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Financial Analytics in R

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4 hours
4,750 XP
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Course Description

This course is an introduction to the world of finance where cash is king and time is money. In this course, you will learn how to use R to quantify the value of projects, opportunities, and actions and drive decision-making. Students will use the R language to explore cashflow statements, compute profitability metrics, apply decision rules, and compare alternatives. You will end this case-motivated course with an understanding of key financial concepts and the skills needed to conceptualize an communicate the value of you or your teams' projects in a corporate setting.
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  1. 1

    Cash is King (Intro to Valuations)

    Free

    Introducing the motivation for and basic concepts of discounted cashflow valuations analysis.

    Play Chapter Now
    Valuations Overview
    50 xp
    A first example: book publishing
    100 xp
    Business Models & Writing R Functions
    50 xp
    Premium music streamers
    100 xp
    Freemium music streamers
    100 xp
    Writing business models as functions
    100 xp
    Pro-Forma Income Statements
    50 xp
    Different depreciation methods
    100 xp
    Creating income statement
    100 xp
    From Income to Cash
    50 xp
    Jeff Bezos' income versus cash example (part 1)
    100 xp
    Jeff Bezos' income versus cash example (part 2)
    100 xp
  2. 2

    Time is Money (Key Financial Concepts)

    An overview of time-value of money and related concepts.

    Play Chapter Now
    The Time Value of Money
    50 xp
    Calculating PV
    100 xp
    Writing a PV function
    100 xp
    Visualizing PV by Time Delay
    100 xp
    Effect of Time on Discounting
    50 xp
    PV by Discount Rate and Time Delay
    100 xp
    Effect of Rate on Discounting
    50 xp
    Using Different Discount Rates
    50 xp
    Rates of Different Durations
    100 xp
    Real versus Nominal Rates
    100 xp
    Discounting Multiple Cashflows
    50 xp
    Selling a Car (Calculation)
    100 xp
    Selling a Car (Decision)
    50 xp
    Licensing a Software (Calculation)
    100 xp
    Licensing a Software (Decision)
    50 xp
  3. 3

    Prioritizing Profitability (Financial Metrics)

    Understanding different ways to summarize cashflow output.

    Play Chapter Now
    Profitability Metrics, Payback Period
    50 xp
    Calculate Payback Period
    100 xp
    Write Payback Period Function
    100 xp
    How would discounting change payback?
    100 xp
    NPV, IRR, and Profitability Index
    50 xp
    Write NPV Function
    100 xp
    Write IRR Function
    100 xp
    Applying the IRR Decision Rule
    50 xp
    Write Profitability Index Function
    100 xp
    Terminal value
    50 xp
    Perpetuity growth model
    100 xp
    Comparing & Computing Metrics
    50 xp
    Relationship between NPV and IRR
    100 xp
    Pitfalls of IRR
    100 xp
    NPV vs. IRR Decision Making
    100 xp
    Recap of Metrics
    50 xp
  4. 4

    Understanding Outcomes

    Piecing it altogether with sensitivty and scenario analysis.

    Play Chapter Now
    Building a Business Case
    50 xp
    Identifying incremental costs
    50 xp
    Cafe Model
    100 xp
    Scenario Analysis with tidyr and purrr
    50 xp
    Creating scenarios
    100 xp
    Conducting scenario analysis
    100 xp
    Visualizing scenario analysis
    100 xp
    Sensitivity analysis
    50 xp
    Calculating univariate sensitivities
    100 xp
    Plotting univariate sensitivity
    100 xp
    Communicating Cashflow Concepts
    50 xp
    Tidying a cashflow
    100 xp
    Untidying a cashflow
    100 xp
    Prepping waterfall data
    100 xp
    Creating a waterfall plot
    100 xp
    Advanced Topics in Cashflow Modeling
    50 xp
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

Collaborators

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David Campos
Collaborator's avatar
Shon Inouye
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Richie Cotton

Prerequisites

Introduction to RIntroduction to the Tidyverse
Emily Riederer HeadshotEmily Riederer

Analytics Manager

Emily Riederer is an Analytics Manager at Capital One. She is passionate about incorporating coding best practices and reproducible methods into standard business analysis workflows. Previously, she studied at the University of North Carolina at Chapel Hill where she worked in a healthcare operations research group. You can find her on Twitter at @emilyriederer.
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