This course is part of these tracks:

Lore Dirick
Lore Dirick

Data Scientist

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.

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  • Davis Vaughan

    Davis Vaughan

Course Description

Learning R can be intimidating, especially without concrete examples you might see in the real world. 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 directly to financial examples. By the end of the course, you will feel comfortable with the basics of manipulating your data to perform financial analysis in R.

  1. 1

    The Basics


    Welcome! Let's get comfortable with the very basics of R, and learn how to use it as a calculator. You will 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 as examples. After all of this, you will feel confident about 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 be in the form of. Combining the structure of a matrix with the flexibility of having different types of data in each column, you will soon see that the data frame is a powerful tool indeed! Good luck!

  4. Factors

    Are you a male or female? On a scale of 1-10, how are you feeling? These are questions with answers that fall into a limited number of categories. These types of data 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

    Think about your grocery list for a second. Apples, pizza, milk, and whatever else you might have on there. These are very different items right? Wouldn't it be nice if there was a way to hold related vectors, matrices, or data frames together in R? Enter, the list! In this final chapter, you will explore lists and many of their interesting features by building a small portfolio of stocks, and even come to realize that you have seen some of this already!