Python Data Analysis - Second Edition: Data manipulation and complex data analysis with Python

Key Features

• Find, manipulate, and analyze your data using the Python 3.5 libraries
• Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code
• An easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects.

Book Description

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks.

With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis.

The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.

What you will learn

• Install open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platforms
• Prepare and clean your data, and use it for exploratory analysis
• Manipulate your data with Pandas
• Retrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5
• Visualize your data with open source libraries such as matplotlib, bokeh, and plotly
• Learn about various machine learning methods such as supervised, unsupervised, probabilistic, and Bayesian
• Understand signal processing and time series data analysis
• Get to grips with graph processing and social network analysis

About the Author

Armando Fandango is Chief Data Scientist at Epic Engineering and Consulting Group, and works on confidential projects related to defense and government agencies. Armando is an accomplished technologist with hands-on capabilities and senior executive-level experience with startups and large companies globally. His work spans diverse industries including FinTech, stock exchanges, banking, bioinformatics, genomics, AdTech, infrastructure, transportation, energy, human resources, and entertainment.

Armando has worked for more than ten years in projects involving predictive analytics, data science, machine learning, big data, product engineering, high performance computing, and cloud infrastructures. His research interests spans machine learning, deep learning, and scientific computing.

Table of Contents

1. Getting Started with Python Libraries
2. NumPy Arrays
3. The Pandas Primer
4. Statistics and Linear Algebra
5. Retrieving, Processing, and Storing Data
6. Data Visualization
7. Signal Processing and Time Series
8. Working with Databases
9. Analyzing Textual Data and Social Media
10. Predictive Analytics and Machine Learning
11. Environments Outside the Python Ecosystem and Cloud Computing
12. Performance Tuning, Profiling, and Concurrency
13. Key Concepts
14. Useful Functions
15. Online Resources

Armando Fandango is an accomplished technologist with hands-on capabilities and senior executive level experience with startups and large companies globally.  Armando founded NeuraSights, with the goal of creating insights from small and big data using neural networks and machine learning. For more than ten years, Armando has been building products and developing solutions that involve Predictive Analytics, Data Science, Machine Learning, Big Data, and High-Performance Computing. His work spans across diverse industries including FinTech, Banking, BioInformatics, Genomics, AdTech, Utilities and Infrastructure, Traffic and Transportation, Energy, Human Resource, and Entertainment. His research interests include machine learning, deep learning, algorithmic game theory and scientific computing. Armando is the author of the book titled "Python Data Analysis - Second Edition" and has published research in international journals and conferences.