TECH3100 Data Visualisation in R Assessment 1 Information

Your Task

This assessment is to be completed individually. In this assessment, you will evaluate and apply standard statistical techniques to data analytics using the R language that meets the specifications described in the case study.

Assessment Description

For this assessment, you are required to develop a case study that demonstrates your ability to evaluate and apply standard statistical techniques to data analytics using the R language.

Case Study:

You have been hired as a data analyst for a marketing research company. Your task is to analyse a dataset to derive insights and make data-driven recommendations for a marketing campaign. The dataset provided contains information about customers, their demographics, purchasing behaviour, and campaign response.

Step 1: Dataset Selection: Choose a suitable dataset from publicly available datasets commonly used with R. Some popular options include:

• "mtcars": This dataset contains information about various car models, including variables such as horsepower, miles per gallon (MPG), and number of cylinders.
• "iris": This dataset includes measurements of different iris flower species, including variables like petal length, petal width, and sepal length.
• "diamonds": This dataset contains information about diamonds, including variables such as carat weight, cut quality, and price.
• Or you may use a dataset specific to your chosen industry or field of interest. Ensure that the dataset has sufficient variables and diversity to allow for the application of a range of statistical techniques.

Step 2: Data Import: Import the chosen dataset into R using appropriate functions or libraries. Include the necessary code for dataset importation in your submission.

Step 3: Data Exploration: Perform exploratory data analysis by examining the structure, summary statistics, and visualisations of the dataset. Utilise appropriate techniques covered in the subject (Weeks 1, 2, and 3) to gain insights into the dataset.

Step 4: Descriptive Statistics: Calculate and present descriptive statistics, including measures of central tendency, measures of dispersion, and relevant statistical summaries for key variables in the dataset. Utilise appropriate R functions and techniques to perform these calculations.

Step 5: R Code Documentation: Document your R code using comments to explain the purpose and functionality of each step in the analysis. Ensure that your code is well-structured, readable, and easily understandable.

Step 6: Interpret the results obtained from the statistical analysis in the context of the marketing campaign. Clearly explain the insights or conclusions drawn from the analysis and provide data- driven recommendations for the marketing strategy based on your findings. You will present this in the case study report.

This assessment aims to achieve the following subject learning outcomes:

Assessment Instructions

Assessment instructions for this assessment:

1. Case Study Report: Prepare a comprehensive written report documenting your case study. Include an introduction to the marketing problem or scenario, details of the dataset used, step- by-step explanation of the analysis performed, interpretation of the results, and data-driven recommendations for the marketing campaign. Use appropriate headings, subheadings, and visualisations to present your findings logically.
   1. You must submit your report in Word document or in PDF format.
2. R Code: Include the complete R code you used to perform the analysis. Ensure that the code is well-documented with comments.
   1. You must submit your R code in .r format extension. Any other formats will not be accepted.
3. Please refer to the assessment marking guide to assist you in completing all the assessment criteria.
4. Note: zipped files will not be accepted.

Important Study Information

Academic Integrity and Conduct Policy

https://www.kbs.edu.au/admissions/forms-and-policies

KBS values academic integrity. All students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Academic Integrity and Conduct Policy.

Please read the policy to learn the answers to these questions:

• What is academic integrity and misconduct?
• What are the penalties for academic misconduct?
• How can I appeal my grade?

Late submission of assignments (within the Assessment Policy)

https://www.kbs.edu.au/admissions/forms-and-policies

Length Limits for Assessments

Penalties may be applied for assessment submissions that exceed prescribed limits.

Study Assistance

Students may seek study assistance from their local Academic Learning Advisor or refer to the resources on the MyKBS Academic Success Centre page. Further details can be accessed at https://elearning.kbs.edu.au/course/view.php?id=1481

Generative AI Traffic Lights

Please see the level of Generative AI that this assessment is Level 2 has been designed to accept: