In this video, the instructor shows how to clean financial data to remove incorrect values and winsorize data.
- [Instructor] All right, now you've gathered your data,…you've put it all on a spreadsheet,…the question is what comes next.…Well gathering the data alone is not enough.…We need to clean it up.…I like to say that data are a lot like kids,…it often gets dirty.…Now what I mean by that is that…there's all kinds of problems that come up with data,…for example, you might have various errors in your data.…Transposition errors are very common for instance.…A transposition error simply means that…one column of data gets transposed with another.…
Perhaps stock price…has gotten mixed up with gross margins, for example.…We need to have a way to go through and clean that up…and remove those kinds of errors.…Data availability can also change.…One classic example of this…is SIC codes versus NAICS codes.…Now you might not be familiar with either of these,…but both types of codes represent the industry…that a particular firm is in.…SIC codes were used prior to the year 2000…and NAICS codes were used after 2000.…
Both of them represent the industry…
Join Professor Michael McDonald and discover how to use predictive analytics to forecast key performance indicators of interest, such as quarterly sales, projected cash flow, or even optimized product pricing. All you need is Microsoft Excel. Michael uses the built-in formulas, functions, and calculations to perform regression analysis, calculate confidence intervals, and stress test your results. You'll walk away from the course able to immediately begin creating forecasts for your own business needs.
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- List the two methods of making decisions.
- Identify the most common method of conventional financial forecasting.
- Describe common challenges that come when trying to merge data.
- Assess the types of questions that business intelligence is best suited to answer.
- Distinguish the statistic that is most useful for estimating the impact of an X variable on a Y variable.