图书简介
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 processesReaders 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.
Part-I Data Science Introduction Importance of Data Science Need for Data Science What is Data Science Data Science Process Business Intelligence and Data Science Prerequisite for Data Scientist Components of Data Science Tools and Skills Need Summary Exercise References 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. 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 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 Part II Data Modelling and Analytics Chapter 4: Data Science Methodology 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 Chapter 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 Naive 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 Chapter 6: Data Analytics and Text Mining 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 Part III: Platforms for Data Science Chapter 7: Data Science Tool: Python Basics Of Python Python libraries: Data Frame Manipulation with Pandas, Numpy Data Analysis Exploration With Python Time Series Data Clustering with Python Arch
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