Interactive Course

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.

  • 4 hours
  • 12 Videos
  • 49 Exercises
  • 2,166 Participants
  • 4,050 XP

Loved by learners at thousands of top companies:

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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!

  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!

  2. 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.

  3. Balance sheet and forecast ratios

    In this chapter, we will learn a bit more about the balance sheet, covering assets and liabilities and specific ratios to help evaluate the financial health and efficiency of a company, as well as how these ratios can assist us in building a great forecast.

  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.

  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!

  2. Balance sheet and forecast ratios

    In this chapter, we will learn a bit more about the balance sheet, covering assets and liabilities and specific ratios to help evaluate the financial health and efficiency of a company, as well as how these ratios can assist us in building a great forecast.

  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.

  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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Victoria Clark
Victoria Clark

Chartered Global Management Accountant at CIMA

I started my career in Financial Management and combined this with my interest in technology and automation. In this way, I have been exploring easy to use programming languages, especially Python, to help me with my day to day tasks. In the Financial world, there are huge synergies to be gained with programming languages such as Python that is unfortunately not leveraged as much as it could be. I hope to share my passion of teaching to these subjects, and join the worlds of Finance and Technology together.

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