Faculty of Science and Engineering Department of Computing
ITEC871: Information Systems Design and Management
Individual Assignment Number One “Watson Analytics”
Semester 2, 2018
Lecturer: Dr. Ian Krycer
Email: ian.krycer@mq.edu.au
Individual Assignment
DATE DUE:
Two Weeks, 5:00 pm on Week 4, 22/8/18
Please submit online through iLearn’s Turnitin API as a single PDF document (https://ilearn.mq.edu.au)
MARKS: 15% Contribution
Background
IBM’s Watson Analytics uses machine learning to Explore, Predict and Assemble large datasets. By doing your own unique analysis using the free Watson subscription service, you will explore the business potential for this SaaS.
Your Challenge
1. Sign up for the free version of Watson.
https://watson.analytics.ibmcloud.com/
2. Select a Dataset and Add to your Watson platform. Some Datasets are relatively simple, whilst the more complex ones allow for a richer analysis. [Don’t use the HR_Training file as this was used as the example in the
lecture.]
3. Study your Dataset in Excel. Understand the metadata (column headings) and any definitions given in Worksheet 2 in the file. Think about the related interesting business questions. Design THREE (3) questions to Explore. Provide a brief report on each question including your graphic and analysis. (Use the Share tool to download a png version of each graphic for clarity.)
4. Use Predict to study one variable in your Dataset. Report on the result including the Watson graphic.
5. Use Display to construct a useful Dashboard. Again export as a png file for report and briefly explain its design.
Your report should include the following: a) Cover sheet
(Make sure that you clearly identify which Dataset that you have chosen.) b) Three questions analysed with Explore
c) One variable analysed with Predict
d) One dashboard designed with Display
e) A conclusion with your views on how well Watson analysed your dataset.
Ensure that your report does not exceed 10 pages.
This assignment will contribute a maximum of 15% to the final assessment grade, and is a compulsory component of the course.
Marking Criteria
Students should provide the required components. Marks will be deducted for incomplete reports.
You will be marked according to the following criteria, with a final mark out of 100:
Element Excellent to Good
(80) Pass
(40) Incomplete
(20)
Demonstrates understanding or interpretation of key concepts of
Watson
Analytics
? Demonstrates high level of understanding of key concepts
? Skillfully organises description of issues and application of functions
? Provides an insightful analysis
? Provides a logical and complete report ? Demonstrates some
understanding of key concepts
? Organises description of issues and application of functions in some cases
? Provides a reasonable
analysis
? Provides a mostly complete report. ? Demonstrates little understanding of key concepts
? Little organisation used to describe issues; shows poor application of functions
? Includes an incomplete
analysis
? Provides an incomplete report.
Element Excellent to Good
(20) Pass
(10) Incomplete
(5)
Technical ? Clarity of expression, correct grammar, sentence structure, punctuation, spelling – excellent in all areas
? Used consistent and clear graphics and structure ? Clarity of expression, correct grammar, sentence structure, punctuation, spelling - some difficulties determining meaning, some errors
? Inconsistent structure and graphics that lack clarity ? Poor clarity of expression, grammar, sentence structure, punctuation, spelling – limited readability; requires many corrections/further explanation
? Very poor structure and reproduction of graphics