The Data Science Toolset
DOWNLOAD
Download The Data Science Toolset PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get The Data Science Toolset book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
The Data Science Toolset
DOWNLOAD
Author : Barrett Williams
language : en
Publisher: Barrett Williams
Release Date : 2025-07-17
The Data Science Toolset written by Barrett Williams and has been published by Barrett Williams this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-17 with Computers categories.
Unleash the full potential of your data with "The Data Science Toolset"—a comprehensive guide that transforms numbers into compelling narratives and striking visuals. Designed for both beginners keen to dip their toes into the world of data visualization and seasoned professionals seeking to sharpen their skills, this eBook is your one-stop resource for mastering the art of turning data into insights. Start your journey by understanding the backbone of data storytelling in Chapter 1, where the significance of data visualization is dissected alongside an exploration of popular visualization tools. From there, delve deep into the intricacies of Matplotlib over Chapters 2 and 3, learning how to craft stunning 2D plots and customize every detail to perfection. The adventure continues with Seaborn in Chapters 4 and 5, where you'll discover its power in statistical visualizations and advanced techniques for complex data interactions. Transition effortlessly into the realm of interactive data with Plotly, as you build engaging dashboards and harness Plotly Express for quick, yet mesmerizing plots in Chapters 6 and 7. Uncover Bokeh's capabilities in Chapters 8 and 9, from dynamic plotting to seamless integration with web applications, opening doors to real-time data streaming and instant insights. Enhance your understanding of effective data visualization principles in Chapter 10, ensuring clarity and avoiding common pitfalls. Explore specialized visualization techniques for various data types, including categorical and temporal data, in Chapter 11. Dive into storytelling and engage your audience with captivating narratives in Chapter 12. As you navigate the complexities of Big Data in Chapter 13 and visualize predictive model outputs in Chapter 14, you'll be well-prepared for the future of data visualization. Conclude your journey in Chapter 15, exploring emerging trends and the evolution of the tools that redefine how we perceive data. Unlock the secrets of data visualization and become a leader in the data science field with "The Data Science Toolset." Your data deserves to tell a story. Make it unforgettable.
The Data Science Toolset
DOWNLOAD
Author : Barrett Williams
language : en
Publisher: Barrett Williams
Release Date : 2025-03-01
The Data Science Toolset written by Barrett Williams and has been published by Barrett Williams this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-01 with Computers categories.
Unlock the ultimate guide to mastering the expansive world of data science with "The Data Science Toolset." Whether you're a curious beginner or a seasoned analyst, this eBook is your gateway to an arsenal of powerful tools and techniques designed to elevate your data analysis skills and transform the way you work with data. Dive into the essential aspects of data tool selection, from understanding your data requirements to conducting thorough cost-benefit analyses. Unleash the potential of Python with in-depth guidance on libraries like Pandas and NumPy, ensuring you can manipulate data with ease. Elevate your visualization game with advanced techniques using Matplotlib, Seaborn, and interactive Plotly plots. Learn to clean, wrangle, and transform data efficiently and explore R's robust ecosystem, from data manipulation and visualization with ggplot2 to sophisticated statistical modeling. Discover how SQL can be your ally in writing efficient queries and handling complex data operations. Automation awaits you as you delve into workflow tools and pipeline building with Apache Airflow and Luigi. Excel doesn't get left behind; unlock its potential with advanced functions, pivot tables, and powerful data transformation using Power Query. Venture into the world of machine learning, understanding algorithms and model deployment with practical tools like Flask and Docker. Time series analysis and NLP techniques open doors to predictive and text data analysis, while big data frameworks like Hadoop and Spark redefine what you can achieve with vast datasets. With a focus on ethics and privacy, this eBook ensures you maintain integrity and compliance throughout your data journey. Finally, sustain your growth by exploring ways to stay current in the field and expand your professional network. "The Data Science Toolset" is more than a book—it's your companion for navigating the ever-evolving landscape of data science, empowering you with the knowledge to succeed in this dynamic domain. Get ready to transform your data insights into impactful decisions.
Python Data Science Handbook
DOWNLOAD
Author : Jake VanderPlas
language : en
Publisher: O'Reilly Media, Inc.
Release Date : 2022-12-06
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 2022-12-06 with Computers categories.
Python is a first-class tool for many researchers, primarily 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 new edition of 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 the second edition of 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: IPython and Jupyter provide computational environments for scientists using Python NumPy includes the ndarray for efficient storage and manipulation of dense data arrays Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data Matplotlib includes capabilities for a flexible range of data visualizations Scikit-learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms
Just Enough Data Science And Machine Learning
DOWNLOAD
Author : Mark Levene
language : en
Publisher: Addison-Wesley Professional
Release Date : 2024-12-04
Just Enough Data Science And Machine Learning written by Mark Levene 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 2024-12-04 with Business & Economics categories.
An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: Clear explanations of fundamental statistical notions and concepts Coverage of various types of data and techniques for analysis In-depth exploration of popular machine learning tools and methods Insight into specific data science topics, such as social networks and sentiment analysis Practical examples and case studies for real-world application Recommended further reading for deeper exploration of specific topics.
Data Science At The Command Line
DOWNLOAD
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
Introducing Data Science
DOWNLOAD
Author : Davy Cielen
language : en
Publisher: Simon and Schuster
Release Date : 2016-05-02
Introducing Data Science written by Davy Cielen and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-02 with Computers categories.
Summary Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering what you need to learn to begin a career as a data scientist can seem bewildering. This book is designed to help you get started. About the Book Introducing Data ScienceIntroducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You’ll explore data visualization, graph databases, the use of NoSQL, and the data science process. You’ll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. What’s Inside Handling large data Introduction to machine learning Using Python to work with data Writing data science algorithms About the Reader This book assumes you're comfortable reading code in Python or a similar language, such as C, Ruby, or JavaScript. No prior experience with data science is required. About the Authors Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and managing partners of Optimately and Maiton, where they focus on developing data science projects and solutions in various sectors. Table of Contents Data science in a big data world The data science process Machine learning Handling large data on a single computer First steps in big data Join the NoSQL movement The rise of graph databases Text mining and text analytics Data visualization to the end user
Thoughtful Data Science
DOWNLOAD
Author : David Taieb
language : en
Publisher:
Release Date : 2018-07-30
Thoughtful Data Science written by David Taieb and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-07-30 with Artificial intelligence categories.
The fourth involves applying graph algorithms to solve data problems. Taieb wraps up with a deep look into the future of data science for developers and his views on AI for data science. What you will learn Bridge the gap between developer and data scientist with a Python-based toolset Get the most out of Jupyter Notebooks with new productivity-enhancing tools Explore and visualize data using Jupyter Notebooks and PixieDust Work with and assess the impact of artificial intelligence in data science Work with TensorFlow, graphs, natural language processing, and time series Deep dive into multiple industry data science use cases Look into the future of data analysis and where to develop your skills Who this book is for This book is for established developers who want to bridge the gap between programmers and data scientists. With the introduction of PixieDust from its creator, the book will also be a great desk companion for the already accomplished Data Scientist. Some fluency in data interpretation and visualization is also assumed since this book addresses data professionals such as business and general data analysts. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development.
Data Science
DOWNLOAD
Author : Pallavi Vijay Chavan
language : en
Publisher: Chapman & Hall/CRC
Release Date : 2022-07
Data Science written by Pallavi Vijay Chavan and has been published by Chapman & Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07 with Computers categories.
"The proposed book covers the topic of data science in a very comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The book starts from the basic concepts of data science; it highlights the types of data, its use and its importance, followed by discussion on a wide range of applications of data science and widely used techniques in data science. Key features: provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science, presents predictive outcomes by applying data science techniques on real life applications, provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods, and gives the reader variety of intelligent applications that can be designed using data science and its allied fields. The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful"--
Cleaning Data For Effective Data Science
DOWNLOAD
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.
Data Science At The Command Line
DOWNLOAD
Author : Jeroen Janssens
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-08-17
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 2021-08-17 with Computers 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 100 Unix power 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, engineers, system administrators, and researchers. Obtain data from websites, APIs, databases, and spreadsheets Perform scrub operations on text, CSV, HTML, XML, and JSON files Explore data, compute descriptive statistics, and create visualizations Manage your data science workflow Create your own tools from one-liners and existing Python or R code Parallelize and distribute data-intensive pipelines Model data with dimensionality reduction, regression, and classification algorithms Leverage the command line from Python, Jupyter, R, RStudio, and Apache Spark