Data Preparation and Analysis
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Follow on Instagram for UpdatesData Preparation and Analysis Syllabus — Unit-wise Content
The official GTU curriculum for Data Preparation and Analysis (subject code ME02000361) as prescribed for CE · Semester 2 divides the course into 5 units, covering 30 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (ME02000361)Unit 1 Understanding Data
- Data Objects and Attribute Types
- Basic Statistical Descriptions of Data
- Data Visualization (Pixel-Oriented
- Geometric Projection
- Measuring Data Similarity and Dissimilarity for Different Types of Attributes
Unit 2 Data Preprocessing
- Data Preprocessing: An Overview
- Data Cleaning
- Data Integration
- Data Reduction
- Data Transformation and Data Discretization
Unit 3 Correlation and Regression Analysis
- Introduction to Correlation
- Correlation Coefficients (Pearson and Spearman Rank)
- Introduction to SLR(Simple Linear Regression)
- SLR Model Building
- Estimation of Parameters Using OLS, Interpretation of SLR Coefficients
Unit 4 Classification
- Basic Concepts
- Decision Tree Induction
- Bayes Classification Methods
- Model Evaluation and Selection
- Techniques to Improve Classification Accuracy
- Bayesian Belief Networks
- Classification by Backpropagation
- Support Vector Machines
- Classification Using Frequent Patterns
- Lazy Learners (or Learning from Your Neighbors)
Unit 5 Clustering and Association
- Mining Frequent Patterns
- Associations, and Correlations: Basic Concepts and Methods
- Cluster Analysis
- Partitioning Methods
- Hierarchical Methods
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