A Guide To Data Science And Analytics
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R for Data Science
Author: Hadley Wickham
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-12-12
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Python for Data Science
Are you looking for a crash course that will help you learn Python? Do you want to master data science using python? If yes, then keep reading! Python is one of the most popular programming languages in the word in 2020 and specially for data science. Every day people use it to do cool things like Automation, they use it in Artificial Intelligence, Machine Learning, as well as Building Applications and Websites like Instagram and Dropbox. YouTube, Pinterest, and SurveyMonkey are all built on Python. So if you are looking for a trendy job, like data scientist, Python is for you. This is a Python guide with 2 Books in 1: Python crash course Python for data analysis Python has seen an explosion in popularity in recent years, driven by several aspects that make it an incredibly versatile and intuitive language. Moreover, data analysis plays a significant job in numerous parts of your regular day to day existence today. Organizations use information to Understand Their Customer Needs and produce the Best Possible Product or Service. Python Programming Language is one of the best framework with regards to information examination. Data Scientist is the most requested job of the 21st century and Python is the most popular programming language of the 21st century. So it's pretty obvious that anyone have skills in both Data Science and Python will be in great demand in industry. You needn't bother with an exhausting and costly reading material. This guide is the best one for every readers. This guide covers: The world of data science technologies Application of machine learning Data scientist: the sexiest job in the 21st century Learning Python from scratch Data analysis with Python NumPy for numerical data processing Data visualization with Python Projects on Python And much more! Despite its simplicity, Python is also sturdy and robust enough to carry out complex scientific and mathematical tasks. Python has been designed with features that drastically simplify the visualization and analysis of data, and Python is also the go-to choice for the creation of machine learning models and artificial intelligence. Be it machine learning, data analytics, data processing, web development, enterprise software development or taking the photo of Blackhole: Python is everywhere. Beloved by the data scientists and new generation developers, Pyhton will eat the word! Ready to get started? Click the BUY NOW button!
Python Data Science Handbook
Author: Jake VanderPlas
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-11-21
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms