ANL203 Analytics for Decision-Making End-of-Course Assessment 2026 | SUSS, Singapore

University Singapore University of Social Science (SUSS)
Subject (ANL203) Analytics for Decision Making

ANL203 End-of-Course Assessment 2026

ANL203 Analytics for Decision-Making End-of-Course Assessment 2026

Answer all questions in this section.

Question 1

You are provided with a dataset that captures middle school students’ academic performance in a specific course across three evaluation periods. In addition to the grades, the dataset includes a variety of demographic, social, and school-related characteristics collected through school reports and structured questionnaires. These attributes can provide deeper insights into students’ backgrounds, well-being, learning environments, and behaviours. This dataset offers a valuable opportunity to explore how different factors may be associated with or contribute to students’ academic outcomes.

Identify one (1)  business problem that can be addressed by analysing the data. List the relevant data fields and elaborate on how they can be used to provide insights to address the business problem. (Max word count: 150 words)

(15 marks)

Question 2

Prepare a tabular summary of the dataset, indicating the data types (nominal, ordinal, interval or ratio) and relevant summary measures for all data fields.

(15 marks)

Question 3

Employ visualisation methods to create a dashboard that incorporates at least three (3) charts, each showing valuable insights from the dataset. Attach screenshots of each chart, along with the entire dashboard. Describe the steps for creating the charts and how the components of the dashboard collectively address the business problem identified in Question 1. (Max word count:  500 words)

(40 marks)

Question 4

Develop a proposal for a business analytics solution to tackle the business problem identified in Question 1. It should include the discussion of at least two (2) appropriate business analytics techniques, such as spreadsheet analysis, clustering, or predictive/prescriptive analytics. Explain how the relevant data fields can be used to generate useful information to address the business problem. (Max word count: 300 words)

(30 marks)

                                           —– END OF ECA PAPER —–

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