Assignment: Data Eexploration and Preparation

Introduction to Data Analytics

Introduction to Data Analytics

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 he/she could deliver to the client. Your tasks include:

• understanding the specifics of the data set
• extracting information about each of the attributes, possible associations between them and other specifics of the data set.

The tasks in the assignment are specified below.

Data sets

For this dataset you only have the attribute headings, no descriptions of what they mean. 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.

Tasks

1A. Initial data exploration

1. Identify the type of first 30 attributes {row ID, ......., foreign_worker_info_education} (nominal, ordinal, interval or ratio). If it's not clear you may need to justify why you choose the type.

2. Identify the values of the summarising properties for the first 30 attributes including frequency, location and spread (e.g. value ranges of the attributes, frequency of values, distributions, medians, means, variances, percentiles, etc. - the statistics that have been covered in the lectures and materials given). Note that not all of these summary statistics will make sense for all the attribute types, so use your judgement! Where necessary, use proper visualisations for the corresponding statistics.

3. Using KNIME or other tools, explore your data set and identify any outliers, clusters of similar instances, "interesting" attributes and specific values of those attributes. Note that you may need to 'temporarily' recode attributes to numeric or from numeric to nominal. In the report include the corresponding snapshots from the tools and explanation of what has been identified there.

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 employer_num_employees 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 employer_num_employees:

• 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 employer_num_employees attribute into the following categories: Startup=0-10; Small_Scale=11-100; Medium_Scale=101- 2000; Large_Scale=2001-20000, Giant_Scale=20001+, 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 foreign_worker_info_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.

1C. Summary

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.).