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1. Big Data is often described by the three V's, or

2. Which approach to Data Analytics attempts to assign each unit in a population into a small set of classes where the unit belongs?

3. Which approach to Data Analytics attempts to identify similar individuals based on data known about them?

4. Which approach to Data Analytics attempts to predict relationship between two data items?

5. Which of these terms is defined as being a central repository of descriptions for all of the data attributes of the dataset?

6. Which skills were not emphasized that analytic-minded accountants should have?

7. Which skills were not emphasized that analytic-minded accountants should have?

8. Consider the 2013 declined loan data from LendingClub titled "RejectStatsB2013" from the Connect Website. Similar to the analysis done in the chapter, let's scrub the risk score data. First, since our analysis requires risk scores, debt-to-income data and employment length, we need to make sure each of them has valid data.

Required:

After unselecting the observations with a zero or missing risk score, which group (Excellent, Very Good, Good, Fair, Poor, Bad) had the most observations? Which had the least?

9. Which DTI bucket had the highest and lowest grouping for this Rejected Loans dataset?

10. Consider the 2013 declined loan data from LendingClub titled "RejectStatsB2013". 

Similar to the analysis done in the chapter, let's analyze the employment length. Since our analysis requires risk scores, debt-to-income data and employment length, we need to make sure each of them has valid data.

1. Sort the file based on employment length, and unselect those observations (the complete row or record) those that have a missing score ("NA").
2. Sort the file based on debt-to-income, and unselect those observations (the complete row or record) that have a missing score or a negative score.
3. Sort the file based on risk score and unselect those observations (the complete row or record) that has a missing score.
4. T0here should now be 669,993 observations.
5. Run a PivotTable analysis to show the number of excellent risk scores but high debt-to-income (DTI) bucket loans in each employment year bucket.

Required:

After completing the steps above, answer the following questions by inputting the corresponding employment level.

11. In this lab, you will:

Part 1: Identify appropriate questions and develop a hypothesis for each question.

Part 2: Translate questions into target fields and value in a database.

Part 3: Perform a simple analysis.

Refer to Chapter 1 pages 28 through 30 for instructions and steps for each of the lab parts. After completing Lab parts 1 through 3, complete the following requirements.

Software needed

• Word processor
• Web browser
• Screen capture tool (Windows: Snipping Tool; Mac: Cmd+Shift+4)
   

Select an appropriate hypothesized answer to the following question: "How has Apple Inc's gross margin changed in the past three years?".

12. Match data elements from the previous question to XBRL tags that would provide data to answer the question from Part 1. If the data element requires a calculation, choose "Expression"

13. Select an appropriate hypothesized answer to the following question: "Are there geographic patterns or anomalies regarding customers who make late payments?".

14. Match data elements from the previous question to XBRL tags that would provide data to answer the question from Part 1. If the data element requires a calculation, choose "Expression"