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Applied Data Science with Python and Jupyter: Use powerful industry-standard tools to unlock new, actionable insights from your data
Applied Data Science with Python and Jupyter: Use powerful industry-standard tools to unlock new, actionable insights from your data

Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications.

Key Features

  • Get up and running with the Jupyter ecosystem and some example datasets
  • Learn about key machine...
Translating Statistics to Make Decisions: A Guide for the Non-Statistician
Translating Statistics to Make Decisions: A Guide for the Non-Statistician

Examine and solve the common misconceptions and fallacies that non-statisticians bring to their interpretation of statistical results. Explore the many pitfalls that non-statisticians―and also statisticians who present statistical reports to non-statisticians―must avoid if statistical results are to be correctly used for...

Python Data Analysis
Python Data Analysis

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...
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib
Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute...

Big Data Analysis with Python: Combine Spark and Python to unlock the powers of parallel computing and machine learning
Big Data Analysis with Python: Combine Spark and Python to unlock the powers of parallel computing and machine learning

Get to grips with processing large volumes of data and presenting it as engaging, interactive insights using Spark and Python.

Key Features

  • Get a hands-on, fast-paced introduction to the Python data science stack
  • Explore ways to create useful metrics and statistics from...
Mathematical Statistics with Resampling and R
Mathematical Statistics with Resampling and R
This book bridges the latest software applications with the benefits of modern resampling techniques

Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques...

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas,...

Learn RStudio IDE: Quick, Effective, and Productive Data Science
Learn RStudio IDE: Quick, Effective, and Productive Data Science
Discover how to use the popular RStudio IDE as a professional tool that includes code refactoring support, debugging, and Git version control integration. This book gives you a tour of RStudio and shows you how it helps you do exploratory data analysis; build data visualizations with ggplot; and create custom R packages and...
Practical Data Science Cookbook - Second Edition
Practical Data Science Cookbook - Second Edition

Over 85 recipes to help you complete real-world data science projects in R and Python

About This Book

  • Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data
  • Get beyond the theory and implement real-world projects in data science using...
Become a Python Data Analyst: Perform exploratory data analysis and gain insight into scientific computing using Python
Become a Python Data Analyst: Perform exploratory data analysis and gain insight into scientific computing using Python

Enhance your data analysis and predictive modeling skills using popular Python tools

Key Features

  • Cover all fundamental libraries for operation and manipulation of Python for data analysis
  • Implement real-world datasets to perform predictive analytics with Python
  • ...
Medical Informatics: Knowledge Management and Data Mining in Biomedicine (Integrated Series in Information Systems)
Medical Informatics: Knowledge Management and Data Mining in Biomedicine (Integrated Series in Information Systems)

Medical Informatics and biomedical computing have grown in quantum measure over the past decade. An abundance of advances have come to the foreground in this field with the vast amounts of biomedical and genomic data, the Internet, and the wide application of computer use in all aspects of medical, biological, and health care research and...

Functional Python Programming: Discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads, 2nd Edition
Functional Python Programming: Discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads, 2nd Edition

Create succinct and expressive implementations with functional programming in Python

Key Features

  • Learn how to choose between imperative and functional approaches based on expressiveness, clarity, and performance
  • Get familiar with complex concepts such as monads,...
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