Privacy issues with data mining

Complete the following assignment as a single MS Word document. Only a Word document will be accepted.
Choose any two other following questions.

Chapter 1 – Discussion question #1 &
Exercise question #5 &
Exercise question #15 (limit to one page of analysis for question 15)

When submitting work, you MUST proceed each of your answers with a heading line that shows the Chapter # and the Question # you have chosen –  as an example, if you chose the 1st and 3rd question:

Chapter #1 – Discussion question #1:
your answer to discussion question #1…..

Chapter #1 – Exercise 15:
your answer to exercises question #15…

Be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.

All work must be original (not copied from any source).

Chapter Discussions

Initial post (goal: by Weds)

+ 2 responses (100-300 words).


Choose any 2 questions each week: Must provide a

heading to identify which questions were chosen.

Must be a WORD document with APA formatting.



CH #1 Compare and contrast predictive

analytics with prescriptive and

descriptive analytics. Use examples.

Discussion #1 (Applications)

Exercises #5 (

Exercise #15 (Watson in healthcare)


CH #2 Discuss the process that generates

the power of AI and discuss the

differences between machine

learning and deep learning.

Discussion #1 (Measuring machine intelligence)

Exercise #4 (5-part video)

Exercise #5 (today’s drivers of AI)

Exercise #15 (


CH #3 Why are the original/raw data not

readily usable by analytics tasks?

What are the main data pre-

processing steps? List and explain

their importance in analytics.

Discussion #1 (Analytics w/o data)

Discussion #2 (inputs & outputs)

Discussion #3 (data sources)

Discussion #4 (metrics)

Exercise #12 (


CH #4 What are the privacy issues with

data mining? Do you think they are


Discussion #1 (names & definitions)

Discussion #2 (recent popularity)

Discussion #3 (purchasing software)

Discussion #4 (distinguish data mining)

Discussion #5 (data mining methods)

Exercise #2 (teradata seminars)


CH #5 What is the relationship between

Naïve Bayes and Bayesian

networks? What is the process of

developing a Bayesian networks


Discussion #1 (ANN problems)

Discussion #2 (artificial vs biological NN)

Discussion #3 (ANN architectures)

Discussion #4 (supervised vs unsupervised)

Exercise 6 (scholar google machine-learning)

Internet Exercise #7 (


CH #6 List and briefly describe the nine-

step process in conducting a neural

network project.

Discussion #1 (what is deep learning)

Discussion #2 (learning paradigms/methods)

Discussion #3 (representation learning)

Discussion #4 (common ANN activation functions)

Discussion #5 (what is MLP)

Exercise #4 (cognitive computing cases)


CH #7 What are the common challenges

with which sentiment analysis deals?

What are the most popular

application areas for sentiment

analysis? Why?

Discussion #1 (data vs text mining vs sentiment-a)

Discussion #2 (define text mining)

Discussion #3 (induce structure – text-based data)

Discussion #4 (role of NLP)

Exercise #3 (Teradata eBay Analytics)

Internet Exercise # 7 (


Mid-Term Exam






Chapter Discussions

Initial post (goal: by Weds)

+ 2 responses (100-300 words).


Choose any 2 questions each week: Must provide a

heading to identify which questions were chosen.

Must be a WORD document with APA formatting.



CH #8 Excel is probably the most popular

spreadsheet software for PCs. Why?

What can we do with this package

that makes it so attractive for

modeling efforts?

Discussion #1 (prescriptive analytics relationship)

Discussion #2 (static vs dynamic models)

Discussion #3 (optimistic vs pessimistic approach)

Discussion #4 (problem solving under uncertainty)

Exercise #4 (war against terrorists)


CH #9 What are the common business

problems addressed by Big Data

analytics? In the era of Big Data, are

we about to witness the end of data

warehousing? Why?

Discussion #1 (what is Big Data)

Discussion #2 (future of Big Data)

Discussion #3 (Big Data analytics)

Discussion #4 (critical success factors)

Discussion #5 (big challenges)

Exercise #3 (Teradata – Sports analytics)


CH #10 There have been many books and

opinion pieces written about the

impact of AI on jobs and ideas for

societal responses to address the

issues. Two ideas were mentioned in

the chapter – UBI and SIS. What are

the pros and cons of these ideas?

How would these be implemented?

Discussion #1 (embracing robotics)

Discussion #2 (AI on future jobs)

Exercise #1 (use of Pepper)

Exercise #7 (self-driving cars)


CH #11 Explain how GDSS can increase

some benefits of collaboration and

decision making in groups and

eliminate or reduce some losses.

Discussion #1 (describe group work)

Discussion #2 (support from groupware)

Discussion #3 (groupware deployed)

Discussion #4 (physical meetings inefficient)

Exercise #4 (Simon’s 4-phase vs GDSS)


CH #12 Examine Alexa’s skill in ing

drinks from Starbucks.

Discussion #1 (chatbots inferior or not)

Discussion #2 (financial benefits of chatbots)

Discussion #3 (IBM Watson implications)

Exercise #1 (Facebook vs WeChat)

Exercise #12 (helping with dementia)

Exercise #16 (Singapore e-services)


Portfolio Project 11/29

CH #13 Research Apple Home Pod. How

does it interact with smart home

devices? Alexa is now connected to

smart home devices such as

thermostats and microwaves. Find

examples of other appliances that are

connected to Alexa and write a


Discussion #1 (IoT vs regular Internet)

Discussion #2 (autonomous vehicles)

Discussion #3 (smart home with bot)

Discussion #4 (IoT disruptive technology)

Exercise #3 (AT&T smart city projects)

Exercise #4 (IoT new customer services)

Exercise #6 (Sophia)


CH #14 None Discussion #1 (dehumanize managerial)

Discussion #3 (privacy concerns)

Discussion #4 (violation of user privacy)

Exercise #2 (empowerment, customization, etc.)


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