Data Science At The Command Line
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Data Science At The Command Line
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Author : Jeroen Janssens
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2014-09-25
Data Science At The Command Line written by Jeroen Janssens and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-25 with Computers categories.
This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. To get you started—whether you’re on Windows, OS X, or Linux—author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools. Discover why the command line is an agile, scalable, and extensible technology. Even if you’re already comfortable processing data with, say, Python or R, you’ll greatly improve your data science workflow by also leveraging the power of the command line. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on plain text, CSV, HTML/XML, and JSON Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow using Drake Create reusable tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines using GNU Parallel Model data with dimensionality reduction, clustering, regression, and classification algorithms
Data Science At The Command Line
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Author : Jeroen Janssens
language : en
Publisher: O'Reilly Media
Release Date : 2021-09-30
Data Science At The Command Line written by Jeroen Janssens and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-30 with categories.
This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small yet powerful command-line tools to quickly obtain, scrub, explore, and model your data. To get you started, author Jeroen Janssens provides a Docker image packed with over 80 tools--useful whether you work with Windows, macOS, or Linux. You'll quickly discover why the command line is an agile, scalable, and extensible technology. Even if you're comfortable processing data with Python or R, you'll learn how to greatly improve your data science workflow by leveraging the command line's power. This book is ideal for data scientists, analysts, and engineers; software and machine learning engineers; and system administrators. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on text, CSV, HTM, XML, and JSON files Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow Create reusable command-line tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines Model data with dimensionality reduction, clustering, regression, and classification algorithms
Cleaning Data For Effective Data Science
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Author : David Mertz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-03-31
Cleaning Data For Effective Data Science written by David Mertz and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-31 with Mathematics categories.
Think about your data intelligently and ask the right questions Key FeaturesMaster data cleaning techniques necessary to perform real-world data science and machine learning tasksSpot common problems with dirty data and develop flexible solutions from first principlesTest and refine your newly acquired skills through detailed exercises at the end of each chapterBook Description Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses. What you will learnIngest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structuresUnderstand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and BashApply useful rules and heuristics for assessing data quality and detecting bias, like Benford’s law and the 68-95-99.7 ruleIdentify and handle unreliable data and outliers, examining z-score and other statistical propertiesImpute sensible values into missing data and use sampling to fix imbalancesUse dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your dataWork carefully with time series data, performing de-trending and interpolationWho this book is for This book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you. Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful.
Python Data Science Handbook
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Author : Jake VanderPlas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-11-21
Python Data Science Handbook written by Jake VanderPlas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with Computers categories.
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
Doing Data Science
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Author : Cathy O'Neil
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2013-10-09
Doing Data Science written by Cathy O'Neil and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-09 with Computers categories.
A guide to the usefulness of data science covers such topics as algorithms, logistic regression, financial modeling, data visualization, and data engineering.
Unix Power Tools
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Author : Shelley Powers
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2003
Unix Power Tools written by Shelley Powers and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computers categories.
With the growing popularity of Linux and the advent of Darwin, Unix has metamorphosed into something new and exciting. No longer perceived as a difficult operating system, more and more users are discovering the advantages of Unix for the first time. But whether you are a newcomer or a Unix power user, you'll find yourself thumbing through the goldmine of information in the new edition of Unix Power Tools to add to your store of knowledge. Want to try something new? Check this book first, and you're sure to find a tip or trick that will prevent you from learning things the hard way. The latest edition of this best-selling favorite is loaded with advice about almost every aspect of Unix, covering all the new technologies that users need to know. In addition to vital information on Linux, Darwin, and BSD, Unix Power Tools 3rd Edition now offers more coverage of bash, zsh, and other new shells, along with discussions about modern utilities and applications. Several sections focus on security and Internet access. And there is a new chapter on access to Unix from Windows, addressing the heterogeneous nature of systems today. You'll also find expanded coverage of software installation and packaging, as well as basic information on Perl and Python. Unix Power Tools 3rd Edition is a browser's book...like a magazine that you don't read from start to finish, but leaf through repeatedly until you realize that you've read it all. Bursting with cross-references, interesting sidebars explore syntax or point out other directions for exploration, including relevant technical details that might not be immediately apparent. The book includes articles abstracted from other O'Reilly books, new information that highlights program tricks and gotchas, tips posted to the Net over the years, and other accumulated wisdom. Affectionately referred to by readers as "the" Unix book, UNIX Power Tools provides access to information every Unix user is going to need to know. It will help you think creatively about UNIX, and will help you get to the point where you can analyze your own problems. Your own solutions won't be far behind.
Data Science Foundations Tools And Techniques
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Author : Michael Freeman
language : en
Publisher: Addison-Wesley Professional
Release Date : 2018-11-16
Data Science Foundations Tools And Techniques written by Michael Freeman and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-16 with Application software categories.
The Foundational Hands-On Skills You Need to Dive into Data Science "Freeman and Ross have created the definitive resource for new and aspiring data scientists to learn foundational programming skills." -From the foreword by Jared Lander, series editor Using data science techniques, you can transform raw data into actionable insights for domains ranging from urban planning to precision medicine. Programming Skills for Data Science brings together all the foundational skills you need to get started, even if you have no programming or data science experience. Leading instructors Michael Freeman and Joel Ross guide you through installing and configuring the tools you need to solve professional-level data science problems, including the widely used R language and Git version-control system. They explain how to wrangle your data into a form where it can be easily used, analyzed, and visualized so others can see the patterns you've uncovered. Step by step, you'll master powerful R programming techniques and troubleshooting skills for probing data in new ways, and at larger scales. Freeman and Ross teach through practical examples and exercises that can be combined into complete data science projects. Everything's focused on real-world application, so you can quickly start analyzing your own data and getting answers you can act upon. Learn to Install your complete data science environment, including R and RStudio Manage projects efficiently, from version tracking to documentation Host, manage, and collaborate on data science projects with GitHub Master R language fundamentals: syntax, programming concepts, and data structures Load, format, explore, and restructure data for successful analysis Interact with databases and web APIs Master key principles for visualizing data accurately and intuitively Produce engaging, interactive visualizations with ggplot and other R packages Transform analyses into sharable documents and sites with R Markdown Create interactive web data science applications with Shiny Collaborate smoothly as part of a data science team Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Python For Devops
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Author : Noah Gift
language : en
Publisher: O'Reilly Media
Release Date : 2019-12-12
Python For Devops written by Noah Gift and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-12 with Computers categories.
Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout these transformations, Python has become one of the most popular languages in the world. This practical resource shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform. Learning how to interact and automate with Linux is essential for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. Looking for effective ways to "get stuff done" in Python? This is your guide. Python foundations, including a brief introduction to the language How to automate text, write command-line tools, and automate the filesystem Linux utilities, package management, build systems, monitoring and instrumentation, and automated testing Cloud computing, infrastructure as code, Kubernetes, and serverless Machine learning operations and data engineering from a DevOps perspective Building, deploying, and operationalizing a machine learning project
Foundations Of Data Science
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Author : Avrim Blum
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-23
Foundations Of Data Science written by Avrim Blum and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-23 with Computers categories.
Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.
Introduction To Data Science
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Author : Rafael A. Irizarry
language : en
Publisher: CRC Press
Release Date : 2019-11-12
Introduction To Data Science written by Rafael A. Irizarry and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-12 with Mathematics categories.
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course.