Fundamentals of Data Science

ISBN-13: 9781138336186 / Angielski / Twarda / 2021 / 282 str.

WAGH

Opis

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.

Spis treści

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 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 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 & Garch Dimensionality Reduction Python for Machine Learning Algorithms: KNN, Decision Tree, Random Forest, SVM Python IDEs for Data Science Summary Exercise References 　 Chapter 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 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 　 Chapter 9: Data Science Tool: MATLAB 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 　 Chapter 10 : GNU Octave as a Data Science Tool 10.1 Vectors and Matrices 10.2 Arithmetic Operations 10.3 Set Operations 10.4 Plotting Data10.5 Summary Exercise References Chapter 11: Data Visualization using Tableau 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