Fundamentals of Data Science

Part-I Data Science Introduction

1. Importance of Data Science

1. Need for Data Science
2. What is Data Science
3. Data Science Process
4. Business Intelligence and Data Science
5. Prerequisite for Data Scientist
6. Components of Data Science
7. Tools and Skills Need
8. Summary

Exercise

References

1. Statistics and Probability

2.1 Data Types

2.2. Variable Types

2.3 Statistics

2.4 Sampling Techniques and Probability

2.5 Information Gain and Entropy

2.6 Probability Theory

2.7 Probability Types

2.8 Probability Distribution

2.9 Bayes Theorem

2.10 Inferential Statistics

2.11 Summary

Exercise

References

3.1 SQL-Tool for Data Science

3.1.1 Basic Statistics with SQL

3.1.2 Data Munging with SQL

3.1.3 Filtering, Joins and Aggregation

3.1.4 Window Functions and Ordered Data

3.1.5 Preparing Data for Analytics Tool

3.2 NoSQL for Data Science

3.2.1 Why NoSQL

3.2.2 Document databases for Data Science

3.2.3 Wide-Column Databases for Data Science

3.2.4 Graph Databases for Data Science

3.3 Summary

Exercise

References

4.1 Analytics for Data Science

4.2 Data Analytics Examples

4.3 Data Analytics Life Cycle

4.3.1 Data Discovery

4.3.2 Data preparation

4.3.3 Model Planning

4.3.4 Model Building

4.3.5 Communicate Results

4.3.6 Operationalization

4.4 Summary

Exercise

References

5.1 Regression Analysis

5.1.1 Linear Regression

5.1.2 Logistic Regression

5.1.3 Multinomial Logistic Regression

5.1.4 Time Series Models

5.2 Machine Learning

5.2.1 Decision Trees

5.2.2 Naïve Bayes

5.2.3 Support Vector Machines

5.2.4 Nearest Neighbour learning

5.2.5 Clustering

5.2.6 Confusion Matrix

5.3 Summary

Exercise

References

6.1 Text Mining

6.1.1 Major Text Mining Areas

6.2 Text Analytics

6.2.1 Text Analysis Subtasks

6.2.2 Basic Text Analysis Steps

6.3 Natural Language Processing

6.3.1 Major Components of NLP

6.3.2 Stages of NLP

6.3.3 Statistical Processing of Natural Language

6.3.4 Applications of NLP

6.4 Summary

Exercise

References

1. Basics Of Python
2. Python libraries: Data Frame Manipulation with Pandas, Numpy
3. Data Analysis Exploration With Python
4. Time Series Data
5. Clustering with Python
6. Arch & Garch
7. Dimensionality Reduction
8. Python for Machine Learning
9. Algorithms: KNN, Decision Tree, Random Forest, SVM
10. Python IDEs for Data Science
11. Summary

Exercise

References

8.1 Reading and Getting Data into R

8.1.1 Reading Data into R

8.1.2 Writing Data into File

8.1.3 Scan() function

8.1.4 Built-in Datasets

8.2 Ordered and Unordered Factors

8.3 Arrays and Matrices

8.3.1 Arrays

8.3.2 Matrices

8.4 Lists and Data Frames

8.4.1 Lists

8.4.2 Data Frames

8.5 Probability Distributions

8.5.1 Normal Distribution

8.6 Statistical Models in R

8.6.1 Model Fitting

8.6.2 Marginal Effects

8.7 Manipulating Objects

8.7.1 Viewing Objects

8.7.2 Modifying Objects

8.7.3 Appending Elements

8.7.4 Deleting Objects

8.8 Data Distribution

8.8.1 Visualizing Distributions

8.8.2 Statistics in Distributions

8.9 Summary

Exercise

References

9.1 Data Science Workflow and MATLAB

9.2 Importing Data

9.2.1 How Data is stored

9.2.2 How MATLAB Represents Data

9.2.3 MATLAB Data Types

9.2.4 Automating the Import Process

9.3 Visualizing and Filtering Data

9.3.1 Plotting Data Contained in Tables

9.3.2 Selecting Data from Tables

9.3.3 Accessing and Creating Table Variables

9.4 Performing Calculations

9.4.1 Basic Mathematical Operations

9.4.2 Using Vectors

9.4.3 Using Functions

9.4.4 Calculating Summary Statistics

9.4.5 Correlations between Variables

9.4.6 Accessing Subsets of Data

9.4.7 Performing Calculations by Category

9.5 Summary

Exercise

References

10.1 Vectors and Matrices

10.2 Arithmetic Operations

10.3 Set Operations

10.4 Plotting Data
10.5 Summary

Exercise

References

11.1 Introduction to Data Visualization

11.2 Tableau Basics

11.3 Dimensions, Measures and Descriptive Statistics

11.4 Basic Charts

11.5 Dashboard Design & Principles

11.6 Special Chart Types

11.7 Integrate Tableau with Google Sheets

11.8 Summary

Exercise

References

Index

Biography

Sanjeev J. Wagh, Manisha S. Bhende, Anuradha D. Thakare