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Decode Data Analytics for JNTU-H 16 Course (IV - II-CSE - CS853PE)

SKU: 9789389750751
I. A. DHOTRE ISBN 9789389750751 Buy Kindle Edition Buy Printed Book
₹ 80.00
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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 and Processing. (Chapter - 1) UNIT - II Data Analytics : Introduction to Analytics, Introduction to Tools and Environment, Application of Modeling in Business, Databases and Types of Data and variables, Data Modeling Techniques, Missing Imputations etc. Need for Business Modeling. (Chapter - 2) 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. (Chapter - 3) 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. (Chapter - 4) UNIT - V Data Visualization : Pixel-Oriented Visualization Techniques, Geometric Projection Visualization Techniques, Icon-Based Visualization Techniques, Hierarchical Visualization Techniques, Visualizing Complex Data and Relations. (Chapter - 5)

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 and Processing. (Chapter - 1) UNIT - II Data Analytics : Introduction to Analytics, Introduction to Tools and Environment, Application of Modeling in Business, Databases and Types of Data and variables, Data Modeling Techniques, Missing Imputations etc. Need for Business Modeling. (Chapter - 2) 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. (Chapter - 3) 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. (Chapter - 4) UNIT - V Data Visualization : Pixel-Oriented Visualization Techniques, Geometric Projection Visualization Techniques, Icon-Based Visualization Techniques, Hierarchical Visualization Techniques, Visualizing Complex Data and Relations. (Chapter - 5)

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