Fundamentals of Data Science
- Length: 296 pages
- Edition: 1
- Language: English
- Publisher: Chapman and Hall/CRC
- Publication Date: 2021-09-28
- ISBN-10: 1138336181
- ISBN-13: 9781138336186
- Sales Rank: #0 (See Top 100 Books)
Fundamentals of Data Science is designed for students, academicians and practitioners with a complete walkthrough right from the foundational groundwork required to outlining all the concepts, techniques and tools required to understand Data Science.
Data Science is an umbrella term for the non-traditional techniques and technologies that are required to collect, aggregate, process, and gain insights from massive datasets. This book offers all the processes, methodologies, various steps like data acquisition, pre-process, mining, prediction, and visualization tools for extracting insights from vast amounts of data by the use of various scientific methods, algorithms, and processes
Readers will learn the steps necessary to create the application with SQl, NoSQL, Python, R, Matlab, Octave and Tablue.
This book provides a stepwise approach to building solutions to data science applications right from understanding the fundamentals, performing data analytics to writing source code. All the concepts are discussed in simple English to help the community to become Data Scientist without much pre-requisite knowledge.
Features :
- Simple strategies for developing statistical models that analyze data and detect patterns, trends, and relationships in data sets.
- Complete roadmap to Data Science approach with dedicatedsections which includes Fundamentals, Methodology and Tools.
- Focussed approach for learning and practice various Data Science Toolswith Sample code and examples for practice.
- Information is presented in an accessible way for students, researchers and academicians and professionals.
Cover Half Title Title Page Copyright Page Table of Contents Preface Authors Part I: Introduction to Data Science 1 Importance of Data Science 1.1 Need for Data Science 1.2 What Is Data Science? 1.3 Data Science Process 1.4 Business Intelligence and Data Science 1.5 Prerequisites for a Data Scientist 1.6 Components of Data Science 1.7 Tools and Skills Needed 1.8 Summary References 2 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 Functions 2.9 Bayes’ Theorem 2.10 Inferential Statistics 2.11 Summary References 3 Databases for Data Science 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 Advanced 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 References Part II: Data Modeling and Analytics 4 Data Science Methodology 4.1 Analytics for Data Science 4.2 Examples of Data Analytics 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 References 5 Data Science Methods and Machine Learning 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 Neighbor learning 5.2.5 Clustering 5.2.6 Confusion Matrix 5.3 Summary References 6 Data Analytics and Text Mining 6.1 Text Mining 6.1.1 Major Text Mining Areas 6.1.1.1 Information Retrieval 6.1.1.2 Data Mining 6.1.1.3 Natural Language Processing (NLP) 6.2 Text Analytics 6.2.1 Text Analysis Subtasks 6.2.1.1 Cleaning and Parsing 6.2.1.2 Searching and Retrieval 6.2.1.3 Text Mining 6.2.1.4 Part-of-Speech Tagging 6.2.1.5 Stemming 6.2.1.6 Lemmatization 6.2.2 Basic Text Analysis Steps 6.3 Introduction to Natural Language Processing 6.3.1 Major Components of NLP 6.3.2 Stages of NLP 6.3.3 Statistical Processing of Natural Language 6.3.3.1 Document Preprocessing 6.3.3.2 Parameterization 6.3.4 Applications of NLP 6.4 Summary References Part III: Platforms for Data Science 7 Data Science Tool: Python 7.1 Basics of Python for Data Science 7.2 Python Libraries: DataFrame Manipulation with pandas and NumPy 7.3 Exploration Data Analysis with Python 7.4 Time Series Data 7.5 Clustering with Python 7.6 ARCH and GARCH 7.7 Dimensionality Reduction 7.8 Python for Machine ML 7.9 KNN/Decision Tree/ Random Forest/SVM 7.10 Python IDEs for Data Science 7.11 Summary References 8 Data Science Tool: R 8.1 Reading and Getting Data into R 8.1.1 Reading Data into R 8.1.2 Writing Data into Files 8.1.3 scan() Function 8.1.4 Built-in Data Sets 8.2 Ordered and Unordered Factors 8.3 Arrays and Matrices 8.3.1 Arrays 8.3.1.1 Creating an Array 8.3.1.2 Accessing Elements in an Array 8.3.1.3 Array Manipulation 8.3.2 Matrices 8.3.2.1 Creating a Matrix 8.3.2.2 Matrix Transpose 8.3.2.3 Eigenvalues and Eigenvectors 8.3.2.4 Matrix Concatenation 8.4 Lists and Data Frames 8.4.1 Lists 8.4.1.1 Creating a List 8.4.1.2 Concatenation of Lists 8.4.2 Data Frames 8.4.2.1 Creating a Data Frame 8.4.2.2 Accessing the Data Frame 8.4.2.3 Adding Rows and Columns 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 References 9 Data Science Tool: MATLAB 9.1 Data Science Workflow with 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 References 10 GNU Octave as a Data Science Tool 10.1 Vectors and Matrices 10.2 Arithmetic Operations 10.3 Set Operations 10.4 Plotting Data 10.5 Summary References 11 Data Visualization Using Tableau 11.1 Introduction to Data Visualization 11.2 Introduction to Tableau 11.3 Dimensions and Measures, 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 References Index
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