EXPLORATORY DATA ANALYSIS (Professional Elective – V) B.Tech. IV Year I Sem. JNTUH R-18

• Explain the different stages of the data analytics lifecycle and discuss the role of EDA in the process.

• Compare and contrast different definitions and motivations for Exploratory Data Analysis.

• Identify and describe the various steps involved in a comprehensive data exploration process.

• Analyze the importance of understanding different data types and their potential impact on analysis.

Unit II: Preprocessing - Traditional Methods and Maximum Likelihood Estimation

• Discuss the challenges posed by missing data in real-world datasets and analyze different traditional methods for handling them (e.g., mean/median imputation, deletion).

• Explain the concept of Maximum Likelihood Estimation and its application in missing data imputation.

• Compare and contrast the advantages and limitations of Traditional and Bayesian methods for dealing with missing data.

• Analyze the potential benefits of improving the accuracy of data analysis through effective preprocessing techniques.

Unit III: Data Summarization and Visualization

• Describe different statistical methods for summarizing and analyzing one-dimensional, two-dimensional, and multi-dimensional data.

• Create and interpret various graphical representations of data like histograms, boxplots, scatterplots, and heatmaps.

• Discuss the importance of selecting appropriate visuals based on the type of data and the desired insights.

• Analyze real-world datasets and utilize statistical summaries and visualizations to understand their key characteristics.

Unit IV: Outlier Analysis and Feature Subset Selection

• Explain the concept of outliers and their potential impact on data analysis.

• Discuss different outlier detection methods like extreme value analysis, clustering-based, distance-based, and density-based approaches.

• Analyze the challenges of identifying outliers in categorical data and discuss appropriate techniques for addressing them.

• Compare and contrast different feature selection algorithms like filter methods, wrapper methods, and embedded methods.

• Implement common feature selection algorithms like forward selection, backward elimination, and Relief on practical datasets.

Unit V: Dimensionality Reduction

• Explain the concept of dimensionality reduction and its benefits for data analysis and visualization.

• Analyze the principles and steps involved in Principal Component Analysis (PCA) and discuss its limitations.

• Compare and contrast PCA with other dimensionality reduction techniques like Kernel PCA, Canonical Correlation Analysis, and Factor Analysis.

• Apply dimensionality reduction techniques like PCA to real-world datasets and evaluate their effectiveness in simplifying data structures for further analysis.