MCS-068 IGNOU Guess Paper 2026-27

Course Scope & Syllabus Overview for MCS-068: Predictive Data Analysis

MCS-068: Predictive Data Analysis is a core component of IGNOU’s M.Sc. (Data Science and Analytics) curriculum, designed to equip students with advanced analytical techniques for forecasting trends, patterns, and outcomes from structured and unstructured data. This course bridges foundational statistics and machine learning, emphasizing practical applications in predictive modeling, data preprocessing, and algorithmic implementation. Through rigorous engagement with real-world datasets, students learn to build, validate, and deploy predictive models using industry-standard tools, aligning with the School of Computer and Information Sciences’ focus on applied data science.

Key Syllabus Units & Topics

• Block-1 Data Analysis An Introduction: Covers fundamental concepts of data types, data cleaning, and exploratory data analysis (EDA) using statistical and visualization techniques. Students explore data wrangling methodologies to prepare datasets for predictive modeling.
• Block-2 Predictive Data Analysis-I: Focuses on regression analysis, classification algorithms, and model evaluation metrics. Topics include linear and logistic regression, decision trees, and ensemble methods, with emphasis on interpreting model outputs and addressing overfitting.
• Block-3 Predictive Data Analysis-II: Delves into advanced predictive techniques such as clustering (K-means, hierarchical), dimensionality reduction (PCA), and anomaly detection. Students analyze unsupervised learning algorithms and their applications in customer segmentation and fraud detection.
• Block-4 R-Programming for Data Analysis: Provides hands-on training in R for data manipulation (dplyr, tidyr), visualization (ggplot2), and predictive modeling. The block integrates R scripts with statistical packages to automate workflows and generate actionable insights.

Frequently Asked Questions

Q: How are marks distributed in the MCS-068 Term-End Examination (TEE), and what weightage do assignments carry?
A: The TEE for MCS-068 carries 100 marks, divided into objective (20 marks) and descriptive (80 marks) sections. Assignments account for 30 marks in the overall grade, requiring students to submit solutions based on practical exercises and theoretical questions from the course blocks.

Q: Which units in MCS-068 have historically appeared most frequently in past TEE question papers?
A: Blocks 2 and 3 (Predictive Data Analysis-I and II) consistently feature prominently in TEE papers, with regression models, classification algorithms, and clustering techniques appearing repeatedly. Block 4 (R-Programming) also attracts questions on data wrangling and visualization scripts.