#Datascience

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IPython Interactive Computing and Visualization Cookbook

IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code.

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Text Mining with R (A Tidy Approach)

If you work in analytics or data science, like we do, you are familiar with the fact that data is being generated all the time at ever faster rates. (You may even be a little weary of people pontificating about this fact.) Analysts are often trained to handle tabular or rectangular data that is mostly numeric, but much of the data proliferating today is unstructured and text-heavy. Many of us who work in analytical fields are not trained in even simple interpretation of natural language.

We developed the tidytext (Silge and Robinson 2016) R package because we were familiar with many methods for data wrangling and visualization, but couldn’t easily apply these same methods to text.

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Data Science at the Command Line

Today, data scientists can choose from an overwhelming collection of exciting technologies and programming languages. Python, R, Hadoop, Julia, Pig, Hive, and Spark are but a few examples. You may already have experience in one or more of these. If so, then why should you still care about the command line for doing data science? What does the command line have to offer that these other technologies and programming languages do not?

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R Packages

Packages are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and sample data. In this book you’ll learn how to turn your code into packages that others can easily download and use. Writing a package can seem overwhelming at first. So start with the basics and improve it over time. It doesn’t matter if your first version isn’t perfect as long as the next version is better. This is where we are developing the 2nd edition of this book.

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R Programming Succinctly

The R programming language on its own is a powerful tool that can perform thousands of statistical tasks, but by writing programs in R, you gain tremendous power and flexibility to extend its base functionality. Senior Succinctly series author and editor James McCaffrey shows you how in R Programming Succinctly.

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Altair: Declarative Visualization in Python

Altair is a declarative statistical visualization library for Python. With Altair, you can spend more time understanding your data and its meaning. Altair’s API is simple, friendly and consistent and built on top of the powerful Vega-Lite JSON specification. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code. Altair is developed by Jake Vanderplas and Brian Granger in close collaboration with the UW Interactive Data Lab.

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Efficient R programming

There are many excellent R resources for visualization, data science, and package development. Hundreds of scattered vignettes, web pages, and forums explain how to use R in particular domains. But little has been written on how to simply make R work effectively-until now.

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SQL Notes for Professionals book

This SQL Notes for Professionals book is compiled from Stack Overflow Documentation. (166 pages, published on May 2018)

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TPOT is a Python Automated Machine Learning tool

Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning (AutoML) tool that optimizes machine learning pipelines using genetic programming.

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Python for Everybody: Exploring Data in Python 3

Python for Everybody is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet. (Dr. Charles R. Severance)