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Tableau Assignment Help | Sample Paper



Dataset - Supermarket

(size: 1000 < x < 10000)




Software Tools: Tableau


Requirements:

You are required to analyse a large data set of your choice, which has been agreed with your module tutor:

Your project may use any combination of data analysis techniques, data-mining algorithms and software that has been covered in the module. You may also apply them to any aspect(s) of the dataset for knowledge discovery.


❖ Data Analysis and Visualisation

⮚ Initial analysis of the data using visualisation techniques within Tableau (use diagrams/graphs to highlight important patterns/findings).

⮚ Discussion and interpretation of result.

⮚ Discussion of overall trends and patterns observed.


❖ Selection of Data Mining Algorithm

⮚ Select one data mining algorithm suitable for further analysis of your data.

⮚ Clearly justify your choice, with reference to the visualisation analysis carried out.


❖ Data Pre-processing

⮚ Identify your input and class variables, if relevant (i.e. which variable are you going to consider for your class variables).


Data Set Features:


Invoice ID

Branch

City

Customer type

Gender

Product line

Unit price

Quantity

Tax 5%

Total

Date

Time

Payment

cogs

gross margin percentage

gross income

Rating


Identify and resolve any anomalies in the data (i.e.missing values, outliers etc.).


Data Anomalies identification / rectification

  1. Missing value (Blank)

  2. Value not in allowable range (range is “Male”, “Female”), the value is now “M”

  3. Value outside outlier (usually, a value higher/lower than 1.5 x IQR)

    1. Upper outlier = + Stddev * 1.5

    2. Lower outlier = - Stddev * 1.5


⮚ Carry out any appropriate pre-processing/transformations to the data set.


Outlier Detection



Example here:














1st Quartile = Quartile (A2:A11,1) = 1

3rd Quartile = Quartile (A2:A11,3) = 14.25

Lower Quartile = 11 - 1.5 * (14.25 - 11) = 6.125

Upper Quartile = 14.25 + 1.5 * (14.25 - 11) = 19.125


So outliers are

3, 99.
















❖ Data Mining

⮚ Use the chosen data mining algorithm for further analysis of your pre-processed data set.

⮚ Clearly discuss the implementation of the data mining algorithm.

⮚ Discuss and interpret the results.


❖ Data Ethics

⮚ A discussion of data ethical issues related to the analysis and use of business data.

  • 240 words

  • What is / are the concerns?

● Data privacy?

♦ What is data privacy?

♦ What data under your case need concern?

♦ Ethical concern of data privacy

⮚ Discrimination

⮚ Retention

⮚ Transparency

♦ Legal concern

⮚ Compliance of GDPR requirements

⮚ Seven Principles of GDPR

⮚ Professional Concern ( Standards / practices to follow)

▪ Digital analytics association (https://www.digitalanalyticsassociation.org/) Provides training / certification services to public to enhance personal data security.

▪ Data Science Code of Professional Conduct: http://www.datascienceassn.org/code-of-conduct.html).

▪ Privacy & data protection by design

● The functional requirement of systems should allow users customise:

♦ Privacy settings

♦ Levels of acceptable recording, monitoring and tracking

♦ Levels of security


● FOSS and open algorithm

▪ Data ethics framework

* Seven principles



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