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Data Analytics

₹243.00
₹270.00
Tax excluded
1987
50 Items

Specific References

According to New Revised CBCS Syllabus w.e.f. 2021-22

T.Y. B.Sc. (Computer Science)

Semesters-VI

A Text book of

Data Analytics

Author : Dr. Poonam Ponde

ISBN: 978-93-90646-06-7

Price: 250/-

This book on Data Analytics, covering the syllabus of Semester VI of T.Y. B.Sc. (Computer Science), is authored by an eminent educationist, in a simple and easy-to-follow style. It is ideally suited to helps students prepare for the exams and is replete with explanations, examples and self-test questions. Theoretical concepts are explained via simple language and diagrams. Program examples help explain practical aspects of the subject. The book also contains solved programs with outputs to further help the students grasp the theory and practice of Data Analytics.

Quantity
In Stock

CONTENTS

1.Introduction to Data Analytics

1. Introduction

2. Concept of Data Analytics

3. Data Analysis vs. Data Analytics

4. Types of Analytics

4.1 Descriptive Analysis

4.2 Diagnostic Analytics

4.3 Predictive Analytics

4.4 Prescriptive Analytics

4.5 Exploratory Analysis

4.6 Mechanistic Analysis

5. Mathematical Models - Concept

5.1 Model Evaluation

5.2 Metrics for Evaluating Classifiers

6. ROC (Receiver-Operating Characteristic) Curves

7. Evaluating Value Prediction Models

2.Machine Learning Overview

1. Introduction

2. Introduction to Machine Learning, Deep Learning, Artificial Intelligence

2.1  Artificial Intelligence

2.2  Machine Learning

2.3 Deep Learning

2.4 Applications for Machine Learning in Data Science

3. The Modeling Process

4. Regression Models

4.1 Types of Regression

5. Concept of Classification

6. Concept of Clustering

3.Mining Frequent Patterns, Associations, and Correlations

1. Introduction to Data Mining

1.1 Knowledge Discovery Process

2. What kinds of data can be mined?

3. What kinds of patterns can be mined?

3.1 Class/Concept Description: Characterization and Discrimination

3.2 Mining Frequent Patterns, Associations, and Correlations

3.3 Classification and Regression for Predictive Analysis

3.4 Cluster Analysis

3.5 Outlier Analysis

4. Mining Frequent Patterns

4.1 Market Basket Analysis

5. Frequent Itemsets, Closed Itemsets, and Association Rules

5.1 Closed and Maximal Frequent Itemsets

6. Frequent Itemset Mining Methods

6.1 Apriori Algorithm

7. Python Implementation of Apriori Algorithm

8. Generating Association Rules from Frequent Itemsets

8.1 Python Implementation of Generating Association Rules

8.2 Improving the Efficiency of Apriori Algorithm

8.3 Frequent Pattern Growth (FP-Growth) Algorithm

8.4 Python Implementation of FP Growth Algorithm

4.Social Media and Text Analytics

1. Introduction

2. Social Media Analytics Process

3. Seven layers of Social Media Analytics

4. Accessing Social Media Data

5. Key Social Media Analytics Methods

5.1 Social Network Analysis

6. Introduction to Natural Language Processing

6.1 Text Analytics

7. Sentiment Analysis

8. Document or Text Summarization

8.1 Trend Analytics

9. Challenges to Social Media Analytics

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