代做COMM1190 Data, Insights and Decisions Term 3, 2024代写R编程

COMM1190

Data, Insights and Decisions

Term 3, 2024

Course Learning Outcomes (CLOs)

1. Explain how an organisation uses analytical and statistical tools to gain valuable insights. [PLO1]

2. Apply statistics and data analysis skills to real data sets from various organisations and domains to generate insights to make informed decisions. [PLO2]

3. Visualise and analyse data to support arguments that increase stakeholder comprehension of information and business insights. [PLO3]

4. Work effectively in teams to communicate cohesive data insights and recommendations to various stakeholders. [PLO4]

5. Critically evaluate the suitability of data and data sources to identify and analyse business problems. [PLO2]

6. Evaluate the ethical implications of the organisational use of big data and analytics on stakeholders and society. [PLO5]

Assessment 1: Individual Assignment

Context of assessment task

You are a Business Analyst working for TelcomCo, a telecommunications organisation in Australia. The General Manager (GM) has asked you to conduct a deeper analysis to explore the factors associated with the churn rate and suggest recommendations on how to enhance customer retention. He provided you with a copy of the Memo produced by a freshly recruited junior analyst.

GM has expressed concerns over the quality of the Memo in form. and substance. Your task is to review the initial report and produce a revised version using an updated, expanded personalised data set containing the original pilot data and some extra observations.

Instructions from the General Manager

From: GM

Subject: Report revision project

Good morning,

Thank you for agreeing to revise the initial report. It is imperative that I have high-quality, data-driven insights to use in my presentation to the Board of Directors, and I am not satisfied with the initial report provided—there are mistakes that need to be corrected. Please see below for details on what is required.

As outlined below, I am interested in understanding the factors triggering the churn rate.

1) Characteristics of the customers

2) How much do they spend, and what services do they subscribe to?

3) How satisfied they are with our services

4) Any insights into customer churn and recommendations on extra data we need to continue this investigation.

This is the first step in a more extensive analysis of customer retention. It is important because we need to encourage repeat business from loyal customers and avoid losing them to competitors.

Some guidelines that will aid you in improving upon the original report:

 Please create high-quality graphics using R to meet our organisation’s presentation standards.

 Please conduct the entire analysis in R for quality control reasons. I’ll leave the choice of graphs and associated analyses to you as an analyst.

 Please provide advice on what other variables and data would be useful as the project develops, specifically related to spending patterns, services provided, and customer retention.

 You can use the initial report as a template; it is appropriate for sections and length, so your revised report should be approximately the same in these two dimensions. Any other elements of the report structure are left to your discretion.

The dataset you will use contains both the initial data used by the junior analyst and some additional data collected after the fact. In a separate communication, I will provide access to these data and a copy of the original report.

Good luck with the project. I look forward to seeing what you come up with.

Approach to the assessment task

a) Read the GM’s instructions carefully, including the metrics she wants insights into and her guidelines to improve the original report.

b) Download the entire dataset. Note that this dataset contains both the original (pilot) data used by the intern and extra observations. All students in the course will have the same pilot data, but the extra observations are individualised. You will access your dataset on Moodle.

c) Review the intern’s initial report to plan how you will revise it using your analyses and visualisations with R. Remember that the structure of your report will be approximately the same as the initial report, i.e. similar length and sections.

d) As there were problems with the initial report, as highlighted by the GM in his email, you should not be restricted to the analyses presented in the initial report.

e) Ensure you carefully select and include only data and visualisations supporting your main findings and conclusions. It would be best to outline key assumptions or limitations in your analysis.

f) When submitting your report, you must provide a separate file containing the R code used to conduct your analysis and generate visualisations. No marks will be awarded for this code file, but your submission will be deemed incomplete and given a zero mark if this file is not included.

g) Submit your revised report and code file as separate documents via Turnitin on the Moodle course site. You can choose the structure of the code file. There is no word limit for the code file.

h) Late submission will incur a penalty of 5% per day or part thereof (including weekends) from the due date and time. Assessment 1 will not be accepted after 9:00 a.m.11 October 2024. For further information, please refer to Policies and Support.

i) Special consideration will be granted only in the case of serious illness, misadventure, or bereavement, which must be supported with documentary evidence. In these circumstances, students must apply for Special Consideration. Because of the sequential nature of the assessment tasks, it isn't easy to allow extensions without impacting the academic integrity of the assessment. This course does not use the short extension process you may have seen in other classes. Moreover, suppose you are granted special consideration due to exceptional circumstances precluding you from completing the assessment task on time. In that case, you will likely have your final exam reweighted rather than being given an extension.

Assessment 2: Team Assessment

Assessment Overview

You will undertake a project as a team, applying the key concepts discussed in the course to a real-world scenario. In this assessment, you will explore data using descriptive and predictive analytics to derive actionable insights that can be used to assist with business decision-making. The assessment task is designed to develop teamwork skills within an analytics team and technical skills for analysing data to arrive at decisions and recommendations based on the team’s data-generated insights.

Instructions

In Week 5, you will receive detailed instructions regarding Assessment 2, the associated rubric, and the formation of groups.

Approach to the assessment task

a) As soon as you have the assessment instructions, you are encouraged to start working with your group from Week 5 onwards.

b) You should first complete Stage 1 (individual component) of Assessment 2 to support your group work for Stage 2.

Assessment 3: Final Exam

Assessment Overview

The final exam will test your technical competence, problem-solving skills, and understanding of the concepts discussed in all weeks of the course. Later, a range of questions and examples drawn from past exams will be provided.

You will be able to access the COMM1190 exam and detailed instructions via the course Moodle site closer to the examination's time.