Preface
Data Science - Overview - 10
Chapter 1: Getting Started with Raw Data - 19
The world of arrays with NumPy
Empowering data analysis with Pandas
Data cleansing
Data operations
Chapter 2: Inferential Statistics - 37
Various forms of distribution
A z-score
A p-value
One-tailed and two-tailed tests
Chapter 3: Finding a Needle in a Haystack - 61
What is data mining?
Chapter 4: Making Sense of Data through - 73
Advanced Visualization
Controlling the line properties of a chart
Creating multiple plots
Playing with text
Chapter 5: Uncovering Machine Learning - 97
Different types of Machine Learning
Chapter 6: Performing Predictions with a Linear Regression - 108
Simple linear regression
Multiple regression
Chapter 7: Estimating the Likelihood of Events - 121
Logistic regression
Model building and evaluation with SciKit
Chapter 8: Generating Recommendations with
Collaborative Filtering - 133
Recommendation data
User-based collaborative filtering
Chapter 9: Pushing Boundaries with Ensemble Models - 144
The census income dataset
Decision trees
Random forests
Chapter 10: Applying Segmentation with k-means Clustering - 159
The k-means algorithm and its working
Chapter 11: Analyzing Unstructured Data with Text Mining - 178
Preprocessing data
Creating a wordcloud
Word and sentence tokenization
Chapter 12: Leveraging Python in the World of Big Data - 188
What is Hadoop?
Python MapReduce
File handling with Hadoopy
Pig
Python with Apache Spark
Summary