Master of Data Analytics Major in Software Engineering Assignment-2 (MDA512) Assignment Help

Assessment Details and Submission 

Guidelines

Course Name 

Master of Data Analytics (MDA) 

Master of Data Analytics Major in Software Engineering

Unit Code 

MDA512

Unit Title 

Data Science

Term, Year 

T1, 2024

Assessment 

Type

Assignment 2 (Group Assessment)

Assessment 

Title

Develop a business model for efficient operations – based on selected business case study

Purpose of the assessment 

(with 

UL 

O Mapping)

The purpose of this assignment is to introduce students to some of the most important  tools in data analysis available in Python, as well as model design, data wrangling and to gain understanding of model debugging schemes.  

On completion of this assignment students will be expected to be able to: c. Analyse, transform and propose business visualisation solutions on structured  and unstructured data. 

d. Model and evaluate real-time decision data models using data analytics tools.

Weight 

25% of the total assessment for the unit

Total Marks 

60 marks

Word limit 

1500 -2000 words

Due Date 

This assignment is due by the end of Week 11 (Sunday, 2nd June 2024, 23:59)

Submission 

Guidelines

Submit 3 files, including:  

1. Word document “MDA512_Assigment2_Your_name.doc” with the URL of  2. Recorded video presenting a Summary of your project. 

3. Python program 

• The assignment must be in MS Word format, 1.5 spacing, 11-pt Calibri (Body) font and 2.5cm 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 theend 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 to the School’s Administration Officer online on AMS  . You must submit this application three working days prior to the due date of the assignment. Further information is available at:

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. For further information, please refer to the Academic Integrity  Section in your Unit Description.


ASSIGNMENT 2 DESCRIPTION (Total 60 MARKS) 

This is a group assignment.  

Your lab tutor will create groups and inform you of the details of your group members. This assignment has the  two Parts:  

Part A: Prepare your project report according to the section details given below. Discuss the algorithms applied and  the steps you followed to analyse the dataset and the interpretation of your results.  

Part B: Oral presentation of your project. 

You are required to upload 

1. Word Document with answer to section 1-5 – Project Report 

2. Python code download as pdf / HTML and upload 

3. If you want, you can add Jupiter notebook as an additional document. 

PART A – REPORT (45 MARKS) 

For part A, you will develop a project report on the data science problem that your team will work on.  This allows you to investigate your data science problem in detail. You are required to analyse the  selected data set to find the answers to the research questions.  

You can use advanced algorithms to help you answer your research questions, the focus on the final  project should not be solely on the algorithms themselves but should be grounded in some practical  question you want to understand from the data itself.  

This report should divided into the following sections, namely: Problem Statement, Resources,  Business Model, Data Analysis, and Results, as listed below. 

1. Problem Statement and Background: A high-level statement of the problem or business case study  you intend to address. Give a clear and complete statement of the problem and its background. 

2. Resources: Where do the data come from, and what are their characteristics? 

a. The data source(s) and  

b. Characteristics of the data you used (e.g. attributes, data types) 

3. Business Model:  Any business can succeed by analysing and making informed decisions from the use of data and its analytical capabilities. It will be a great benefit to the company if they can make quick and  increasingly complex decisions to cater for the changing demands from customers and evolving market  conditions. You are required to design a business model for more efficient operations which can  provide significant benefits (e.g., increased profitability and improved customer service) to the  selected business. 

 

Section 3 of the report should include the business model as discussed below. You need to describe your business model with respect to the points listed below. 

a. Research questions: The first thing you need to do before you plan to solve a problem is to define exactly what it is. You can translate data case scenarios into a question that you can find the answer  to, by analyzing the data set.

b. Challenges in the selected project (when acquiring data, or during the analysis process) 

c. How did you pre-process the data for data analytics –  

• Selected tools and methodologies in data wrangling for your selected case. 

d. How did you use the data set/s to address the business requirements / research question  at hand? E.g. increased profitability, improved customer service, etc.  

e. Specify the methodologies/ algorithms used in the project:  

Describe the algorithm and how did you identify the most appropriate algorithm . 

methodology for the project – with respect to the concepts you learned in class. 

Brief description of the Python libraries and commands used to implement the algorithm. 

4. Data Analysis:  

a. Description of the goals of your analysis, ideally in the form of testable hypothesis, or via well defined success metrics. 

b. A brief description of the data analytics tools/ Python libraries you used for the project.  

c. Provides details of analysis or visuali sations and describe why you chose those methods or visualisations and discuss the high-level business understanding of data analytic outcomes. 

5. Results:  

In this section, you are required to interpret the results of your analyses; ideally including  visualizations andreport the outcomes of your research study clearly in this section. You need to upload the Python program code that details the data uploading, analysis and results mentioned in your main report and attach the complete programming code to your  

report as an Appendix.  

You also need to provide sufficient evidence of evaluation and analysis comparing appropriate data analytic tools and methodologies. You can include important Python codes fragments  

here (part of the main code) to discuss your results.

 

NOTE:

Organise this section of the report with proper sub-headings is completely at your discretion. 

 

                           B – PRESENTATION (15 MARKS)  

a. Assignment 2 Presentation – In addition to your written report, you should demonstrate your work to your instructor as a team in week 11 or 12. Prepare not more than 7 slides to describe your project. 

This will allow you to demonstrate your understanding and skills to your instructor. You are evaluated  on the quality of your ability to perform specific tasks and the products you have created in the  process for Assignments 2. Your final mark for the assignment will be calculated based on this evaluation.

 

Marking criteria: 

Marks are allocated as follows: 

Note: The marking criteria varies for each assignment

 

 

Section to be included in the report

Description of the Criteria 

Marks

Part  

A

1. Problem Statement  and Background:

Give a clear and complete statement of the  problem and its background

3

2. Resources: 

Where do the data come from, what are their characteristics? 

• The source(s) reference and  

characteristics of your data

2

Part B 

3. Business Model 

You need to describe your business model with respect to the following points: 

• Research questions – problem you  intend to address. 

• Challenges in the selected project • How did you pre-process the data? • selected tools and methodologies in  data wrangling 

• Algorithm / model you used? With  proper references  

• How you can use data set/s to answer the questions at hand.

10

5. Data Analysis 

Design objectives for the data analytics tools. 

Description of the data analytics tools in decision support and decision analysis 

The methodologies for processing and evaluation used in the project

15

6. The outcomes of 

the project:

High level business interpretation of data analytic outcomes. 

Appendix of relevant visualization results, Evaluation and analysis of the data analytic outcomes and evidence of programming

10
 

Document Format / 

References

Professional presentation and layouts. If you use resources from elsewhere, make sure that you acknowledge/ reference them. Use IEEE reference style.

5

Part C 

Presentation 

Clarity of recorded video and 

presentation slides. Rationale for 

decisions made in the project. Project  outcomes and interpretation of results Future work and conclusion.

15
 

Total 

60

Marking Rubric for Assignment 2:

 

Marking Rubric Criteria/ Grades

High Distinction (HD) [Excellent] >80%

Distinction (D) 

[Very Good] 

70%-80%

Credits 

(C) [Good] 

60%-70%

Pass (P) 

[Satisfactory] 

50%-60%

Fail (N) 

[Unsatisfactory] <50%

Introduction 

Concise and 

specific to the 

project

Topics are 

relevant and 

soundly 

analysed.

Generally 

relevant and 

analysed.

Some relevance and briefly 

presented.

This is not 

relevant to the 

assignment 

topic.

Resources 

Clear and 

detailed, with 

excellentsource of references.

Clear and 

somewhat 

detailed with 

referencing

Generally good with referencing

Sometimes 

clear with 

referencing

Lacks 

consistency with many errors

Business Model and 

requirements

Logic is clear 

and easy to 

follow with 

strong 

arguments; 

Evidence of 

critical thinking

Consistency 

logical and 

convincing

Mostly 

consistent 

logical and 

convincing

Adequate 

cohesion and 

conviction

Argument is 

confused and 

disjointed

Data Analysis 

All elements are present and 

very well 

integrated.

Components 

present with 

good cohesion

Components 

present and 

mostly well 

integrated

Correct 

methods and 

most 

components 

present

Incorrect 

methods and 

wrong results

Outcomes of 

the project

Detailed 

interpretation 

of results; 

Logic is clear 

and easy to 

follow with 

strong 

arguments; 

Consistent and convincing.

Results 

interpreted 

well; 

Consistency is 

present with 

logical 

arguments

Mostly 

consistent, 

logical and 

convincing 

interpretation 

of results

Acceptable 

explanation of 

results; 

Adequate 

cohesion and 

conviction

Arguments are 

confusing and 

disjointed

Document 

format / 

References

All external 

sources 

referenced 

correctly, 

completely and consistently. 

High level of 

consistency and elegance in 

document 

formatting.

All external 

sources 

referenced 

correctly; Very good formatting of the 

document.

All external 

sources are 

referenced but with missing 

details; 

Formatting is of acceptable 

quality.

External sources are referenced, but not 

consistently or completely; 

Formatting is 

done for the 

document to be readable but 

with 

inconsistencies.

Did not 

reference most sources; 

Bad formatting of the document making it hard 

to read

Demonstra 

tion 

Demonstrated  excellent  

ability to think  critically and  

skills in using  

tools and  

methodologies  for data  

acquisition and  wrangling

Demonstrated  good ability to  think critically  and skills in  

using tools and  methodologies  for data 

acquisition and  wrangling

Demonstrated  ability to think  critically and  

skills in using  

tools and  

methodologies  for data  

acquisition  

and wrangling

Demonstrated  some ability to  think critically  

and skills in  

using tools and  methodologies  for data 

acquisition and  wrangling

Did not  

demonstrate  

ability to think  critically and  

skills in using  

tools and  

methodologies  for data 

acquisition and  wrangling