32130 Fundamentals of Data Analytics
Assignment 2: data exploration and preparation
This assignment is individual work. Each of you will be working with an individual data set that you will be able to download from UTS Online. Scenario
You have just started working as a data miner/analyst in the Analytics Unit of a company. The Head of the Analytics Unit has brought you a data set [a welcome present ;-))]. The data set includes two files: description of the attributes and a table with the actual values of these attributes. The Head of the Analytics Unit has mentioned to you that this is some sort of demographic data that a potential client has provided for analysis. The Head of the Analytics Unit would like to have a report with some insights about that data, that she could deliver to the client. Your tasks include:
The tasks in the assignment are specified below.
The description of the attributes is the same for all students and comes in a tiny documentation file (download it from UTS Online). Each student is assigned an individual table with the actual values of these attributes. Please, download the file that is linked to your name from UTS Online.
1A. Initial data exploration
Present your findings in the assignment report.
1B. Data preprocessing
Perform each of the following data preparation tasks (each task applies to the original data) using your choice of tool:
a. Use the following binning techniques to smooth the values of the Age attribute:
• equi-width binning
• equi-depth binning.
In the assignment report for each of these techniques you need to illustrate your steps. In your Excel workbook file place the results in separate columns in the corresponding spreadsheet. Use your judgement in choosing the appropriate number of bins – and justify this in the report.
b. Use the following techniques to normalise the attribute Age:
• min-max normalization to transform the values onto the range [0.0-1.0].
• z-score normalization to transform the values. In the assignment report provide explanation about each of the
applied techniques. In your Excel workbook file place the results in
separate columns in the corresponding spreadsheet.
c. Discretise the Age attribute into the following categories: Teenager = 1-20; Young = 21-30; Mid_Age = 31-45; Mature = 46-65; Old = 66+. Provide the frequency of each category in your data set.
In the assignment report provide explanation about each of the applied techniques. In your Excel workbook file place the results in a separate column in the corresponding spreadsheet.
d. Binarise the Education variable [with values “0” or “1”]. In the assignment report provide explanation about the applied binarisation technique. In your Excel workbook file place the results in separate columns in the corresponding spreadsheet.
At the end of the report include a summary section in which you summarise your findings. The summary is not a narrative of what you have done, but a condensed informative section of what you have found about the data that you should report to the Head of the Analytics Unit. The summary may include the most important findings (specific
characteristics (or values) of some attributes, important information about the distributions, some clusters identified visually that you propose to examine, associations found that should be investigated more rigorously, etc.).
The deliveries include:
Report: In the report include a section (starting with a section title) for each of the tasks in this assignment.
Your report will likely be between 25-30 pages in length using an 11 or 12 point font, including title page and graphs. On average you will require between 15 and 23 hours to complete this assignment.
This assignment is assessed as individual work. The assessment criteria are:
Relationship to Objectives
This assignment addresses subject objectives 2 and 3.
Return of Assignments
We plan to return marked assignments within 3 weeks of submission. Emails will be sent when marking is complete.
All text in your assignment should be paraphrased into your own words and referenced using the Harvard referencing style. Please refer to the Subject Outline for details about penalties for Academic Misconduct.
A late penalty of up to 50% may be applied to submitted work unless prior arrangements have been made with the subject coordinator.
You may apply for special consideration (SC) due to unforeseen circumstances, either before or after the due date, at http://www.sau.uts.edu.au/assessment/consideration/online.html. The three basic reasons for SC are health, family, or work problems; “I haven’t finished yet” is not a valid reason. You must provide documentary evidence to support your claim, such as a doctor’s certificate, a statutory declaration, or a letter from your employer.
The assignments will be checked through the Turnitin ® Plagiarism Prevention system, for identifying unoriginal material, copied (without reference to the source) from an electronic source on the Internet, electronic libraries, other assignments.Order Now