MCS-067 IGNOU Guess Paper 2026-27

MCS-067 Guess Paper: Data Wrangling and Visualization Term-End Exam (TEE) Focus

This structured guess paper aligns with the official IGNOU curriculum for MCS-067, emphasizing Block-1 Data Wrangling-I and Block-2 Data Wrangling-II. It includes chapter-wise question weightage, time management tips, and FAQs based on solved papers from the past 5–10 years.

Key Blocks & Syllabus Coverage

• Block-1 Data Wrangling-I
  • Data cleaning: Handling missing values, outliers, and inconsistencies (SQL/Python: Pandas, NumPy).
  • ETL (Extract, Transform, Load) pipelines: Tools like Apache NiFi, Talend, and scripting (Python/R).
  • Data integration: Merging datasets from relational and NoSQL sources (e.g., MongoDB, PostgreSQL).
• Block-2 Data Wrangling-II
  • Data visualization fundamentals: Principles of effective visual design (e.g., Tableau, Matplotlib, Seaborn).
  • Interactive dashboards: Building dynamic visualizations with Power BI or Plotly for business insights.
  • Case studies: Real-world applications in healthcare analytics (excluding nursing), finance, or IoT data.

Exam Pattern & Question Weightage

Based on previous TEE sessions (June/December), expect:

• 30% weightage on data cleaning (SQL queries, Pandas operations, anomaly detection).
• 25% weightage on ETL and data integration (pipeline design, tool comparisons).
• 20% weightage on visualization techniques (choosing the right chart type, tool-specific features).
• 15% weightage on case studies (interpretation of dashboards, scenario-based questions).
• 10% weightage on short-answer questions (definitions, tool comparisons, or ethical considerations in data wrangling).

3-Hour Exam Time Management Tips

• Allocate 45 minutes for reading the question paper and planning answers (prioritize high-weightage topics).
• Spend 1 hour on long-answer questions (e.g., designing an ETL pipeline or explaining a visualization tool).
• Dedicate 30 minutes to short-answer questions (focus on SQL snippets, tool features, or definitions).
• Leave 15 minutes for review (check calculations, grammar, and clarity of diagrams if applicable).

FAQs for MCS-067

Q1: Are Python scripts allowed in the exam for data wrangling? A: No. While Python is taught for data cleaning, the exam expects conceptual answers and pseudocode if needed. Focus on explaining logic rather than writing executable scripts. Q2: How important are case studies in scoring? A: Case studies contribute 15–20% of the marks. Prioritize understanding real-world applications (e.g., fraud detection, supply chain analytics) over memorization.

Solved Paper Insights

Commonly repeated topics from past papers include:

• SQL queries for data cleaning (e.g., `DROP NULL`, `GROUP BY` with aggregation).
• Comparison of visualization tools (Tableau vs. Power BI vs. Python libraries).
• Steps in an ETL pipeline with tool examples (e.g., Apache Spark for large-scale data).
• Ethical considerations in data visualization (avoiding misleading charts).