ANL252 Python for Data Analytics End-of-Course Assessment – July Semester 2025, Singapore

INSTRUCTIONS TO STUDENTS:

1. This End-of-Course Assessment paper comprises 7 pages (including the cover page).
2. You are to include the following particulars in your submission: Course Code, Title of the ECA, SUSS PI No., Your Name, and Submission Date.
3. Late submission will be subjected to the marks deduction scheme. Please refer to the Student Handbook for details. 

IMPORTANT NOTE

ECA Submission Deadline: Friday, 31 October 2025, 12:00 pm

ECA Submission Guidelines

Please follow the submission instructions stated below:

A – What Must Be Submitted

You are required to submit the following item for marking and grading:

• A Report

Please verify your submissions after you have submitted the above ONE (1) item.

B – Submission Deadline

• The Report is to be submitted by 12 noon on the submission deadline.
• You are allowed multiple submissions till the cut-off date.
• Late submission will be subjected to mark-deduction scheme by the University. Please refer to Section 5.2 Para 2.4 of the Student Handbook.

C – How the (1) Item Should Be Submitted

• The Report: submit online to Canvas via TurnItIn (for plagiarism detection)
• Avoid using a public WiFi connection for submitting large files. If you are using public wireless (WiFi) connection (e.g. SG Wireless at public areas), you might encounter a break in the connection when sending large files.

D – Additional guidelines on file formatting are given as follows

E – Please be Aware of the Following

Submission in hardcopy or any other means not given in the above guidelines will not be accepted. You do not need to submit any other forms or cover sheets (e.g. form ET3) with your ECA.

You are reminded that electronic transmission is not immediate. The network traffic may be particularly heavy on the date of submission deadline and connections to the system cannot be guaranteed. Hence, you are advised to submit your work early. Canvas will allow you to submit your work late but your work will be subjected to the mark-deduction scheme. You should therefore not jeopardise your course result by submitting your ECA at the last minute.

It is your responsibility to check and ensure that your files are successfully submitted to Canvas.

F – Plagiarism and Collusion

Plagiarism and collusion are forms of cheating and are not acceptable in any form in a student’s work, including this ECA. Plagiarism and collusion are taking work done by others or work done together with others respectively and passing it off as your own. You can avoid plagiarism by giving appropriate references when you use other people’s ideas, words or pictures (including diagrams). Refer to the APA Manual if you need reminding about quoting and referencing. You can avoid collusion by ensuring that your submission is based on your own individual effort. 

The electronic submission of your ECA will be screened by plagiarism detection software. For more information about plagiarism and collusion, you should refer to the Student Handbook (Section 5.2.1.3). You are reminded that SUSS takes a tough stance against plagiarism or collusion. Serious cases will normally result in the student being referred to SUSS’s Student Disciplinary Group. For other cases, significant mark penalties or expulsion from the course will be imposed.

G – Use of Generative AI Tools (Allowed)

The use of generative AI tools is allowed for this assignment.

• You are expected to provide proper attribution if you use generative AI tools while completing the assignment, including appropriate and disciplinespecific citation, a table detailing the name of the AI tool used, the approach to using the tool (e.g. what prompts were used), the full output provided by the tool, and which part of the output was adapted for the assignment;
• To take note of section 3, paragraph 3.2 and section 5.2, paragraph 2A.1 (Viva Voce) of the Student Handbook;
• The University has the right to exercise the viva voce option to determine the authorship of a student’s submission should there be reasonable grounds to suspect that the submission may not be fully the student’s own work.
• For more details on academic integrity and guidance on responsible use of generative AI tools in assignments, please refer to the TLC website for more details;
• The University will continue to review the use of generative AI tools based on feedback and in light of developments in AI and related technologies.

(Full marks: 100)

Section A(100 marks)

Answer all questions in this section.

The dataset used in this paper contains information about startup acquisitions, and its data dictionary is provided in the Appendix. Please refer to Canvas for details of this dataset.

Notes on assignment writing: Your writing should be succinct but not at the expense of excluding relevant details. The topics in the main report should be presented in the order according to the sequence of the tasks/questions listed in the assignment; that is, in the order of Question 1, Question 2, …, etc. To avoid high Turnitin score, do not copy the assignment questions into the report. Some questions may not come with absolutely right or wrong answers. For such questions, you have the liberty to express your views about the problem. You are also permitted to engage in independent research to demonstrate higher-order thinking skills when answering the questions. You are suggested to include less relevant details in your Appendix, if any.

Question 1

Propose and conduct at least three (3) data pre-processing tasks to clean and prepare the given dataset on startup acquisitions using Python. Provide relevant explanations. [No more than 300 words (including the corresponding content in appendix and in-text citation; excluding Python code and reference list)]

(30 marks)

Question 2

Use Python to plot three (3) figures based on the processed startup acquisitions dataset obtained from Question 1. Discuss the insights for each figure accordingly. Each figure and its corresponding Python codes and insights collectively carry 10 marks.

The figures and Python codes are to be provided as part of the answer in the main report. [No more than 450 words (including the corresponding content in appendix and in-text citation; excluding Python code and reference list)]

(30 marks)

Question 3

Use Python to further analyse or model the processed dataset obtained from Question 1 using a decision tree, where the dependent variable is ‘Acquired’. Explain the relevant steps involved in building the decision tree model. [No more than 200 words (including the corresponding content in appendix and in-text citation; excluding Python code and reference list). You do not need to plot the decision tree in this question.]

(20 marks)

Question 4

Plot the decision tree model obtained from Question 3 with Python. Discuss the relevant insights based on the tree plot. [No more than 200 words (including the corresponding content in appendix and in-text citation; excluding Python code and reference list)]

(10 marks)

Question 5

Discuss alternative data analytics methods or models that could be employed to complement or enhance the insights derived from the decision tree model above. Assumptions can be made to support the discussion. [No more than 300 words (including the corresponding content in appendix and in-text citation; excluding reference list)]

(10 marks)

Appendix:

DATA DICTIONARY

—– END OF ECA PAPER —–