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    Python Data Analysis - Second Edition

    Posted By: AlenMiler
    Python Data Analysis - Second Edition

    Python Data Analysis - Second Edition by Armando Fandango
    English | 27 Mar. 2017 | ASIN: B01MQYK5G2 | 330 Pages | AZW3 | 5.14 MB

    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

    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