MCS-067 IGNOU Solved Assignment 2026-27

MCS-067 Solved Assignment: Data Wrangling and Visualization Verified TMA Solutions

The following content outlines real syllabus blocks from the official IGNOU curriculum for MCS-067 (Data Wrangling and Visualization), with solved assignment answers aligned to 30% course weightage and submission deadlines. All solutions are plagiarism-free and comply with academic session guidelines.

Core Syllabus Blocks Covered

• Block-1: Data Wrangling-I
  • Data Cleaning Techniques: Handling missing values, outliers, and inconsistent data formats using Python (Pandas) and R (dplyr).
  • Data Transformation: Normalization, encoding categorical variables, and feature engineering for machine learning pipelines.
  • Case Study: Real-world dataset (e.g., Kaggle) with step-by-step wrangling code snippets for 500-word solutions.
• Block-2: Data Wrangling-II
  • Data Integration: Merging datasets (SQL joins, Pandas `merge`), resolving schema conflicts, and API-based data extraction.
  • Data Validation: Statistical tests (Chi-square, ANOVA) and cross-validation techniques for data quality assurance.
  • Assignment Focus: 250-word answers on data pipeline automation using Python scripts or R Shiny dashboards.
• Visualization Integration
  • Interactive plots (Plotly, Matplotlib) for exploratory data analysis (EDA) with 100-word concise summaries.
  • Heatmaps, box plots, and time-series visualizations for Block-1/Block-2 assignments.

Assignment Structure & Submission Guidelines

Solutions are structured per IGNOU’s TMA format:

• 500-word answers: Detailed Python/R code + explanations for Blocks 1–2 (e.g., cleaning a dataset with 10% missing values).
• 250-word answers: Focused on data integration (e.g., merging two CSV files with conflicting keys).
• 100-word answers