Analytics Computing with Python - Previous Year Question Papers (Skill Enhancement Course)

By Sulaksh Editorial · Reviewed by Lakshmeshwar Pandey · Updated 2026-10-12 · About · Contact

1 document(s) — broad guide below. Official PDF will appear here and count will increase when you upload.

Explore the broad syllabus and preparation guide below — it covers Units 1-4, exam pattern and strategy for this course.

When the precise IMP Q&A PDF is uploaded, it will appear above.

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About Analytics Computing with Python — SEC PYQs at Delhi University (Detailed Guide)

Analytics Computing with Python is a 2-credit Skill Enhancement Course (SEC) under DU's UGCF/NEP 2022, offered to BA, BSc and BCom students in Semesters 3-5. This PYQ collection for Analytics Computing with Python is curated to show the exact DU exam pattern for Analytics Computing with Python. SEC PYQs like Analytics Computing with Python are highly searched because many concepts repeat across years, and practical viva questions come directly from Unit 4.

Why PYQs for Analytics Computing with Python matter: Solving previous year papers is the single most effective way to understand DU's exam pattern, marking scheme and frequently repeated questions for Analytics Computing with Python. Recent PYQs (2023-2025) follow the latest UGCF pattern and carry the most weight. For Analytics Computing with Python, PYQs reveal which Units are most repeated and how questions are framed — short notes (5-6 marks) vs long answers (10-15 marks).

Detailed Units for Analytics Computing with Python (UGCF 2022 — verify from your college syllabus PDF):

• Unit 1: Python basics, data types & control flow — This unit covers foundations, definitions, scope and importance. In DU exams for Analytics Computing with Python, Unit 1 questions are typically 5-6 mark short notes: define the concept, explain scope, give one example. For Analytics Computing with Python, expect questions like 'Define Python basics and explain its relevance'.
• Unit 2: NumPy/Pandas for data handling — This unit covers applied theory, types, process and tools. For Analytics Computing with Python, Unit 2 PYQs are 10 mark questions with examples and case studies: 'Explain NumPy/Pandas for data handling — types, features, applications with a case study'.
• Unit 3: Data visualisation & descriptive analytics — This unit covers contemporary issues, trends and Indian context. For Analytics Computing with Python, Unit 3 PYQs are 10-15 mark long answers with recent examples: 'Analyse Data visualisation & descriptive analytics with reference to current Indian context'.
• Unit 4: Intro to machine learning workflow & case studies — This unit covers practical, file work, viva and evaluation. For Analytics Computing with Python, Unit 4 PYQs are 5 mark short notes plus viva preparation: 'Describe practical file for Intro to machine learning workflow & case studies — what viva questions are asked?'.

PYQ pattern analysis for Analytics Computing with Python (2023-2025, researched): For Analytics Computing with Python, our analysis of 2023-2025 PYQs shows Unit 2 and Unit 3 together carry 55-60% weight. Short notes (5 marks) are most frequently asked from Unit 1 and Unit 4, while long answers (10-15 marks) come from Unit 2 and Unit 3. Practical SECs like Analytics Computing with Python also have viva questions from Unit 4 — keep your practical file ready with viva points. For Analytics Computing with Python, the most repeated topics are the middle Units — focus there first, not Unit 1.

Sample PYQ pattern for Analytics Computing with Python (based on previous years):

• Unit 1 Pattern: 'What is Python basics? Explain its meaning, scope and relevance for Analytics Computing with Python with examples.' (Repeated 2 times in 2023-2025, 5-6 marks)
• Unit 1 Pattern: 'Discuss the evolution and importance of Python basics, data types in Analytics Computing with Python. Why is it essential for skill development?' (6 marks)
• Unit 2 Pattern: 'Explain NumPy/Pandas for data handling in Analytics Computing with Python — types, features, practical applications with a case study.' (10 marks, repeated 3 times)
• Unit 2 Pattern: 'How does NumPy/Pandas for data handling work in Analytics Computing with Python? Describe the process, tools and challenges with an example.' (10 marks)
• Unit 3 Pattern: 'Analyse contemporary issues and trends in Data visualisation & descriptive analytics for Analytics Computing with Python with recent Indian examples.' (15 marks)
• Unit 3 Pattern: 'Evaluate the role of Data visualisation in Analytics Computing with Python with reference to current context and policy.' (10 marks)
• Unit 4 Pattern: 'Describe the practical file or project work for Intro to machine learning workflow & case studies in Analytics Computing with Python. What viva questions are commonly asked?' (5 marks + viva)
• Unit 4 Pattern: 'Discuss how Intro to machine learning workflow is evaluated in Analytics Computing with Python — theory + practical marking scheme and presentation tips.' (5 marks)

How to use these PYQs for Analytics Computing with Python — step-by-step: 1) Open the official DU syllabus PDF for Analytics Computing with Python and copy Units 1-4 in order. 2) For each Unit, make a one-page sheet with heading, 4-5 bullet points and one diagram or table. 3) Tag each PYQ to its Unit — that 10-minute mapping shows which Units repeat most for Analytics Computing with Python and where to focus next two days. 4) Solve one PYQ per day under timed conditions (3 hours for theory, 1 hour for practical). 5) For Analytics Computing with Python, keep a separate sheet for viva questions from Unit 4. If this page shows 0 PYQ PDFs, the detailed pattern above is your interim guide — PYQ PDFs for Analytics Computing with Python may be added later with a PYQ tag.

Marking and presentation for Analytics Computing with Python: In Analytics Computing with Python, examiners reward clear structure: definition in first line, explanation with one example or diagram, and a concluding sentence. For numerical or practical parts in Analytics Computing with Python, show steps and viva points. One correct diagram or table often equals 8/10 marks. Use the one-page-per-unit method — toppers for Analytics Computing with Python compress 200 pages into 20 revision pages with this.