Description**** IMPORTANT INFO ABOUT THE DELIVERY OF THE ASSIGNMENT ****
The timeline for this project and scope is as follows:
Deadline 1: Please provide me with a draft copy (approx. 1500 words) before *** 2021.
Deadline 2: The draft will be reviewed and I will provide feedback before we complete the final assignment.
Deadline3: Final assignment deadline:
******
Data Analytics
General Instructions – Please read carefully
You are required to complete the assignment outlined below and submit your completed final document through the Campus. Your grade will be based 100% on this final document, to which you will also receive written feedback.
In addition, you must upload part of your draft of the above document by the end of Unit 3 (see Interim Assignment, below). This draft will not be graded, but it is an important way of monitoring your progress. Formative feedback on your draft will be given, and general feedback with respect to the topic(s) covered in the interim assignment will be posted on the Forum after Unit 3 has been completed.
Your paper must have a clear structure and must include:
• Cover page (an example is available to you in Induction/Unit 4)
• Abstract (no more than 150 words, a single paragraph)
• Table of contents (Table of tables/figures if necessary) – numbered sections, page numbers
• A subsection for each of the four questions of the assessment – no more than 4’000 words in total
• A list of references – at the Master level you must use in-text citations to support your arguments and any work cited must appear in the References list at the end of the work – use peer reviewed sources.
Draft Submission: Around 1500 words paper, with a full structure for the final paper, and some content on each of the questions
(I will share feedback on the draft and then we will complete the final assignment)
Final Assignment – 4,000 words
For all questions below, you should either:
1. Use the CSV file provided (dataanalytics-class-data.csv)
Questions:
1. Conduct a literature review that critically discusses the concepts of
a. Data Science
b. Data Analytics
and the difference between the two concepts in academic and practitioner literature. Find and discuss relevant literature (peer-reviewed literature is preferred, such as journal articles, conference articles, with books, white papers, practitioner literature, and blog articles having a little less weight).
Weight: 20% of the final mark (roughly 1’000 words)
2. Using concepts discussed in the module, define your data analytics problem using elements and principles of data analytics (see Hicks and Peng (2019) for reference). You should be clear about the problem and the questions you are asking. You should also consider your audience explicitly.
Weight: 30% of the final mark (roughly 1’500 words)
3. Using best practices in storytelling with data, prepare a presentation of the findings of your analysis that could be used in a board room setting in order to lead to a decision on the original problem previously defined. This presentation can either be embedded as a PowerPoint document in the Word file you submit or be a (sub)section of the document itself.
Weight: 20% of the final mark (roughly 1’000 words, with use of graphics)
4. Consider the limitations of your analysis, and possible ways in which this could have been
improved had you had more data, time, etc.
Weight: 20% of the final mark (roughly 500 words)
The remaining 10% of the final mark will be dependent on the quality of Harvard referencing
and overall presentation professionalism of the paper.
