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You roll a trick 6-sided die twice. The trick is that the die has the same number on all sides.

INSTRUCTIONS TO CANDIDATES
ANSWER ALL QUESTIONS

1.

Please express your opinion on the following case: Your CEO recently became aware that your company has an extensive dataset of past online customer pur- chases and would like the company to develop an online product recommenda- tion system. The CEO asked the data science team leader to present a detailed work plan including resources (employees, computer resources) and a very accu- rate timetable with the expected required duration of each activity until a work-

ing system is operational.

 

2.

You roll a trick 6-sided die twice. The trick is that the die has the same number on all sides. What is the conditional probability that the sum of the numbers that come up on the two rolls will be greater than seven, given that the first roll is 5?

 

3.

Explain why Naive Bayes is naive.

 

4.

Explain the meaning of each of the different terms in Bayes Rule. Describe one way that this rule is used for data mining.

 

5.

We would like to target some subset of the vast number of visitors to our main retail web page with a new special offer. Instead of the typical early May special

Offer of a discounted flower bouquet for Mom, we’ve decided to offer selected customers A 30% discount on any electric razor purchase from our stock. We

need to determine which customers will get the normal special offer and which will get the new special offer. Show how computing expected value provides a framework for thinking about what models need to be built for this problem.

 

6.

To apply data mining to text documents, we need to represent text documents by some term (word) based features/attributes. Please describe three common ap- proaches to creating attributes for text documents. Please give a clear explanation of all your terminologies.

 

7. One essential part of the data mining process is creating attributes to describe

 

examples. To represent documents (such as emails) as examples, we create term (e.g., word) based attributes to describe the documents. Which of the following

is not a common approach? (a)

whether or not the term appears in the document (binary attribute)

 

(b)

term frequency (number of times term appears in document)

(c)

term frequency/total number of terms in the document

(d)

term frequency times the term’s frequency in the document corpus

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