statement is true?
a. Customer lifetime value estimation is an example of classification.
b. Demand estimation is an example of classification.
c. Customer churn prediction concerns regression.
d. Detecting fraudulent credit-card transactions concerns classification.
Question 4
Which is not a characteristic of a good data scientist? A good data scientist:
a. Has a solid business understanding.
b. Is creative.
c. Has thorough knowledge on legal aspects of applying analytics.
d. Excels in communication and visualization of results.
Question 5
Which statement is true?
a. All analytical models are profit-driven when applied in a business setting.
b. Only predictive analytics are profit-driven, whereas descriptive analytics are not.
c. There is a difference between analyzing data for the purpose of explaining or predicting.
d. Descriptive analytics aims to explain what is observed, whereas predictive analytics aims to predict as accurately as possible.
Question 1
Discuss the difference between a statistical perspective and a business perspective toward analytics.
Question 2
Discuss the difference between modeling to explain and to predict.
Question 3
List and discuss the key characteristics of an analytical model.
Question 4
List and discuss the ideal characteristics and skills of a data scientist.
Question 5
Draw the analytics process model and briefly discuss the subsequent steps.
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CHAPTER 2
Analytical Techniques
INTRODUCTION
Data are everywhere. IBM projects that every day we generate 2.5 quintillion bytes of data. In relative terms, this means 90 % of the data in the world has been created in the last two years. These massive amounts of data yield an unprecedented treasure of internal knowledge, ready to be analyzed using state-of-the-art analytical techniques to better understand and exploit behavior about, for example, your customers or employees by identifying new business opportunities together with new strategies. In this chapter, we zoom into analytical techniques. As such, the chapter provides the backbone for all other subsequent chapters. We build on the analytics process model reviewed in the introductory chapter to structure the discussions in this chapter and start by highlighting a number of key activities that take place during data preprocessing. Next, the data analysis stage is elaborated. We turn our attention to predictive analytics and discuss linear regression, logistic regression, decision trees, neural networks, and random forests. A subsequent section elaborates on descriptive analytics such as association rules, sequence rules and clustering. Survival analysis techniques are also discussed, where the aim is to predict the timing of events instead of only event occurrence. The chapter concludes by
