代做BSD131: Assessment Task 2代做Java程序

BSD131: Assessment Task 2

Task overview

Assessment name:	Business analytics simulated case study
Task description:	For this assessment, you will  prepare  a  portfolio which  provides a  business analytics research plan. This assessment has four parts aimed at guiding you through the CRSP-DM process: a research component, a descriptive statistics component,  some  analytics  and  a  final  conclusion. All  four  parts  are  to  be answered.
Due date:	Week 10, Wed at 11.59pm (Brisbane time).
Length:	1600 words (word length excludes in-text referencing and your reference list)
Weighting:	35%
Individual/Group:	Individual
Formative/Summative:	Summative
Assessment and feedback:	Grading scale using a rubric

Task details

Background:

Researchers are interested in determining the spending habits of 1000 students across various demographic groups and academic backgrounds. A survey was undertaken to collect data on age, gender, year in school, major, monthly income, financial aid received, and expenses in different spending categories. Spending categories include tuition, housing, food, transportation, books and supplies, entertainment, personal care, technology, health and wellness, and miscellaneous expenses. Additionally, the dataset includes the preferred payment method for each student.

Data key:

The definitions of each of the data columns is as follows: • Age: Age of the student (in years) • Gender: Gender of the student (Male, Female, Non-binary) • Year in School: Year of study (Freshman, Sophomore, Junior, Senior) • Major: Field of study or major • Monthly Income: Monthly income of the student (in dollars) • Financial Aid: Financial aid received by the student (in dollars) • Tuition: Expenses for tuition (in dollars) • Housing: Expenses for housing (in dollars)

	• Food: Expenses for food (in dollars)
	• Transportation: Expenses for transportation (in dollars)
	• Books & Supplies: Expenses for books and supplies (in dollars)
	• Entertainment: Expenses for entertainment (in dollars)
	• Personal Care: Expenses for personal care items (in dollars)
	• Technology: Expenses for technology (in dollars)
	• Health & Wellness: Expenses for health and wellness (in dollars)
	• Miscellaneous: Miscellaneous expenses (in dollars)
	• Preferred Payment Method: Preferred payment method (Cash,
	Credit/Debit Card, Mobile Payment App)
Instructions:	You are  required to work through the CRISP-DM  process  by answering the questions  below. You are expected to  use  academic  language and to  refer explicitly to the data to support your points. This is not a research task, so there will be no citations or reference list. It is very important for you to conduct this analysis yourself and not to access any generative AI software. The use of this type of software is not authorised in this unit. Part A: Explain & Understand the Process of the CRISP-DM Model 1.    What   is   the   importance   of defining   business   objectives   at   the beginning of a data mining project? 2.    Why is data cleaning and pre-processing important in the data mining process? 3.    What   are   the    key   considerations    when   selecting    a   modelling technique? 4.    Discuss the importance of model evaluation and validation. 5.    What  are  the  challenges  associated  with deploying a data mining solution in a real-world scenario? Part B: Data Preparation and Descriptive Statistics Using the dataset supplied. 1.    What questions would the researchers have regarding this dataset? 2.    Are there any issues with the data or other variables that could be collected to help answer the researchers’ questions? 3.    Are  there  any  unusual  observations  in  the  data  set? Identify  their values and any potential problems that they could cause (Hint: think of the results that could be affected) 4.    Prepare a one-page dashboard (box plots, time series plot, bar charts and any relevant tables) that can be used to describe the main features of the dataset.

Part C: Statistical Analysis of the Data

1. What  is the  relationship between Age  and  Monthly  Income? This could help understand if there's a correlation between age and earning potential.

2. How does Year in School relate to Monthly Income? This could reveal whether income tends to increase as students’ progress through their academic years.

3. Is   there   a   relationship   between   Major and Monthly Income? Exploring whether certain fields of study lead to higher incomes.

4. What factors influence Tuition expenses? Analysing how variables like Financial Aid and Year in School impact tuition costs.

5. Does Preferred Payment Method relate  to  any other variables? Investigating  whether   payment   preferences   are   associated   with demographic or financial factors.

6. How  do various  expenses  (e.g.,  Books & Supplies,  Entertainment) relate to each other? Understanding spending patterns and potential trade-offs between different categories.

7. Can   we   predict   Total   Expenses   based on individual spending categories? Building a model to estimate total expenses based on the breakdown of spending across different categories.

8. Does  Financial  Aid impact  the relationship between Income and Expenses? Exploring  whether  financial  aid  mitigates  the  impact  of income on expenditure patterns.

9. How   do    Health    &    Wellness   expenses    vary    across   different demographics? Investigating whether certain groups spend more on health-related items.

10. What is  the   impact of  Age, Gender, and   Major on  Technology expenses? Exploring how demographic and academic factors influence technology spending habits.

Part D: Final Conclusions

Provide a comprehensive analysis of key insights derived from the dataset, spanning demographic information, academic standing, income, expenses, and payment preferences. Your response should encompass a detailed examination of the data trends, highlighting notable correlations, issues, and potential implications for any decision-making and strategic planning to better support students.

Learning outcomes measured: Learning outcome 1: Apply a selection of methods to collect, aggregate and process business data from multiple sources.

Learning outcome 2: Present data and analysis results in effective forms to assist business decision making in a variety of industrial and organisational contexts.

Learning outcome 3: Critically analyse data using a variety of methods and approaches to generate business insights and inform. business decision making.