1Part-1
54Part 2
2Data Science
55Big Data Analytics
3Part-2
56Chapter 1. Introduction To Big Data Analytics
4Big Data Analytics
571.1 What is big data?
5Part 1
581.2 Types of big data
6Part 1
591.3 Need and importance of big data
7Data Science
601.4 The five V’s of big data storage
8Chapter 1. Introduction to Data Science
611.5 Security challenges and solutions
91.1 What is data science?
621.6 MapReduce
101.2 Need for data science
631.7 Exercise
111.3 Scope of data science
64Chapter 2. Technologies For Analytics And Their Uses: Regression
121.4 Components of data science
652.1 Introduction
131.5 Application of data science
662.2 Terminologies utilized in regression analysis
141.6 Exercise
672.3 Types of regression
15Chapter 2. Prerequisite
682.4 Exercise
162.1 Machine learning
69Chapter 3. Decision Tree
172.2 Statistics
703.1 Introduction
182.3 Databases
713.2 Types of decision tree
192.4 Exercise
723.3 Method used
20Chapter 3. Data Science Life Cycle
733.4 Example
213.1 What is a life cycle?
743.5 Exercise
223.2 Why is the life cycle important?
75Chapter 4. Clustering
233.3 Job roles in the industry
764.1 Introduction
243.4 Exercise
774.2 Algorithms
25Chapter 4. Component 1 - Statistics
784.3 K means of clustering
264.1 Introduction to R
794.3 Exercise
274.2 Descriptive statistics in R
80Chapter 5. Naive Bayes
284.3 Descriptive Analysis in R
815.1 Introduction
294.4 Inferential Analysis in R
825.2 Formula
30 4.5 Exercise
835.3 Example
31Chapter 5. Component 2 – Domain Expertise
845.4 Exercise
325.1 Introduction
85Chapter 6. Association Rules
335.2 Use of domains
866.1 Introduction
345.3 Exercise
876.2 Apriori Algorithm
35Chapter 6. Component 3 - Data Engineering
886.3 Applications of Apriori Algorithm
366.1 Introduction
896.4 Exercise
376.2 Role of Data Engineering
90Chapter 7. Technologies: Hadoop
386.3 Cloud Engineering
917.1 Introduction to Hadoop
396.4 Exercise
927.2 Hadoop Distributed file system
40Chapter 7. Component 4 – Visualization
937.3 Some common frameworks of Hadoop
417.1 Introduction
947.4 Used cases
427.2 Need and benefits of visualization
957.5 Hadoop Ecosystem
437.3 Exercise
967.6 Exercise
44Chapter 8. Component 5 - Machine Learning
97Chapter 8. Time Series Analysis
458.1 What Is Machine Learning?
988.1 Introduction
468.2 The Importance Of Machine Learning
998.2 Auto-Regressive Model
478.3 AI – Artificial intelligence
1008.3 Moving Average
488.4 Applications of AI
1018.4 Autocorrelation
498.5 Categorization of AI
1028.5 Modelling series data
508.6 Special Considerations
1038.6 Exercise
518.7 ML v/s Data Science
104Glossary
528.8 Exercise
105Index
53Part 2
