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Financial Forecasting in Python

Step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast.

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Course Description

In Financial Forecasting in Python, you will step into the role of CFO and learn how to advise a board of directors on key metrics while building a financial forecast, the basics of income statements and balance sheets, and cleaning messy financial data. During the course, you will examine real-life datasets from Netflix, Tesla, and Ford, using the pandas package. Following the course, you will be able to calculate financial metrics, work with assumptions and variances, and build your own forecast in Python!
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  1. 1

    Income statements

    Free

    In this chapter, we will learn the basics of financial statements, with a specific focus on the income statement, which provides details on our sales, costs, and profits. We will learn how to calculate profitability metrics and finish off what we have learned by building our profit forecast for Tesla!

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    Introduction to financial statements
    50 xp
    Calculating gross profit
    100 xp
    Calculating net profit
    100 xp
    Elements within net profit & gross profit
    50 xp
    Calculating sales & Cost of Goods Sold (COGS)
    50 xp
    Calculating sales
    100 xp
    Forecasting sales with a discount
    100 xp
    Calculating COGS
    100 xp
    Calculating the break-even point
    100 xp
    Working with raw forecast datasets
    50 xp
    Tesla income statement
    100 xp
    Forecasting profit for Tesla
    100 xp
  2. 3

    Formatting raw data, managing dates and financial periods

    We have gotten a basic understanding of income statements and balance sheets. However, consolidating data for forecasting is complex, so in this chapter, we will look at some basic tools to help solve some of the complexities specifically relating to finance - working with dates and different financial periods, and formatting our raw data into the correct format for financial forecasting.

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  3. 4

    Assumptions and variances in forecasts

    In this chapter, we will be exploring two more aspects to creating a good forecast. First, we will look at assumptions, what drives them and what happens when an assumption changes? Next, we will look at variances, as a forecast is built at one point in time, but what happens when the actual results do not correspond to our forecast? We need to build a sensitive forecast that can be sensitive to changes in both assumptions and take into account variances, and this is what we will explore in this chapter.

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datasets

Ford Balance SheetNetflix ForecastTesla Income Statement

collaborators

Collaborator's avatar
Becca Robins
Collaborator's avatar
Sara Snell
Victoria Clark HeadshotVictoria Clark

Chartered Global Management Accountant at CIMA

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