Predictive analytics and machine learning have recently been accepted into mainstream data science and data analytics by many industries. They are now compared to other fields, and, without a doubt, we can expect a lot of rapid growth. It is even predicted that machine learning will accelerate so fast that within mere decades human labor will be replaced by intelligent machines (see http://en.wikipedia.org/wiki/Technological_singularity). The current state of art for artificial general intelligence (AGI) is far from that utopia, but machine learning has come a long way, and is being used in self-driving cars, chatbots, and AI-based assistants, such as Amazon's Alexa, Apple's Siri, and Ok Google. A lot of computing power and data is still needed to make even simple decisions, such as determining whether pictures on the Internet contain dogs or cats. Predictive analytics uses a variety of techniques, including machine learning, to make...
Python Data Analysis - Second Edition
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Python Data Analysis - Second Edition
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Overview of this book
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.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Getting Started with Python Libraries
NumPy Arrays
The Pandas Primer
Statistics and Linear Algebra
Retrieving, Processing, and Storing Data
Data Visualization
Signal Processing and Time Series
Working with Databases
Analyzing Textual Data and Social Media
Predictive Analytics and Machine Learning
Environments Outside the Python Ecosystem and Cloud Computing
Performance Tuning, Profiling, and Concurrency
Key Concepts
Useful Functions
Online Resources
Customer Reviews