Question descriptions

DATA 610 – Decision Management Systems Assignment №2 – Exploratory Data Analysis (EDA) using Watson Analytics

DATA 610 – Decision Management Systems

Assignment №2 – Exploratory Data Analysis (EDA) using Watson Analytics

Deadline: Last day of week 5, 11:59 pm Eastern Time

Submission via LEO.

This is an individual assignment. Each student will complete the assignment outlined below and post his/her written results to the appropriate assignment. Please note that only 1 document is allowed to be submitted. See content on p.2.

Grading criteria

Submitted assignments will be graded for (a) content, (b) document quality (i.e. formatting, following guidelines, pleasant to read, etc.), and timeliness of submission. Assignments submitted late will be deducted 5 points for each day it is late.


  1. Select from the datasetsprovided (or ones designated by your instructor). Provide a brief description of the dataset to include the number of cases, description of the inputs, description of the variables that could be used to develop predictive models, etc.


  1. Examine the dataset and eliminate mistakes, bad records, data entry errors, and outliers.

Using Watson Analytics:


  1. Explore the dataset, including:
    1. Examine the initial set of questions posed by Watson Analytics. Provide any insights gained from this initial dataset.
    2. Develop newspecific questions which provide additional insights into and answer specific questions from the dataset. Discuss how these insights could be useful.  Did Watson Analytics provide the answers necessary?  Discuss how you would improve the relevancy.
    3. Experiment with the available filters and visualization options and summarize the results. Create and explain at least one insightful global and one local filter for your dataset.
    4. Create and explain at least one insightful calculation. Discuss why this would be useful.
  2. Refine the dataset.
    1. Which variables have the highest quality score? Which ones have the lowest quality score and why?  Discuss how the quality of the dataset could be improved.
    2. Utilize the available grouping, filtering and hierarchical functionalities to refine the data. Summarize the approach you took and the outcome.  What suggestions or insights are gained?




Each student will submit a single document conforming to the guidelines and standards outlined below.


Document format:

  • limited to 5pages (excludingtitle page, references, and appendix),
  • Double-spaced, 12 point Times New Roman font, 1” margins, Bottom-right page numbering.

Note:   Submitted report must be either in MS Word or PDF format and titled: “Assignment2_LastName”.

Only one document will be allowed to be submitted.

Content(note that the document must have clearly marked sections for the items listed below)

  • Title page (1 page limit): course number and term, assignment number and project title, student name and contact information, instructor’s name. Format it so it looks pleasant and presentable. Follow formatting guidelines above.
  • Provide a brief outline of the dataset you are using for this assignment. Briefly explain the content of the data. Include a screenshot of the data (not all, but partial as far as all relevant variables are visible).
  • Discuss the data exploration process followed and the results. Include any specific ideas or suggestions as to how this could be used in your organization.
  • Discuss the data refinement process followed and the results.


  • References (1 page limit): List all references in APA format used in preparing this report. It is strongly recommended to use outside knowledge in setting-up the analysis or discussing the results where possible.


  • Appendix (4 page limit):
  1. Appendix A: Include any appropriateworkbooks and/or screenshots (figures, tables, diagrams) used in this assignment. Make sure all tables, figures, or diagrams are properly numbered and titled. For example, “Table 1. Model Results”. Make sure all tables or figures or diagrams are easily readable and visually presentable.



General guidance

  • Assignments that: 1) adequately address all required tasks; 2) are submitted on time; 3) are properly formatted (APA format for references, no typos or misspelled words, no grammar errors, cover page, etc.) will receive a grade of B (80-89, depending on content).
  • In order to increase (but not guarantee) your chances of receiving a higher grade, you need to show clear evidence of critical thinking. Critical thinking can take many forms, depending on the type of assignment. In some instances, showing greater depth (e.g., such as creating more models, looking at more than one insightful fact or relationships, and comparing them on key criteria) is one method for providing evidence of critical thinking. In other cases, it might include providing more explanation to include the pros and cons of the approach used or the arguments in favor and against the proposal as well as some criteria for choosing among the alternatives. Still another example would be providing significant insights as to how the assignment outcome would benefit (or would meet resistance) in your organization and what steps might be employed to facilitate acceptance. Certainly, this is not a complete list, but gives some examples of critical thinking aspects.




DATA 610 – Decision Management Systems


Student Name:                                               Assignment №2 – EDA using Watson Analytics                            Total points: 100

Content Explanation Points Comment
Total Earned
Written Report        
a.       Introduction Is the dataset fully described and outlined?Is the dataset a robust (i.e., lots of cases and inputs) selection?  Is the intent of the assignment discussed at an appropriate level of detail?  Are any initial insights provided? 10


b.      Dataset cleansing Is the dataset fully described and cleansed of outliers (as appropriate), mistakes and erroneous entries? Is rationale provided for any cleansing to the data set? 5


c.       Data exploration a)      Are the initial questions discussed and their relevance analyzed?  Are insights from the questions provided? 15


b)      How well additional specific questions developed and discussed?  Are the answers provided by Watson Analytics insightful and do they provide specific answers? 20


c)      Are the available filters and visualization options utilized and explained?  Are insights provided from using the filters and visualization options? 15


a.       Data refinement a)      Are quality scores compared and explained?  Are suggestions provided to improve the scores? 15


b)      Are the grouping, filtering, and hierarchical features fully explored and summarized?  Are suggestions for improving the outcomes provided? 15


b.      Mechanics (spelling, grammar) Is the paper free of grammatical errors and spelling and punctuation?  Is the paper properly formatted? 3


c.       Citations and References Are all references and citations correctly written and presented? 2


Total   100    
d)     Less        
1.      Formatting Does the submission follow formatting guidelines? – 5    
2.      Page limit Is the submission written within specified page limits? – 5    
3.      Late submission (less) 5 points will be deducted for each day the assignment is late. -5 each day    
Final Grade for Assignment 2      


DATA 610 – Decision Management Systems Assignment №2 – Exploratory Data Analysis (EDA) using Watson Analytics
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