S36103 Statistical Thinking for Data Science
Assessment task 1: Exploration of data skills and issues
Task:
This assessment is intended to conduct exploratory data analysis (EDA) on a marketing campaign dataset from a telecommunication company. A telecommunication company recently launched a marketing campaign to promote the adoption of their new subscription plan among customers. The company seeks assistance in gaining a comprehensive understanding of their customers and identifying the customer segments that display the highest responsiveness to marketing campaigns.
The dataset is available on Canvas. The response variable, subscribed, indicates whether the client subscribed to a new plan, which was the objective of the campaign.
The dataset may have issues such missing information and data errors. Identifying and handling such issues is part of the assessment.
The requirements involve applying a minimum of three distinct exploratory data analysis techniques to gain preliminary insights from the data.
➢ For the assessment task, you are required to submit a report that includes the following elements as a minimum requirement:
➢ Problem formulation. It should incorporate a comprehensive discussion of the analysis context, the specific problem at hand, the pertinent questions, and hypotheses to be addressed. (Criteria 1: CILO 2.2)
➢ Data preprocessing. Your report should encompass a comprehensive and informative overview of the dataset. It is essential to ensure that the dataset is error-free and undergoes accurate processing before analysis. Furthermore, it is crucial to provide a concise description of the data processing steps undertaken. (Criteria 2: CILO 2.4)
➢ Exploratory data analysis (EDA). It involves a comprehensive examination of relevant variables both individually and in relation to each other. This is achieved through the use
of appropriate figures and descriptive statistics. During this process, you take note of all data characteristics that are pertinent for model development. The results of the EDA are clearly explained in terms of their relevance to the overall goal of the project. (Criteria 2: CILO 2.4)
Finalise and submit the report:
o Before submission, check for spelling and grammatical errors. (Criteria 3: CILO 3.1)
o Format the report to enhance readability, use headings and choose appropriate fonts, etc. (Criteria 3: CILO 3.1)
o Submit the report in Canvas in pdf format.
Assessment Criteria:
Assessment task 2: Data analysis project
This task focuses on exposing students to each key step in a data science project cycle using real-world data. Students will work in teams to propose, execute, and critically reflect upon a full data science research project. Students will learn how to develop and pitch a research idea, execute their chosen project, communicate findings both verbally and in report format, and self-evaluate project progress and areas for improvement. This task is split into three subtasks.
As a data scientist, one of your core duties will be to work with a team of people to analyse complex datasets and to report back the results of that analysis to stakeholders from a variety of backgrounds, who often have different needs and capabilities. This assessment task will give you a chance to experience the complexity that can often arise in this
situation, and gain experience executing a full data science project cycle.
Different stakeholders often have different expectations as to how statistical information and models will be communicated to them. Thus, senior managers (who are frequently the final decision makers) often expect a brief presentation but will rely upon a separate set of recommendations from an in-house team of people who are more expert in a domain. These recommendations are often derived from a combination of reports and presentations. The aim of the assessment task is to communicate with both types of stakeholders.
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