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Rafał Alitojć has completed

Introduction to R for Finance

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4 hours
5,300 XP
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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.
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  1. 1

    The Basics


    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.

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    Welcome to Introduction to R for Finance!
    50 xp
    Your first R script
    100 xp
    Arithmetic in R (1)
    100 xp
    Arithmetic in R (2)
    50 xp
    Assignment and variables (1)
    100 xp
    Assignment and variables (2)
    100 xp
    Financial returns
    50 xp
    Financial returns (1)
    100 xp
    Financial returns (2)
    100 xp
    Basic data types
    50 xp
    Data type exploration
    100 xp
    What's that data type?
    50 xp
  2. 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.

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  3. 4


    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.

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  4. 5


    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.

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In the following tracks

Finance FundamentalsQuantitative Analyst


Collaborator's avatar
Davis Vaughan
Lore Dirick HeadshotLore Dirick

Director of Data Science Education 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 and is now Director of Data Science Education at Flatiron School, a coding school with branches in 8 cities and online programs.
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