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DataCamp

Finance Fundamentals in R

via DataCamp

Overview

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Learn the finance and R fundamentals you need to make data-driven financial decisions. There’s no prior coding experience needed. In this track, you’ll learn about fundamental R concepts including vectors, matrices, lists, and functions, before discovering how to work with time series data to evaluate index performance. Throughout the track, you’ll work with popular R packages, including xts, zoo, tidyquant, PerformanceAnalytics, and PortfolioAnalytics, as you learn to import and manage financial data from different sources, including Excel files and from the web. Hands-on exercises will reinforce your new skills, as you work with real-world data, including Apple stock data, Microsoft, investment portfolios, and data from the S&P 500. By the end of the track, you'll be ready to navigate the world of finance using R—having learned how to work with investment portfolios, calculate measures of risk, and calculate an optimal portfolio based on risk and return.

Syllabus

  • Introduction to R for Finance
    • Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.
  • Intermediate R for Finance
    • Learn about how dates work in R, and explore the world of if statements, loops, and functions using financial examples.
  • Manipulating Time Series Data in R
    • Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.
  • Importing and Managing Financial Data in R
    • Learn how to access financial data from local files as well as from internet sources.
  • Introduction to Portfolio Analysis in R
    • Apply your finance and R skills to backtest, analyze, and optimize financial portfolios.
  • Intermediate Portfolio Analysis in R
    • Advance you R finance skills to backtest, analyze, and optimize financial portfolios.

Taught by

Kris Boudt, Joshua Ulrich, Ross Bennett, Lore Dirick, and Harrison Brown

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