A Hands-On Introduction to Data Science

Contents
Preface
About the Author
Acknowledgments
Part I: Conceptual Introductions
1 Introduction
	1.1 What Is Data Science?
	1.2 Where Do We See Data Science?
		1.2.1 Finance
		1.2.2 Public Policy
		1.2.3 Politics
		1.2.4 Healthcare
		1.2.5 Urban Planning
		1.2.6 Education
		1.2.7 Libraries
	1.3 How Does Data Science Relate to Other Fields?
		1.3.1 Data Science and Statistics
		1.3.2 Data Science and Computer Science
		1.3.3 Data Science and Engineering
		1.3.4 Data Science and Business Analytics
		1.3.5 Data Science, Social Science, and Computational Social Science
	1.4 The Relationship between Data Science and Information Science
		1.4.1 Information vs. Data
		1.4.2 Users in Information Science
		1.4.3 Data Science in Information Schools (iSchools)
	1.5 Computational Thinking
	1.6 Skills for Data Science
	1.7 Tools for Data Science
	1.8 Issues of Ethics, Bias, and Privacy in Data Science
	Summary
	Key Terms
	Conceptual Questions
	Hands-On Problems
2 Data
	2.1 Introduction
	2.2 Data Types
		2.2.1 Structured Data
		2.2.2 Unstructured Data
		2.2.3 Challenges with Unstructured Data
	2.3 Data Collections
		2.3.1 Open Data
		2.3.2 Social Media Data
		2.3.3 Multimodal Data
		2.3.4 Data Storage and Presentation
	2.4 Data Pre-processing
		2.4.1 Data Cleaning
		2.4.2 Data Integration
		2.4.3 Data Transformation
		2.4.4 Data Reduction
		2.4.5 Data Discretization
	Summary
	Key Terms
	Conceptual Questions
	Hands-On Problems
	Further Reading and Resources
3 Techniques
	3.1 Introduction
	3.2 Data Analysis and Data Analytics
	3.3 Descriptive Analysis
		3.3.1 Variables
		3.3.2 Frequency Distribution
		3.3.3 Measures of Centrality
		3.3.4 Dispersion of a Distribution
	3.4 Diagnostic Analytics
		3.4.1 Correlations
	3.5 Predictive Analytics
	3.6 Prescriptive Analytics
	3.7 Exploratory Analysis
	3.8 Mechanistic Analysis
		3.8.1 Regression
	Summary
	Key Terms
	Conceptual Questions
	Hands-On Problems
	Further Reading and Resources
Part II: Tools for Data Science
4 UNIX
	4.1 Introduction
	4.2 Getting Access to UNIX
	4.3 Connecting to a UNIX Server
		4.3.1 SSH
		4.3.2 FTP/SCP/SFTP
	4.4 Basic Commands
		4.4.1 File and Directory Manipulation Commands
		4.4.2 Process-Related Commands
		4.4.3 Other Useful Commands
		4.4.4 Shortcuts
	4.5 Editing on UNIX
		4.5.1 The vi Editor
		4.5.2 The Emacs Editor
	4.6 Redirections and Piping
	4.7 Solving Small Problems with UNIX
	Summary
	Key Terms
	Conceptual Questions
	Hands-On Problems
	Further Reading and Resources
5 Python
	5.1 Introduction
	5.2 Getting Access to Python
		5.2.1 Download and Install Python
		5.2.2 Running Python through Console
		5.2.3 Using Python through Integrated Development Environment (IDE)
	5.3 Basic Examples
	5.4 Control Structures
	5.5 Statistics Essentials
		5.5.1 Importing Data
		5.5.2 Plotting the Data
		5.5.3 Correlation
		5.5.4 Linear Regression
		5.5.5 Multiple Linear Regression
	5.6 Introduction to Machine Learning
		5.6.1 What Is Machine Learning?
		5.6.2 Classification (Supervised Learning)
		5.6.3 Clustering (Unsupervised Learning)
		5.6.4 Density Estimation (Unsupervised Learning)
	Summary
	Key Terms
	Conceptual Questions
	Hands-On Problems
	Further Reading and Resources
6 R
	6.1 Introduction
	6.2 Getting Access to R
	6.3 Getting Started with R
		6.3.1 Basics
		6.3.2 Control Structures
		6.3.3 Functions
		6.3.4 Importing Data
	6.4 Graphics and Data Visualization
		6.4.1 Installing ggplot2
		6.4.2 Loading the Data
		6.4.3 Plotting the Data
	6.5 Statistics and Machine Learning
		6.5.1 Basic Statistics
		6.5.2 Regression
		6.5.3 Classification
		6.5.4 Clustering
	Summary
	Key Terms
	Conceptual Questions
	Hands-On Problems
	Further Reading and Resources
7 MySQL
	7.1 Introduction
	7.2 Getting Started with MySQL
		7.2.1 Obtaining MySQL
		7.2.2 Logging in to MySQL
	7.3 Creating and Inserting Records
		7.3.1 Importing Data
		7.3.2 Creating a Table
		7.3.3 Inserting Records
	7.4 Retrieving Records
		7.4.1 Reading Details about Tables
		7.4.2 Retrieving Information from Tables
	7.5 Searching in MySQL
		7.5.1 Searching within Field Values
		7.5.2 Full-Text Searching with Indexing
	7.6 Accessing MySQL with Python
	7.7 Accessing MySQL with R
	7.8 Introduction to Other Popular Databases
		7.8.1 NoSQL
		7.8.2 MongoDB
		7.8.3 Google BigQuery
	Summary
	Key Terms
	Conceptual Questions
	Hands-On Problems
	Further Reading and Resources
Part III: Machine Learning for Data Science
8 Machine Learning Introduction and Regression
	8.1 Introduction
	8.2 What Is Machine Learning?
	8.3 Regression
	8.4 Gradient Descent
	Summary
	Key Terms
	Conceptual Questions
	Hands-On Problems
	Further Reading and Resources
9 Supervised Learning
	9.1 Introduction
	9.2 Logistic Regression
	9.3 Softmax Regression
	9.4 Classification with kNN
	9.5 Decision Tree
		9.5.1 Decision Rule
		9.5.2 Classification Rule
		9.5.3 Association Rule
	9.6 Random Forest
	9.7 Naïve Bayes
	9.8 Support Vector Machine (SVM)
	Summary
	Key Terms
	Conceptual Questions
	Hands-On Problems
	Further Reading and Resources
10 Unsupervised Learning
	10.1 Introduction
	10.2 Agglomerative Clustering
	10.3 Divisive Clustering
	10.4 Expectation Maximization (EM)
	10.5 Introduction to Reinforcement Learning
	Summary
	Key Terms
	Conceptual Questions
	Hands-On Problems
	Further Reading and Resources
Part IV: Applications, Evaluations, and Methods
11 Hands-On with Solving Data Problems
	11.1 Introduction
	11.2 Collecting and Analyzing Twitter Data
	11.3 Collecting and Analyzing YouTube Data
	11.4 Analyzing Yelp Reviews and Ratings
	Summary
	Key Terms
	Conceptual Questions
	Practice Questions
12 Data Collection, Experimentation, and Evaluation
	12.1 Introduction
	12.2 Data Collection Methods
		12.2.1 Surveys
		12.2.2 Survey Question Types
		12.2.3 Survey Audience
		12.2.4 Survey Services
		12.2.5 Analyzing Survey Data
		12.2.6 Pros and Cons of Surveys
		12.2.7 Interviews and Focus Groups
		12.2.8 Why Do an Interview?
		12.2.9 Why Focus Groups?
		12.2.10 Interview or Focus Group Procedure
		12.2.11 Analyzing Interview Data
		12.2.12 Pros and Cons of Interviews and Focus Groups
		12.2.13 Log and Diary Data
		12.2.14 User Studies in Lab and Field
	12.3 Picking Data Collection and Analysis Methods
		12.3.1 Introduction to Quantitative Methods
		12.3.2 Introduction to Qualitative Methods
		12.3.3 Mixed Method Studies
	12.4 Evaluation
		12.4.1 Comparing Models
		12.4.2 Training–Testing and A/B Testing
		12.4.3 Cross-Validation
	Summary
	Key Terms
	Conceptual Questions
	Further Reading and Resources
Appendices Appendix A:Useful Formulas from Differential Calculus
	Further Reading and Resources
Appendix B:Useful Formulas from Probability
	Further Reading and Resources
Appendix C:Useful Resources
	C.1 Tutorials
	C.2 Tools
Appendix D:Installing and Configuring Tools
	D.1 Anaconda
	D.2 IPython (Jupyter) Notebook
	D.3 Spyder
	D.4 R
	D.5 RStudio
Appendix E:Datasets and Data Challenges
	E.1 Kaggle
	E.2 RecSys
	E.3 WSDM
	E.4 KDD Cup
Appendix F:Using Cloud Services
	F.1 Google Cloud Platform
	F.2 Hadoop
	F.3 Microsoft Azure
	F.4 Amazon Web Services (AWS)
Appendix G:Data Science Jobs
	G.1 Marketing
	G.2 Corporate Retail and Sales
	G.3 Legal
	G.4 Health and Human Services
Appendix H:Data Science and Ethics
	H.1 Data Supply Chain
	H.2 Bias and Inclusion
	H.3 Considering Best Practices and Codes of Conduct
Appendix I:Data Science for Social Good
Index