Mastering Data Science A Comprehensive Guide To Techniques And Applications
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Mastering Data Science: A Comprehensive Guide to Techniques and Applications
Embark on your data science journey with "Mastering Data Science: A Comprehensive Guide to Techniques and Applications," the ultimate resource for mastering the vast landscape of data science. Whether you're a novice eager to explore the field or an expert looking to refine and expand your skills, this book provides a thorough exploration of essential data science techniques and applications. Discover the foundational principles of data wrangling, preprocessing, statistical fundamentals, and essential mathematical techniques. Delve into the complexities of machine learning, deep learning, and natural language processing with clear explanations that connect theory to real-world practice. Each chapter is carefully designed to unravel complex concepts and features practical examples to enhance your understanding. Explore the intricacies of time series analysis, develop proficiency in crafting compelling data visualizations, and learn to effectively deploy machine learning models. With a focus on practical application, this guide not only imparts theoretical insights but also empowers you to address data science challenges confidently. Unlock the potential of data science to innovate, solve critical problems, and make informed decisions in today's data-centric world with "Mastering Data Science: A Comprehensive Guide to Techniques and Applications."
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 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