Build your ultimate AI agent
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!
Prerequisites
Curriculum
Course outline
1
Income statements
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!
- Introduction to financial statements50 XP
- Calculating gross profit100 XP
- Calculating net profit100 XP
- Elements within net profit & gross profit50 XP
- Calculating sales & Cost of Goods Sold (COGS)50 XP
- Calculating sales100 XP
- Forecasting sales with a discount100 XP
- Calculating COGS100 XP
- Calculating the break-even point100 XP
- Working with raw forecast datasets50 XP
- Tesla income statement100 XP
- Forecasting profit for Tesla100 XP
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.
Financial Forecasting in Python
Course
Complete
