- Python Data Science Cookbook
- Table of Contents
- Python Data Science Cookbook
- Credits
- About the Author
- About the Reviewer
- www.PacktPub.com
- Preface
- 1. Python for Data Science
- Introduction
- Using dictionary objects
- Working with a dictionary of dictionaries
- Working with tuples
- Using sets
- Writing a list
- Creating a list from another list - list comprehension
- Using iterators
- Generating an iterator and a generator
- Using iterables
- Passing a function as a variable
- Embedding functions in another function
- Passing a function as a parameter
- Returning a function
- Altering the function behavior with decorators
- Creating anonymous functions with lambda
- Using the map function
- Working with filters
- Using zip and izip
- Processing arrays from the tabular data
- Preprocessing the columns
- Sorting lists
- Sorting with a key
- Working with itertools
- 2. Python Environments
- 3. Data Analysis – Explore and Wrangle
- Introduction
- Analyzing univariate data graphically
- Grouping the data and using dot plots
- Using scatter plots for multivariate data
- Using heat maps
- Performing summary statistics and plots
- Using a box-and-whisker plot
- Imputing the data
- Performing random sampling
- Scaling the data
- Standardizing the data
- Performing tokenization
- Removing stop words
- Stemming the words
- Performing word lemmatization
- Representing the text as a bag of words
- Calculating term frequencies and inverse document frequencies
- 4. Data Analysis – Deep Dive
- 5. Data Mining – Needle in a Haystack
- 6. Machine Learning 1
- 7. Machine Learning 2
- 8. Ensemble Methods
- 9. Growing Trees
- 10. Large-Scale Machine Learning – Online Learning
- Index
Bastiaan Sjardin is a data scientist and entrepreneur with a background in artificial intelligence, mathematics, and machine learning. He has an MSc degree in cognitive science and mathematical statistics from the University of Leiden. In the past 5 years, he has worked on a wide range of data science projects. He is a frequent community TA at Coursera in the social network analysis course from the University of Michigan and the practical machine learning course from Johns Hopkins University. His programming language of choice is R and Python. Currently, he is the cofounder of Quandbee (www.quandbee.com), a company specializing in machine learning applications.
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