|University||Singapore University of Social Science (SUSS)|
|Subject||ANL203 ANALYTICS FOR DECISION-MAKING Assignment|
ANL203 ANALYTICS FOR DECISION-MAKING Assignment, SUSS, Singapore: Identify one (1) possible problem that can be addressed by analyzing the dataset and discuss the critical assumptions for potential analytics application.
“Employment_GBA.csv” records the Graduate Employment Survey (GES), a collaborative effort among NTU, NUS, SMU, SIT (since 2014), SUTD (since 2015), and SUSS (since 2018) to conduct an annual survey of graduate employment conditions approximately six months after their final examinations. The Ministry of Education (MOE) publishes the results of key
employment indicators from this survey to provide prospective students with timely and comparable data, enabling them to make informed decisions about their courses. Graduates from NTU, NUS, SMU, and SUSS are surveyed in November. However, due to differences in academic calendars, graduates from SUTD and SIT are surveyed in February and March, respectively. You are to follow the Six-Stage Problem-Solving Process to answer the following
questions using this dataset.
(a) Identify one (1) possible problem that can be addressed by analyzing the dataset and discuss the critical assumptions for potential analytics application. (Up to 150 words for part (a))
(b) Prepare a summary of the dataset in tabular format. It should contain the data dictionary, each data field’s type, and summary measures. (Up to 150 words for part (b))
(c) Develop a proposal for an analytics solution. It should contain a discussion of the appropriateness of at least one (1) business analytics technique to use and an explanation of how the technique(s) can be used to address the identified problem in part a). You are only required to discuss the expected results and do not need to construct the actual model(s). (Use up to 250 words for part (c))
(d) Employ data visualization techniques to develop four (4) charts to show an interesting trend, pattern, or relationship among variables from the dataset. Provide a screenshot of each produced chart. Explain how the charts are created and discuss why the charts are recommended. (Use up to 250 words for part (d))
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