Wk 1 DQ 2

/in /by

 

Wk1 DQ2

  1. Discussion Question 2 – Applied Concepts (AC) – Week/Course Learning Outcomes
     Using your textbook, LIRN-based research, and the Internet, apply the learning outcomes for the week/course and lecture concepts to one of the following scenarios:

    As applied to your current professional career
    As applied to enhancing, improving, or advancing your current professional career
    As applied to a management, leadership, or any decision-making position
    As applied to a current or future entrepreneurial endeavor
    OR
    Using your textbook, LIRN-based research, and the Internet, apply the learning outcomes for the week/course and lecture concepts to a business organization that exhibits and demonstrates these concepts. You should develop a summary of the organizations strategy and how they use these concepts to compete.
     This is a learning and application exercise designed to give you an opportunity to apply concepts learned in a pragmatic and meaningful way that will enable you to gain valuable and relevant knowledge in an effort to augment your skill set and enhance your professional careers.

case study 1

/in /by

 

Case Study 1

Read the attached article:

  • https://www.mckinsey.com/business-functions/risk/our-insights/enterprise-risk-management-practices-where-is-the-evidence

GUIDELINES FOR WRITING A CASE STUDY

A case study analysis requires you to investigate a problem, examine the alternative solutions, and propose the most effective solution using supporting evidence.  Your submission should be no more than 2 pages and needs to adhere to APA formatting for spacing and citations.  Include a title page, your case study (1-2 pages), and reference page.  For guidance on APA formatting check out this resource:  https://owl.english.purdue.edu/owl/section/2/10/

Preparing the Case

Before you begin writing, follow these guidelines to help you prepare and understand the case study:

  1. Read and examine the case thoroughly
    • Take notes, highlight relevant facts, underline key problems.
  2. Focus your analysis
    • Identify two to three key problems
    • Why do they exist?
    • How do they impact the information security field?
    • Who is responsible for them?
  3. Uncover possible solutions
    • Review course readings, discussions, outside research, and your experience.
  4. Select the best solution
    • Consider strong supporting evidence, pros, and cons: is this solution realistic?

Drafting the Case

Once you have gathered the necessary information, a draft of your analysis should include these sections:

  1. Introduction
    • Identify the key problems and issues in the case study.
    • Formulate and include a thesis statement, summarizing the outcome of your analysis in 12 sentences.
  2. Background
    • Set the scene: background information, relevant facts, and the most important issues.
  3. Alternatives
    • Outline possible alternatives (not necessarily all of them)
    • Why are alternatives not possible at this time (if not possible)?
  4. Proposed Solution
    • Provide one specific and realistic solution
    • Explain why this solution was chosen
    • Support this solution with solid evidence
  5. Recommendations
    • Determine and discuss specific strategies for accomplishing the proposed solution.
    • If applicable, recommend further action to resolve some of the issues
    • What should be done and who should do it?

Finalizing the Case

After you have composed the first draft of your case study analysis, read through it to check for any gaps or inconsistencies in content or structure: Is your thesis statement clear and direct? Have you provided solid evidence? Is any component from the analysis missing?

When you make the necessary revisions, proofread and edit your analysis before submitting the final draft.

INFOTECH DQ 1 (Week 1)

/in /by

 

Week 1 Discussion Forum: Business Strategy Options Menu: Forum

Why is it important for business strategy to drive organizational strategy and IS strategy? What might happen if the business strategy was not the driver?

Please make your initial post and two response posts substantive. A substantive post will do at least TWO of the following:

  • Ask an interesting, thoughtful question pertaining to the topic
  • Answer a question (in detail) posted by another student or the instructor
  • Provide extensive additional information on the topic
  • Explain, define, or analyze the topic in detail
  • Share an applicable personal experience
  • Provide an outside source (for example, an article from the UC Library) that applies to the topic, along with additional information about the topic or the source (please cite properly in APA)
  • Make an argument concerning the topic.

At least one scholarly source should be used in the initial discussion thread. Be sure to use information from your readings and other sources from the UC Library. Use proper citations and references in your post.

Data science assignment

/in /by

Identify all questions that you attempted in this template

Q1 Textbook Theory Questions http://faculty.marshall.usc.edu/gareth-james/ISL/

1. For each of parts (a) through (d), indicate whether we would generally expect the performance of a flexible statistical learning method to be better or worse than an inflexible method. Justify your answer.

(a) The sample size n is extremely large, and the number of predictors p is small.

(b) The number of predictors p is extremely large, and the number of observations n is small.

(c) The relationship between the predictors and response is highly non-linear.

(d) The variance of the error terms, i.e. 2 = Var(), is extremely high

5. What are the advantages and disadvantages of a very flexible (versus a less flexible) approach for regression or classification? Under what circumstances might a more flexible approach be preferred to a less flexible approach? When might a less flexible approach be preferred?

6. Describe the differences between a parametric and a non-parametric statistical learning approach. What are the advantages of a parametric approach to regression or classification (as opposed to a nonparametric approach)? What are its disadvantages?

Q2 Textbook Applied Questions Attempt with Python

8. Exploratory Data Analysis: College data set: College.csv. It contains a number of variables for 777 different universities and colleges in the US. Do all the exercises in Python:

8a. Read the csv file with pandas

8b.Fix the first row as row headers

8c.

  1. produce a numerical summary of the variables in the data set. 
  2. produce a scatterplot matrix of the first ten columns or variables of the data.
  3. produce side-by-side boxplots of Outstate versus Private
  4. Create a new qualitative variable, called Elite, by binning the Top10perc variable and divide universities into two groups based on whether or not the proportion of students coming from the top 10 % of their high school classes exceeds 50 %
  5. Produce some histograms with differing numbers of bins for a few of the quantitative variables: Room.Board’,’Books’, ‘Personal’, ‘Expend’
  6. Examine the elite schools more closely.

Q3 Textbook Applied Questions Attempt with Python

9. Exploration with Auto.csv data.

Make sure that the missing values have been removed from the data.

(a) Which of the predictors are quantitative, and which are qualitative?

(b) What is the range of each quantitative predictor?

(c) What is the mean and standard deviation of each quantitative predictor?

(d) Now remove the 10th through 85th observations. What is the range, mean, and standard deviation of each predictor in the subset of the data that remains?

(e) Using the full data set, investigate the predictors graphically, using scatterplots or other tools of your choice. Create some plots highlighting the relationships among the predictors. Comment on your findings.

(f) Suppose that we wish to predict gas mileage (mpg) on the basis of the other variables. Do your plots suggest that any of the other variables might be useful in predicting mpg? Justify your answer.

Q4 Textbook Applied Questions Attempt with Python

10. Exploration with Boston.csv data

a) How many rows and columns in the data set? What do the rows and columns represent?

(b) Make pairwise scatterplots of the predictors (columns) in this data set. Describe findings.

(c) Are any of the predictors associated with per capita crime rate? If so, explain relationship. (d) Do any of the suburbs of Boston appear to have particularly high crime rates? Tax rates? Pupil-teacher ratios? Comment on the range of each predictor.

(e) How many of the suburbs in this data set bound the Charles river?

 (f) What is the median pupil-teacher ratio among the towns in this data set?

(g) Which suburb of Boston has lowest median value of owner occupied homes?

What are the values of the other predictors for that suburb, and how do those values compare to the overall ranges for those predictors? Comment on your findings.

(h) In this data set, how many of the suburbs average more than seven rooms per dwelling? More than eight rooms per dwelling? Comment on the suburbs that average more than eight rooms per dwelling.

Hint several github sites have the complete solution in python e.g.

https://github.com/mscaudill/IntroStatLearn

https://botlnec.github.io/islp/

College.csv
Boston.csv
Auto.csv
HW02.docx