio returns, construct market-cap weighted equity portfolios, and learn how to forecast and hedge market risk via scenario generation.

Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.

Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation

Introduction to Portfolio Risk Management in Python

Managing risk using Quantitative Risk Management is a vital task across the banking, insurance, and asset management industries. It’s essential that financial risk analysts, regulators, and actuaries can quantitatively balance rewards against their exposure to risk.


This course introduces you to financial portfolio risk management through an examination of the 2007—2008 financial crisis and its effect on investment banks such as Goldman Sachs and J.P. Morgan. You’ll learn how to use Python to calculate and mitigate risk exposure using the Value at Risk and Conditional Value at Risk measures, estimate risk with techniques like Monte Carlo simulation, and use cutting-edge  technologies such as neural networks to conduct real time portfolio rebalancing.

Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python. 

Learn about risk management, value at risk and more applied to the 2008 financial crisis in Python. 

Quantitative Risk Management in Python

Volatility is an essential concept in finance, which is why GARCH models in Python are a popular choice for forecasting changes in variance, specifically when working with time-series data that are time-dependant. This course will show you how and when to implement GARCH models, how to specify model assumptions, and how to make volatility forecasts and evaluate model performance. Using real-world data, including historical Tesla stock prices, you’ll gain hands-on experience of how to better quantify portfolio risks, through calculations of Value-at-Risk, covariance, and stock Beta. You’ll also apply what you’ve learned to a wide range of assets, including stocks, indices, cryptocurrencies, and foreign exchange, preparing you to go forth and use GARCH models.

Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.

Learn about GARCH Models, how to implement them, and calibrate them on financial data. Apply what you learn to assets from stocks to foreign exchange.

GARCH Models in Python

In this track, you’ll learn how to use Tableau—one of the world’s most popular business intelligence tools. Best of all, there’s no prior experience required. With its user-friendly drag-and-drop functionality it can be used by everyone to quickly clean, analyze, and visualize your team’s data. Through hands-on exercises, you'll learn how to organize and analyze data, create presentation-ready visualizations, build insightful dashboards, and apply analytics to worksheets. You’ll then ramp up your skills by learning how to use data connectors to combine and prepare datasets and manage data properties. By the end of the track, you'll be ready to pass Tableau’s Desktop Specialist certification and start applying your Tableau skills for your own analyses.

Gain the essential skills you need to confidently use Tableau and pass the Tableau Desktop Specialist certification. No prior experience required.

Tableau Fundamentals

Enhance your Python financial skills and learn how to manipulate data and make better data-driven decisions.

You’ll begin this track by discovering how to evaluate portfolios, mitigate risk exposure, and use the Monte Carlo simulation to model probability. Next, you’ll learn how to rebalance a portfolio using neural networks. Through interactive coding exercises, you’ll use powerful libraries, including SciPy, statsmodels, scikit-learn, TensorFlow, Keras, and XGBoost, to examine and manage risk. You’ll then apply what you’ve learned to answer questions commonly faced by financial firms, such as whether or not to approve a loan or a credit card request, using machine learning and financial techniques. Along the way, you’ll also create GARCH models and get hands-on with real datasets that feature Microsoft stocks, historical foreign exchange rates, and cryptocurrency data. Start this track to advance your Python financial skills.

Enhance your Python financial skills. Learn how to evaluate portfolios, calculate credit risk, and create GARCH models to forecast volatility. 

in Python

Applied Finance

Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In our Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start DataCamp’s online Python curriculum now.

Master the basics of data analysis in Python. Expand your skillset by learning scientific computing with NumPy.

Discover how to use Python for data science, learning about the ways to store and manipulate data in the environment to begin conducting your own analyses.

Introduction to Python

The beauty of R is that it is built for performing data analysis. The downside is that sometimes R can be slow, thereby obstructing our analysis. For this reason, it is essential to become familiar with the main techniques for speeding up your analysis, so you can reduce computational time and get insights as quickly as possible.

Learn to write faster R code, discover benchmarking and profiling, and unlock the secrets of parallel programming.

Writing Efficient R Code

Dates and times are abundant in data and essential for answering questions that  start with when, how long, or how often.  However, they can be tricky, as they come in a variety of formats and can behave in unintuitive ways. This course teaches you the essentials of parsing, manipulating, and computing with  dates and times in R. By the end, you'll have mastered the lubridate package, a member of the tidyverse, specifically designed to handle dates and times.  You'll also have applied your new skills to explore how often R versions are released,  when the weather is good in Auckland (the birthplace of R), and how long monarchs ruled in Britain.

Learn the essentials of parsing, manipulating and computing with dates and times in R.

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