Interactive Course

Introduction to R for Finance

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

  • 4 hours
  • 14 Videos
  • 62 Exercises
  • 39,535 Participants
  • 5,300 XP

Loved by learners at thousands of top companies:

mls-grey.svg
3m-grey.svg
paypal-grey.svg
t-mobile-grey.svg
credit-suisse-grey.svg
forrester-grey.svg

Course Description

In this finance-oriented introduction to R, you will learn essential data structures such as lists and data frames and have the chance to apply that knowledge to real-world financial examples. By the end of the course, you will be comfortable with the basics of manipulating your data to perform financial analysis in R.

  1. 1

    The Basics

    Free

    Get comfortable with the very basics of R and learn how to use it as a calculator. Also, create your first variables in R and explore some of the base data types such as numerics and characters.

  2. Data Frames

    Arguably the most important data structure in R, the data frame is what most of your data will take the form of. It combines the structure of a matrix with the flexibility of having different types of data in each column.

  3. Lists

    Wouldn't it be nice if there was a way to hold related vectors, matrices, or data frames together in R? In this final chapter, you will explore lists and many of their interesting features by building a small portfolio of stocks.

  1. 1

    The Basics

    Free

    Get comfortable with the very basics of R and learn how to use it as a calculator. Also, create your first variables in R and explore some of the base data types such as numerics and characters.

  2. Vectors and Matrices

    In this chapter, you will learn all about vectors and matrices using historical stock prices for companies like Apple and IBM. You will then be able to feel confident creating, naming, manipulating, and selecting from vectors and matrices.

  3. Data Frames

    Arguably the most important data structure in R, the data frame is what most of your data will take the form of. It combines the structure of a matrix with the flexibility of having different types of data in each column.

  4. Factors

    Questions with answers that fall into a limited number of categories can be classified as factors. In this chapter, you will use bond credit ratings to learn all about creating, ordering, and subsetting factors.

  5. Lists

    Wouldn't it be nice if there was a way to hold related vectors, matrices, or data frames together in R? In this final chapter, you will explore lists and many of their interesting features by building a small portfolio of stocks.

What do other learners have to say?

Devon

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

Louis

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

Ronbowers

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA

Lore Dirick
Lore Dirick

Manager of Data Science Curriculum at Flatiron School

Lore is a data scientist with expertise in applied finance. She obtained her PhD in Business Economics and Statistics at KU Leuven, Belgium. During her PhD, she collaborated with several banks working on advanced methods for the analysis of credit risk data. Lore formerly worked as a Data Science Curriculum Lead at DataCamp, and is now the Manager of Data Science Curriculum at Flatiron School, a coding bootcamp in NYC.

See More
Icon Icon Icon professional info