Book description
Understand, evaluate, and visualize data
About This Book
 Learn basic steps of data analysis and how to use Python and its packages
 A stepbystep guide to predictive modeling including tips, tricks, and best practices
 Effectively visualize a broad set of analyzed data and generate effective results
Who This Book Is For
This book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner.
What You Will Learn
 Get acquainted with NumPy and use arrays and arrayoriented computing in data analysis
 Process and analyze data using the timeseries capabilities of Pandas
 Understand the statistical and mathematical concepts behind predictive analytics algorithms
 Data visualization with Matplotlib
 Interactive plotting with NumPy, Scipy, and MKL functions
 Build financial models using MonteCarlo simulations
 Create directed graphs and multigraphs
 Advanced visualization with D3
In Detail
You will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve dataprocessing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikitlearn.
After this, you will move on to a data analytics specialization  predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikitlearn, and NumPy. You'll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling.
After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examples
This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:
 Getting Started with Python Data Analysis, Phuong Vo.T.H &Martin Czygan
 Learning Predictive Analytics with Python, Ashish Kumar
 Mastering Python Data Visualization, Kirthi Raman
Style and approach
The course acts as a stepbystep guide to get you familiar with data analysis and the libraries supported by Python with the help of realworld examples and datasets. It also helps you gain practical insights into predictive modeling by implementing predictiveanalytics algorithms on public datasets with Python. The course offers a wealth of practical guidance to help you on this journey to data visualization
Table of contents

Python: Data Analytics and Visualization
 Table of Contents
 Python: Data Analytics and Visualization
 Credits
 Preface

1. Module 1
 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
 8. Machine Learning Models with scikitlearn

2. Module 2
 1. Getting Started with Predictive Modelling

2. Data Cleaning
 Reading the data – variations and examples
 Various methods of importing data in Python
 The read_csv method
 Use cases of the read_csv method
 Case 2 – reading a dataset using the open method of Python
 Case 3 – reading data from a URL
 Case 4 – miscellaneous cases
 Basics – summary, dimensions, and structure
 Handling missing values
 Creating dummy variables
 Visualizing a dataset by basic plotting
 Summary

3. Data Wrangling
 Subsetting a dataset
 Generating random numbers and their usage
 Grouping the data – aggregation, filtering, and transformation
 Random sampling – splitting a dataset in training and testing datasets
 Concatenating and appending data
 Merging/joining datasets
 Summary
 4. Statistical Concepts for Predictive Modelling
 5. Linear Regression with Python

6. Logistic Regression with Python
 Linear regression versus logistic regression
 Understanding the math behind logistic regression
 Implementing logistic regression with Python
 Model validation and evaluation
 Model validation
 Summary
 7. Clustering with Python
 8. Trees and Random Forests with Python
 9. Best Practices for Predictive Modelling
 A. A List of Links

3. Module 3
 1. A Conceptual Framework for Data Visualization
 2. Data Analysis and Visualization
 3. Getting Started with the Python IDE
 4. Numerical Computing and Interactive Plotting
 5. Financial and Statistical Models

6. Statistical and Machine Learning
 Classification methods
 Understanding linear regression
 Linear regression
 Decision tree
 The Bayes theorem
 The NaÃ¯ve Bayes classifier
 The NaÃ¯ve Bayes classifier using TextBlob
 Viewing positive sentiments using word clouds
 knearest neighbors
 Logistic regression
 Support vector machines
 Principal component analysis
 kmeans clustering
 Summary
 7. Bioinformatics, Genetics, and Network Models
 8. Advanced Visualization
 B. Go Forth and Explore Visualization
 Bibliography
 Index
Product information
 Title: Python: Data Analytics and Visualization
 Author(s):
 Release date: March 2017
 Publisher(s): Packt Publishing
 ISBN: 9781788290098
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