Preface

Chapter 1��Getting Started
Installing Enthought Canopy
Giving the installation a test run
If you occasionally get problems opening your IPNYB files
Using and understanding IPython��Jupyter��Notebooks
Python basics-Part 1
Understanding Python code
Importing modules
Data structures
Experimenting with Iists
Pre colon
Post colon
Negative syntax
Adding list to list
The append function
Complex data structures
Dereferencing a single element
The sort function
Reverse sort
Tuples
Dereferencing an element
List of tuples
Dictionaries
lterating through entries
Python basics-Part 2
Functions in Python
Lambda functions-functional programming
Understanding boolean expressions
The if statement
The if-else loop
Looping
The while loop
Exploring activity
Running Python scripts
More options than just the lPython��Jupyter Notebook
Running Python scripts in command prompt
Using the Canopy I DE
Summary

Chapter 2��Statistics and Probability Refresher��and Python Practice
Types of data
NumericaI data
Discrete data
Continuous data
Categorical data
OrdinaI data
Mean��median��and mode
Mean
Median
The factor of outliers
Mode
Using mean��median��and mode in Python
Calculating mean using the NumPy package
Visualizing data using matplotlib
Calculating median using the NumPy package
Analyzing the effect of outliers
Calculating mode using the SciPy package
Some exercises
Standard deviation and variance
Variance
Measuring variance
Standard deviation
Identifying outliers with standard deviation
Population variance versus sample variance
The Mathematical explanation
Analyzing standard deviation and variance on a histogram
Using Python to compute standard deviation and variance
Try it yourself
Probability density function and probability mass function
The probability density function and probability mass functions
Probability density functions
Probability mass functions
Types of data distributions
Uniform distribution
Normal or Gaussian distribution
The exponential probability distribution or Power law
Binomial probability mass function
Poisson probability mass function
����

Chapter 3��Matplotlib and Advanced Probability Concepts
ChantAr 4��Predictive ModeIs
Chapter 5��Machine Learning with Pvthon
Chapter 6��Recommender Systems
Chapter 7��More Data Mininq and Machine Learninq Techniaues
ChaDter 8��Dealing with Real��World Data
Chapter 9��Apache Spark-Machine Learning on Big Data
Chapter 10��Testing and Experimental Design

Index