Download PDF by Armando Fandango: Python Data Analysis - Second Edition

By Armando Fandango

ISBN-10: 1787127486

ISBN-13: 9781787127487

Key Features

  • Find, control, and learn your information utilizing the Python 3.5 libraries
  • Perform complex, high-performance linear algebra and mathematical calculations with fresh and effective Python code
  • An easy-to-follow consultant with life like examples which are often utilized in real-world facts research projects.

Book Description

Data research strategies generate precious insights from small and massive volumes of information. Python, with its robust set of libraries, has develop into a well-liked platform to behavior numerous info research and predictive modeling tasks.

With this e-book, you are going to the way to strategy and control facts with Python for advanced research and modeling. We study info manipulations equivalent to aggregating, concatenating, appending, cleansing, and dealing with lacking values, with NumPy and Pandas. The e-book covers easy methods to shop and retrieve info from numerous information resources akin to SQL and NoSQL, CSV fies, and HDF5. We the best way to visualize facts utilizing visualization libraries, in addition to complex themes reminiscent of sign processing, time sequence, textual facts research, computing device studying, and social media analysis.

The ebook covers a plethora of Python modules, comparable to matplotlib, statsmodels, scikit-learn, and NLTK. It additionally covers utilizing Python with exterior environments akin to R, Fortran, C/C++, and improve libraries.

What you are going to learn

  • Install open resource Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on quite a few platforms
  • Prepare and fresh your information, and use it for exploratory analysis
  • Manipulate your information with Pandas
  • Retrieve and shop your info from RDBMS, NoSQL, and allotted filesystems equivalent to HDFS and HDF5
  • Visualize your facts with open resource libraries resembling matplotlib, bokeh, and plotly
  • Learn approximately numerous computing device studying tools equivalent to supervised, unsupervised, probabilistic, and Bayesian
  • Understand sign processing and time sequence information analysis
  • Get to grips with graph processing and social community analysis

About the Author

Armando Fandango is leader info Scientist at Epic Engineering and Consulting crew, and works on exclusive initiatives with regards to security and executive organizations. Armando is an finished technologist with hands-on services and senior executive-level event with startups and massive businesses globally. His paintings spans varied industries together with FinTech, inventory exchanges, banking, bioinformatics, genomics, AdTech, infrastructure, transportation, power, human assets, and entertainment.

Armando has labored for greater than ten years in tasks concerning predictive analytics, facts technological know-how, computer studying, vast information, product engineering, excessive functionality computing, and cloud infrastructures. His study pursuits spans computing device studying, deep studying, and clinical computing.

Table of Contents

  1. Getting begun 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 info and Social Media
  10. Predictive Analytics and desktop Learning
  11. Environments outdoor the Python surroundings and Cloud Computing
  12. Performance Tuning, Profiling, and Concurrency
  13. Key Concepts
  14. Useful Functions
  15. Online Resources

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