Book description
Unleash the power of Python and its robust data science capabilities
About This Book
 Unleash the power of Python 3 objects
 Learn to use powerful Python libraries for effective data processing and analysis
 Harness the power of Python to analyze data and create insightful predictive models
 Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics
Who This Book Is For
Entrylevel analysts who want to enter in the data science world will find this course very useful to get themselves acquainted with Python's data science capabilities for doing realworld data analysis.
What You Will Learn
 Install and setup Python
 Implement objects in Python by creating classes and defining methods
 Get acquainted with NumPy to use it with arrays and arrayoriented computing in data analysis
 Create effective visualizations for presenting your data using Matplotlib
 Process and analyze data using the time series capabilities of pandas
 Interact with different kind of database systems, such as file, disk format, Mongo, and Redis
 Apply data mining concepts to realworld problems
 Compute on big data, including realtime data from the Internet
 Explore how to use different machine learning models to ask different questions of your data
In Detail
The Python: RealWorld Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you'll have gained key skills and be ready for the material in the next module.
The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it's time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and exampledriven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls.
Style and approach
This course includes all the resources that will help you jump into the data science field with Python and learn how to make sense of data. The aim is to create a smooth learning path that will teach you how to get started with powerful Python libraries and perform various data science techniques in depth.
Table of contents

Python: RealWorld Data Science
 Table of Contents
 Python: RealWorld Data Science
 Meet Your Course Guide
 What's so cool about Data Science?
 Course Structure
 Course Journey
 The Course Roadmap and Timeline

1. Course Module 1: Python Fundamentals

1. Introduction and First Steps – Take a Deep Breath
 A proper introduction
 Enter the Python
 About Python
 What are the drawbacks?
 Who is using Python today?
 Setting up the environment
 What you need for this course
 How you can run a Python program
 How is Python code organized
 Python's execution model
 Guidelines on how to write good code
 The Python culture
 A note on the IDEs
 2. Objectoriented Design
 3. Objects in Python
 4. When Objects Are Alike
 5. Expecting the Unexpected
 6. When to Use Objectoriented Programming
 7. Python Data Structures
 8. Python Objectoriented Shortcuts
 9. Strings and Serialization
 10. The Iterator Pattern
 11. Python Design Patterns I
 12. Python Design Patterns II
 13. Testing Objectoriented Programs
 14. Concurrency

1. Introduction and First Steps – Take a Deep Breath

2. Course Module 2: Data Analysis
 1. Introducing Data Analysis and Libraries
 2. NumPy Arrays and Vectorized Computation
 3. Data Analysis with pandas
 4. Data Visualization
 5. Time Series
 6. Interacting with Databases
 7. Data Analysis Application Examples

3. Course Module 3: Data Mining
 1. Getting Started with Data Mining
 2. Classifying with scikitlearn Estimators
 3. Predicting Sports Winners with Decision Trees
 4. Recommending Movies Using Affinity Analysis
 5. Extracting Features with Transformers
 6. Social Media Insight Using Naive Bayes
 7. Discovering Accounts to Follow Using Graph Mining
 8. Beating CAPTCHAs with Neural Networks
 9. Authorship Attribution
 10. Clustering News Articles
 11. Classifying Objects in Images Using Deep Learning
 12. Working with Big Data

13. Next Steps…
 Chapter 1 – Getting Started with Data Mining
 Chapter 2 – Classifying with scikitlearn Estimators
 Chapter 3: Predicting Sports Winners with Decision Trees
 Chapter 4 – Recommending Movies Using Affinity Analysis
 Chapter 5 – Extracting Features with Transformers
 Chapter 6 – Social Media Insight Using Naive Bayes
 Chapter 7 – Discovering Accounts to Follow Using Graph Mining
 Chapter 8 – Beating CAPTCHAs with Neural Networks
 Chapter 9 – Authorship Attribution
 Chapter 10 – Clustering News Articles
 Chapter 11 – Classifying Objects in Images Using Deep Learning
 Chapter 12 – Working with Big Data
 More resources

4. Course Module 4: Machine Learning

1. Giving Computers the Ability to Learn from Data
 How to transform data into knowledge
 The three different types of machine learning
 An introduction to the basic terminology and notations
 A roadmap for building machine learning systems
 Using Python for machine learning
 2. Training Machine Learning Algorithms for Classification
 3. A Tour of Machine Learning Classifiers Using scikitlearn
 4. Building Good Training Sets – Data Preprocessing
 5. Compressing Data via Dimensionality Reduction
 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
 7. Combining Different Models for Ensemble Learning

8. Predicting Continuous Target Variables with Regression Analysis
 Introducing a simple linear regression model
 Exploring the Housing Dataset
 Implementing an ordinary least squares linear regression model
 Fitting a robust regression model using RANSAC
 Evaluating the performance of linear regression models
 Using regularized methods for regression
 Turning a linear regression model into a curve – polynomial regression

A. Reflect and Test Yourself! Answers
 Module 2: Data Analysis

Module 3: Data Mining
 Chapter 1: Getting Started with Data Mining
 Chapter 2: Classifying with scikitlearn Estimators
 Chapter 3: Predicting Sports Winners with Decision Trees
 Chapter 4: Recommending Movies Using Affinity Analysis
 Chapter 5: Extracting Features with Transformers
 Chapter 6: Social Media Insight Using Naive Bayes
 Chapter 7: Discovering Accounts to Follow Using Graph Mining
 Chapter 8: Beating CAPTCHAs with Neural Networks
 Chapter 9: Authorship Attribution
 Chapter 10: Clustering News Articles
 Chapter 11: Classifying Objects in Images Using Deep Learning
 Chapter 12: Working with Big Data

Module 4: Machine Learning
 Chapter 1: Giving Computers the Ability to Learn from Data
 Chapter 2: Training Machine Learning
 Chapter 3: A Tour of Machine Learning Classifiers Using scikitlearn
 Chapter 4: Building Good Training Sets – Data Preprocessing
 Chapter 5: Compressing Data via Dimensionality Reduction
 Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
 Chapter 7: Combining Different Models for Ensemble Learning
 Chapter 8: Predicting Continuous Target Variables with Regression Analysis
 B. Bibliography

1. Giving Computers the Ability to Learn from Data
 Index
Product information
 Title: Python: RealWorld Data Science
 Author(s):
 Release date: June 2016
 Publisher(s): Packt Publishing
 ISBN: 9781786465160
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