You are asked to explore the different types of data analytics, their objectives, the techniques, and appropriate steps for generating insights which today’s businesses are seeking to stay proactive and competitive. In addition, you will explore what ethical use of data analytics means.

MDA611 Predictive Analytics Assignment

Assessment Details and Submission Guidelines
Trimester	T1 2023
Unit Code	MDA611
Unit Title	Predictive Analytics
Assessment Type	Individual
Assessment Title	Assignment 1: Understanding Data Analytics – objectives, techniques, steps, and its ethical use
Purpose of the assessment (with ULO Mapping)	This assignment assesses the following Unit Learning Outcomes; students should be able to demonstrate their achievements in them. Evaluate and interpret data using an ethically responsible approach;Critically review the application and choice of industry standard data analytics tools.
Weight	10%
Total Marks	60 marks
Word limit	Part A – Max 1000 words, excluding references. (50 marks maximum) Part B – R code (10 marks maximum)
Due Date	End of Week 3
Submission Guidelines	For Assignment 1, submit as a Word document for Part A, and a markdown file of the R code for Part B.For Word document, the format includes 1.5 spacing, 11-pt Calibri (Body) font, and 2 cm margins on all four sides of your page with appropriate section headings.Reference sources must be cited in the text of the report and listed appropriately at the end in a reference list using IEEE referencing style.
Extension	If an extension of time to submit work is required, a Special Consideration Application must be submitted directly on AMS. You must submit this application three working days prior to the due date of the assignment. Further information is available at: https://www.mit.edu.au/about-us/governance/institute-rules-policies- and-plans/policies-procedures-and-guidelines/assessment-policy
Academic Misconduct	Academic Misconduct is a serious offence. Depending on the seriousness of the case, penalties can vary from a written warning or zero marks to exclusion from the course or rescinding the degree. Students should make themselves familiar with the full policy and procedure available at: https://www.mit.edu.au/about-mit/institute-publications/policies- procedures-and-guidelines/AcademicIntegrityPolicyAndProcedure. For further information, please refer to the Academic Integrity Section in the Unit Description.

Prepared by: Prof. Paul Kwan Moderated by: Dr. Osama Mahdi March 2023

Assignment 1

PART A [50 marks]

You are asked to explore the different types of data analytics, their objectives, the techniques, and appropriate steps for generating insights which today’s businesses are seeking to stay proactive and competitive. In addition, you will explore what ethical use of data analytics means.

Background and Tasks:

Data is becoming more readily available for the daily operations of businesses today. With data comes analytics, and to drive more efficient decision-making, companies must consider various analytic solutions to discover what will allow them to get the most out of their information.

Data analytics can be categorized into three main types – Descriptive Analytics, Predictive Analytics, and Prescriptive Analytics. While each may be considered separately, their interrelationship is equally crucial if a business desires to gain a fuller perspective of how their past and future are connected.

Discuss the conceptual framework that underpins each of the three types of data analytics. You should explore their respective objectives, associated techniques, compare industry relevant tools, data sources, and processing steps required to achieve the relevant objectives of a company. [7 x 3 marks]

Many organizations nowadays have included business data analytics as an integral part of their growth strategy. In this question, for each type of data analytics research one real-world example of a company that has clearly benefited from its application in their business strategy. You should identify the name of the company, the type of data analytics they have applied, and how they are using it to achieve their growth strategy. [5 x 3 marks]

Research the open literature to gain an understanding of what ethical use of data analytics means. Then answer the following questions: [5 x 2 marks]
- What does it mean by the ethical use of data analytics?
- Discuss how data can be collected in a transparent and fair manner.
- How might results of data analytics be misinterpreted or misused?
- What necessary measures should be taken to protect the privacy and security of data?
- Research one real-world example of unethical use of data analytics. How has this example impacted your own point of view of the data analytics profession?

You can use the Internet to locate resources to support your discussion and answers. However, all sources must be properly acknowledged and cited in your submission in either the IEEE (for MDA students) or APA (for MBAnalytics students) referencing style. [4 marks]

https://library.mit.edu.au/itreferencing

https://library.mit.edu.au/businessreferencing

PART B (Basics of R data manipulation) [10 marks]

Note: # is comment in R

# Download A1.csv from the same folder on Moodle as this Assignment 1 document. # Save it in the working directory of your R session.

# Write R commands to perform the following tasks:

# 1. read the file A1.csv into an R object.

# 2. using appropriate command(s) to display the class of the R object.

# 3. using appropriate command(s) to display the number of rows and columns of the R object. # 4. if the columns of the R object are named, print the names of its columns.

# 5. extract the entry of the R object in row 12 and column 2.

# 6. use the ‘$’ operator to return row 11 of the “weight” column.

# 7. use step 6’s result and a suitable function to return the number of records in the dataset.

# 8. View the R object and decide which rows have the value hf in “typeOfDiet” column. # 9. extract the values in the weight column of the rows returned by step 8.

# 10. Compute the mean of the values returned by step 9.

Marking criteria: The following marking criteria will be used.

Question Number	Description of the section	Marks
Part A
1)	Explain each type of data analytics. Your explanation should include the objectives, techniques, tools comparison, data collection, and processing steps.	21
2)	Identify one real-world example/company for each type of data analytics. Identify the	15
	name of the company, what type of data analytics they are using, and how they are
	using it to achieve their growth strategy.
3)	Conduct an Internet search to answer the list of five questions on the ethical use of data analytics.	10
4)	Include proper referencing.	4
Part B	Write R code to execute the ten (10) steps described.	10
	Total	60

Example Marking Rubric for Part A: Total Marks 50

Grade Mark	HD 27-30	DI 23-26	CR 19-22	P 15-18	Fail <15
	Excellent	Very Good	Good	Satisfactory	Unsatisfactory
1) Explain the concept of each type of data analytics	Concise and accurate explanation	Relevant and sound explanation	Generally relevant explanation	Some relevance explanation	Explanation are not relevant
2) Identify one Real-world Example /Company for each type of data analytics	Clear and Concise	Mostly clear and concise	Mostly clear and adequately concise	Mostly clearbut not concise	Not clear andnot concise
3) Conduct an Internet search to answer five questions about the ethical use of data analytics.	Concise and accurate explanation	Relevant and sound explanation	Generally relevant explanation	Some relevance explanation	Explanation are not relevant
4) Include proper referencing.	Clear and proper referencing	Mostly clear and proper referencing	Often clear and proper referencing	Some proper referencing	Lack proper referenc

You are asked to explore the different types of data analytics, their objectives, the techniques, and appropriate steps for generating insights which today’s businesses are seeking to stay proactive and competitive. In addition, you will explore what ethical use of data analytics means.

MDA611 Predictive Analytics Assignment

Assessment Details and Submission Guidelines
Trimester	T1 2023
Unit Code	MDA611
Unit Title	Predictive Analytics
Assessment Type	Individual
Assessment Title	Assignment 1: Understanding Data Analytics – objectives, techniques, steps, and its ethical use
Purpose of the assessment (with ULO Mapping)	This assignment assesses the following Unit Learning Outcomes; students should be able to demonstrate their achievements in them. Evaluate and interpret data using an ethically responsible approach;Critically review the application and choice of industry standard data analytics tools.
Weight	10%
Total Marks	60 marks
Word limit	Part A – Max 1000 words, excluding references. (50 marks maximum) Part B – R code (10 marks maximum)
Due Date	End of Week 3
Submission Guidelines	For Assignment 1, submit as a Word document for Part A, and a markdown file of the R code for Part B.For Word document, the format includes 1.5 spacing, 11-pt Calibri (Body) font, and 2 cm margins on all four sides of your page with appropriate section headings.Reference sources must be cited in the text of the report and listed appropriately at the end in a reference list using IEEE referencing style.
Extension	If an extension of time to submit work is required, a Special Consideration Application must be submitted directly on AMS. You must submit this application three working days prior to the due date of the assignment. Further information is available at: https://www.mit.edu.au/about-us/governance/institute-rules-policies- and-plans/policies-procedures-and-guidelines/assessment-policy
Academic Misconduct	Academic Misconduct is a serious offence. Depending on the seriousness of the case, penalties can vary from a written warning or zero marks to exclusion from the course or rescinding the degree. Students should make themselves familiar with the full policy and procedure available at: https://www.mit.edu.au/about-mit/institute-publications/policies- procedures-and-guidelines/AcademicIntegrityPolicyAndProcedure. For further information, please refer to the Academic Integrity Section in the Unit Description.

Prepared by: Prof. Paul Kwan Moderated by: Dr. Osama Mahdi March 2023

Assignment 1

PART A [50 marks]

Background and Tasks:

Discuss the conceptual framework that underpins each of the three types of data analytics. You should explore their respective objectives, associated techniques, compare industry relevant tools, data sources, and processing steps required to achieve the relevant objectives of a company. [7 x 3 marks]

Many organizations nowadays have included business data analytics as an integral part of their growth strategy. In this question, for each type of data analytics research one real-world example of a company that has clearly benefited from its application in their business strategy. You should identify the name of the company, the type of data analytics they have applied, and how they are using it to achieve their growth strategy. [5 x 3 marks]

Research the open literature to gain an understanding of what ethical use of data analytics means. Then answer the following questions: [5 x 2 marks]
- What does it mean by the ethical use of data analytics?
- Discuss how data can be collected in a transparent and fair manner.
- How might results of data analytics be misinterpreted or misused?
- What necessary measures should be taken to protect the privacy and security of data?
- Research one real-world example of unethical use of data analytics. How has this example impacted your own point of view of the data analytics profession?

https://library.mit.edu.au/itreferencing

https://library.mit.edu.au/businessreferencing

PART B (Basics of R data manipulation) [10 marks]

Note: # is comment in R

# Download A1.csv from the same folder on Moodle as this Assignment 1 document. # Save it in the working directory of your R session.

# Write R commands to perform the following tasks:

# 1. read the file A1.csv into an R object.

# 2. using appropriate command(s) to display the class of the R object.

# 3. using appropriate command(s) to display the number of rows and columns of the R object. # 4. if the columns of the R object are named, print the names of its columns.

# 5. extract the entry of the R object in row 12 and column 2.

# 6. use the ‘$’ operator to return row 11 of the “weight” column.

# 7. use step 6’s result and a suitable function to return the number of records in the dataset.

# 8. View the R object and decide which rows have the value hf in “typeOfDiet” column. # 9. extract the values in the weight column of the rows returned by step 8.

# 10. Compute the mean of the values returned by step 9.

Marking criteria: The following marking criteria will be used.

Question Number	Description of the section	Marks
Part A
1)	Explain each type of data analytics. Your explanation should include the objectives, techniques, tools comparison, data collection, and processing steps.	21
2)	Identify one real-world example/company for each type of data analytics. Identify the	15
	name of the company, what type of data analytics they are using, and how they are
	using it to achieve their growth strategy.
3)	Conduct an Internet search to answer the list of five questions on the ethical use of data analytics.	10
4)	Include proper referencing.	4
Part B	Write R code to execute the ten (10) steps described.	10
	Total	60

Example Marking Rubric for Part A: Total Marks 50

Grade Mark	HD 27-30	DI 23-26	CR 19-22	P 15-18	Fail <15
	Excellent	Very Good	Good	Satisfactory	Unsatisfactory
1) Explain the concept of each type of data analytics	Concise and accurate explanation	Relevant and sound explanation	Generally relevant explanation	Some relevance explanation	Explanation are not relevant
2) Identify one Real-world Example /Company for each type of data analytics	Clear and Concise	Mostly clear and concise	Mostly clear and adequately concise	Mostly clearbut not concise	Not clear andnot concise
3) Conduct an Internet search to answer five questions about the ethical use of data analytics.	Concise and accurate explanation	Relevant and sound explanation	Generally relevant explanation	Some relevance explanation	Explanation are not relevant
4) Include proper referencing.	Clear and proper referencing	Mostly clear and proper referencing	Often clear and proper referencing	Some proper referencing	Lack proper referenc