All posts by : chebby

Discussion: Examine Alexa’s skill in ordering drinks from Starbucks. 

Discussion Questions: 

1. Some people say that chatbots are inferior for chatting. Others disagree. Discuss. 

2. Discuss the financial benefits of chatbots. 

3. Discuss how IBM Watson will reach 1 billion people by 2018 and what the implications of that are. 

Exercises: 

1.  Compare the chatbots of Facebook and WeChat. Which has more functionalities? 

2.  Research the role of chatbots in helping patients with dementia. 

3.  Microsoft partners with the government of Singapore to develop chatbots for e-services. Find out how this is done. 

Discussion

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

Reply post

Why are statistical programming languages important to data scientists? What are some advantages and disadvantages the R programming language has over the other main statistical programming languages (i.e. Python, SAS, SQL)?

When replying to a classmate, offer your opinion on what they posted comparing the R programming language to the other statistical programming languages. Using at least 3 – 5 sentences, explain why you agreed or disagreed with their evaluation of the different statistical programming languages.

• 8-10 pages in length 
• APA formatted 
• Minimum six (6) sources – at least two (2) from peer reviewed journals 
• Include an abstract, introduction, and conclusion 

For the following program: 

1) Create an algorithm called exemptAlgorithm.txt 

2) Create the source code called exempt.py

3) Upload your algorithm and source code

Exempt

You will be writing a program to determine whether or not a student is exempt from the final exam 

Your program should do the following:

• Prompt the user for a student’s average and number of days missed.  

 Input Validation:  

• Average must be between 0 and 100 

• Number of days missed cannot be less than 0.      

Be sure to validate each value separately! 

•  Use the following conditions to display a message indicating whether or not a student is exempt from the final exam.  If exempt, indicate why. 

o  Average is at least 96

o  Average is at least 93 and days missed are less than 3

o  Average is at least 90 and student has perfect attendance 

Grading Rubric:

Algorithm (5)   ______ 

Intro (1)    ______ 

Variables (1)   ______ 

     • Declaration 

     • Initialization 

Input Validation (3)  ______ 

Selection Structure (10) ______ 

wk 7team activity

 The CEO is very concerned that the company will get dragged through the media if there is a public announcement of the company systems being hacked. A competitor has recently experienced this issue and has had a 50% reduction in revenue. Your team has been requested to detail a response plan. The response plan should be in the form of a table: an action, a responsible party, and the goal of the action.  Utilize your Incident Response Plan from Week 2 to address this event. 

 Do you believe Artificial Intelligence or Machine Learning is the future of cybersecurity? Explain why or why not. 

 This assignment should be in APA format and have to include at least two references.

1. When should the architect begin the analysis?
2. What are the activities the architect must execute?
3. What is the set of knowledge domains applied to the analysis?
4. What are the tips and tricks that make security architecture risk assessment easier?

Assignment:
Provide a reflection of at least 500 words (or 3 pages double spaced) of how the knowledge, skills, or theories of this course have been applied, or could be applied, in a practical manner to your current work environment. If you are not currently working, share times when you have or could observe these theories and knowledge could be applied to an employment opportunity in your field of study.

Requirements:

Provide a  word (or 3 pages double spaced) minimum reflection.

Use of proper APA formatting and citations. If supporting evidence from outside resources is used those must be properly cited.

Share a personal connection that identifies specific knowledge and theories from this course.

Demonstrate a connection to your current work environment. If you are not employed, demonstrate a connection to your desired work environment.

You should NOT, provide an overview of the assignments assigned in the course. This assignment asks that you reflect how the knowledge and skills obtained through meeting course objectives were applied or could be applied in the workplace

if you are not an expert on the topic don’t bid.

further guidelines will be provided on chat.

see file attached.

Discussion 1 (Chapter 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 2 (Chapter 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?

Your response should be 250-300 words.  Respond to two postings provided by your classmates.

1.How does prescriptive analytics relate to descriptive and predictive analytics?

2. Explain the differences between static and dynamic models. How can one evolve into the other?

3. What is the difference between an optimistic approach and a pessimistic approach to decision making under assumed uncertainty?

4. Explain why solving problems under uncertainty sometimes involves assuming that the problem is to be solved under conditions of risk.

Exercise 

Investigate via a Web search how models and their solutions are used by the U.S. Department of Homeland Security in the “war against terrorism.” Also investigate how other governments or government agencies are using models in their missions.

1.What is Big Data? Why is it important? Where does Big Data come from?

2. What do you think the future of Big Data will be? Will it lose its popularity to something else? If so, what will it be?

3. What is Big Data analytics? How does it differ from regular analytics?

4. What are the critical success factors for Big Data analytics?

5. What are the big challenges that one should be mindful of when considering implementation of Big Data analytics?

Exercise: 

At teradatauniversitynetwork.com, go to the Sports Analytics page. Find applications of Big Data in sports. Summarize your findings.