JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Pre-requisites:

• 1. A course on “Database Management Systems”.
• 2. Knowledge of probability and statistics.

Course Objectives:

• 1. To explore the fundamental concepts of data analytics.
• 2. To learn the principles and methods of statistical analysis
• 3. Discover interesting patterns, analyze supervised and unsupervised models and estimate the accuracy of the algorithms.
• 4. To understand the various search methods and visualization techniques.

Course Outcomes

After completion of this course students will be able to

• 1. Understand the impact of data analytics for business decisions and strategy
• 2. Carry out data analysis/statistical analysis
• 3. To carry out standard data visualization and formal inference procedures
• 4. Design Data Architecture
• 5. Understand various Data Sources

UNIT - I

Data Management: Design Data Architecture and manage the data for analysis, understand various sources of Data like Sensors/Signals/GPS etc. Data Management, Data Quality (noise, outliers, missing values, duplicate data) and Data Processing & Processing.

UNIT - II

Data Analytics: Introduction to Analytics, Introduction to Tools and Environment, Application of Modeling in Business, Databases & Types of Data and variables, Data Modeling Techniques, Missing Imputations etc. Need for Business Modeling.

UNIT - III

Regression: Concepts, Blue property assumptions, Least Square Estimation, Variable Rationalization, and Model Building etc. Logistic Regression: Model Theory, Model fit Statistics, Model Construction, Analytics applications to various Business Domains etc.

UNIT - IV

Object Segmentation: Regression Vs Segmentation – Supervised and Unsupervised Learning, Tree Building – Regression, Classification, Overfitting, Pruning and Complexity, Multiple Decision Trees etc. Time Series Methods: Arima, Measures of Forecast Accuracy, STL approach, Extract features from generated model as Height, Average Energy etc and Analyze for prediction

UNIT - V

Data Visualization: Pixel-Oriented Visualization Techniques, Geometric Projection Visualization Techniques, Icon-Based Visualization Techniques, Hierarchical Visualization Techniques, Visualizing Complex Data and Relations.

TEXT BOOKS:

• 1. Student’s Handbook for Associate Analytics – II, III.
• 2. Data Mining Concepts and Techniques, Han, Kamber, 3rd Edition, Morgan Kaufmann Publishers.

REFERENCES:

• 1. Introduction to Data Mining, Tan, Steinbach and Kumar, Addison Wesley, 2006.
• 2. Data Mining Analysis and Concepts, M. Zaki and W. Meira
• 3. Mining of Massive Datasets, Jure Leskovec Stanford Univ. Anand Rajaraman Milliway Labs Jeffrey D Ullman Stanford Univ.