Introduction to Data Science. A Python Approach to Concepts, Techniques and Applications

Preface
	Subject Area of the Book
	Organization and Feature of the Book
	Target Audiences
	Previous Uses of the Materials
	Suggested Uses of the Book
	Supplemental Resources
	Acknowledgements
Contents
Authors and Contributors
1 Introduction to Data Science
	1.1 What is Data Science?
	1.2 About This Book
2 Toolboxes for Data Scientists
	2.1 Introduction
	2.2 Why Python?
	2.3 Fundamental Python Libraries for Data Scientists
		2.3.1 Numeric and Scientific Computation: NumPy and SciPy
		2.3.2 SCIKIT-Learn: Machine Learning in Python
		2.3.3 PANDAS: Python Data Analysis Library
	2.4 Data Science Ecosystem Installation
	2.5 Integrated Development Environments (IDE)
		2.5.1 Web Integrated Development Environment (WIDE): Jupyter
	2.6 Get Started with Python for Data Scientists
		2.6.1 Reading
		2.6.2 Selecting Data
		2.6.3 Filtering Data
		2.6.4 Filtering Missing Values
		2.6.5 Manipulating Data
		2.6.6 Sorting
		2.6.7 Grouping Data
		2.6.8 Rearranging Data
		2.6.9 Ranking Data
		2.6.10 Plotting
	2.7 Conclusions
3 Descriptive Statistics
	3.1 Introduction
	3.2 Data Preparation
		3.2.1 The Adult Example
	3.3 Exploratory Data Analysis
		3.3.1 Summarizing the Data
		3.3.2 Data Distributions
		3.3.3 Outlier Treatment
		3.3.4 Measuring Asymmetry: Skewness and Pearson\'s Median Skewness Coefficient
		3.3.5 Continuous Distribution
		3.3.6 Kernel Density
	3.4 Estimation
		3.4.1 Sample and Estimated Mean, Variance and Standard Scores
		3.4.2 Covariance, and Pearson\'s and Spearman\'s Rank Correlation
	3.5 Conclusions
4 Statistical Inference
	4.1 Introduction
	4.2 Statistical Inference: The Frequentist Approach
	4.3 Measuring the Variability in Estimates
		4.3.1 Point Estimates
		4.3.2 Confidence Intervals
	4.4 Hypothesis Testing
		4.4.1 Testing Hypotheses Using Confidence Intervals
		4.4.2 Testing Hypotheses Using p-Values
	4.5 But Is the Effect E Real?
	4.6 Conclusions
5 Supervised Learning
	5.1 Introduction
	5.2 The Problem
	5.3 First Steps
	5.4 What Is Learning?
	5.5 Learning Curves
	5.6 Training, Validation and Test
	5.7 Two Learning Models
		5.7.1 Generalities Concerning Learning Models
		5.7.2 Support Vector Machines
		5.7.3 Random Forest
	5.8 Ending the Learning Process
	5.9 A Toy Business Case
	5.10 Conclusion
6 Regression Analysis
	6.1 Introduction
	6.2 Linear Regression
		6.2.1 Simple Linear Regression
		6.2.2 Multiple Linear Regression and Polynomial Regression
		6.2.3 Sparse Model
	6.3 Logistic Regression
	6.4 Conclusions
7 Unsupervised Learning
	7.1 Introduction
	7.2 Clustering
		7.2.1 Similarity and Distances
		7.2.2 What Constitutes a Good Clustering? Defining Metrics  to Measure Clustering Quality
		7.2.3 Taxonomies of Clustering Techniques
	7.3 Case Study
	7.4 Conclusions
8 Network Analysis
	8.1 Introduction
	8.2 Basic Definitions in Graphs
	8.3 Social Network Analysis
		8.3.1 Basics in NetworkX
		8.3.2 Practical Case: Facebook Dataset
	8.4 Centrality
		8.4.1 Drawing Centrality in Graphs
		8.4.2 PageRank
	8.5 Ego-Networks
	8.6 Community Detection
	8.7 Conclusions
9 Recommender Systems
	9.1 Introduction
	9.2 How Do Recommender Systems Work?
		9.2.1 Content-Based Filtering
		9.2.2 Collaborative Filtering
		9.2.3 Hybrid Recommenders
	9.3 Modeling User Preferences
	9.4 Evaluating Recommenders
	9.5 Practical Case
		9.5.1 MovieLens Dataset
		9.5.2 User-Based Collaborative Filtering
	9.6 Conclusions
10 Statistical Natural Language Processing for Sentiment Analysis
	10.1 Introduction
	10.2 Data Cleaning
	10.3 Text Representation
		10.3.1 Bi-Grams and n-Grams
	10.4 Practical Cases
	10.5 Conclusions
11 Parallel Computing
	11.1 Introduction
	11.2 Architecture
		11.2.1 Getting Started
		11.2.2 Connecting to the Cluster (The Engines)
	11.3 Multicore Programming
		11.3.1 Direct View of Engines
		11.3.2 Load-Balanced View of Engines
	11.4 Distributed Computing
	11.5 A Real Application: New York Taxi Trips
		11.5.1 A Direct View Non-Blocking Proposal
		11.5.2 Results
	11.6 Conclusions
Index