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| data science in a nutshell: Data Science in a Nutshell Dirk Bangel, 2018-12-15 This booklet gives an introduction to data science and summarizes the commonly applied approaches. |
| data science in a nutshell: Modern Data Science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, 2021-03-31 From a review of the first edition: Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice. |
| data science in a nutshell: Statistics in a Nutshell Sarah Boslaugh, 2012-11-15 A clear and concise introduction and reference for anyone new to the subject of statistics. |
| data science in a nutshell: Algorithms in a Nutshell George T. Heineman, Gary Pollice, Stanley Selkow, 2008-10-14 Creating robust software requires the use of efficient algorithms, but programmers seldom think about them until a problem occurs. Algorithms in a Nutshell describes a large number of existing algorithms for solving a variety of problems, and helps you select and implement the right algorithm for your needs -- with just enough math to let you understand and analyze algorithm performance. With its focus on application, rather than theory, this book provides efficient code solutions in several programming languages that you can easily adapt to a specific project. Each major algorithm is presented in the style of a design pattern that includes information to help you understand why and when the algorithm is appropriate. With this book, you will: Solve a particular coding problem or improve on the performance of an existing solution Quickly locate algorithms that relate to the problems you want to solve, and determine why a particular algorithm is the right one to use Get algorithmic solutions in C, C++, Java, and Ruby with implementation tips Learn the expected performance of an algorithm, and the conditions it needs to perform at its best Discover the impact that similar design decisions have on different algorithms Learn advanced data structures to improve the efficiency of algorithms With Algorithms in a Nutshell, you'll learn how to improve the performance of key algorithms essential for the success of your software applications. |
| data science in a nutshell: Python Data Science Handbook Jake VanderPlas, 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 |
| data science in a nutshell: Model Selection and Error Estimation in a Nutshell Luca Oneto, 2019-07-17 How can we select the best performing data-driven model? How can we rigorously estimate its generalization error? Statistical learning theory answers these questions by deriving non-asymptotic bounds on the generalization error of a model or, in other words, by upper bounding the true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory has been considered only an abstract theoretical framework, useful for inspiring new learning approaches, but with limited applicability to practical problems. The purpose of this book is to give an intelligible overview of the problems of model selection and error estimation, by focusing on the ideas behind the different statistical learning theory approaches and simplifying most of the technical aspects with the purpose of making them more accessible and usable in practice. The book starts by presenting the seminal works of the 80’s and includes the most recent results. It discusses open problems and outlines future directions for research. |
| data science in a nutshell: Data Science from Scratch Joel Grus, 2015-04-14 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases |
| data science in a nutshell: R in a Nutshell Joseph Adler, 2010-01-04 Why learn R? Because it's rapidly becoming the standard for developing statistical software. R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics. The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems. Understand the basics of the language, including the nature of R objects Learn how to write R functions and build your own packages Work with data through visualization, statistical analysis, and other methods Explore the wealth of packages contributed by the R community Become familiar with the lattice graphics package for high-level data visualization Learn about bioinformatics packages provided by Bioconductor I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians. |
| data science in a nutshell: R in a Nutshell Joseph Adler, 2012-10-09 Presents a guide to the R computer language, covering such topics as the user interface, packages, syntax, objects, functions, object-oriented programming, data sets, lattice graphics, regression models, and bioconductor. |
| data science in a nutshell: Journey to Data Scientist Kate Strachnyi, 2017-11-13 When author Kate Strachnyi wanted to learn more about data science, she went straight to the source. In a series of more than twenty interviews, she asks leading data scientists questions about starting in the field and the future of the industry. With their stories, learn about the many different positions available for data scientists, the criteria recruiters look for when hiring, the best options for building your portfolio, the recruitment and interviewing process, the typical workday for a data scientist, the changing industry and its impact on other industries, the wide variety of projects that use data science, and the skills that can complement and improve your work. Strachnyi's interview subjects include team members from some of the world's largest organizations, including LinkedIn, Pinterest, Bloomberg, and IBM. These men and women graciously explain how they fell in love with data science and list the must-have skills that would make you an invaluable member of a team. Their advice gives you invaluable insight into the world of data science and the best ways you yourself can contribute to amazing research projects and the development of new technology. |
| data science in a nutshell: Web Design in a Nutshell Jennifer Niederst Robbins, 2006-02-21 Are you still designing web sites like it's 1999? If so, you're in for a surprise. Since the last edition of this book appeared five years ago, there has been a major climate change with regard to web standards. Designers are no longer using (X)HTML as a design tool, but as a means of defining the meaning and structure of content. Cascading Style Sheets are no longer just something interesting to tinker with, but rather a reliable method for handling all matters of presentation, from fonts and colors to the layout of the entire page. In fact, following the standards is now a mandate of professional web design. Our popular reference, Web Design in a Nutshell, is one of the first books to capture this new web landscape with an edition that's been completely rewritten and expanded to reflect the state of the art. In addition to being an authoritative reference for (X)HTML and Cascading Style Sheets, this book also provides an overview of the unique requirements of designing for the Web and gets to the nitty-gritty of JavaScript and DOM Scripting, web graphics optimization, and multimedia production. It is an indispensable tool for web designers and developers of all levels. The third edition covers these contemporary web design topics: Structural layer: HTML 4.01 and XHTML 1.0 (9 chapters), including an alphabetical reference of all elements, attributes and character entities Presentation layer: Ten all-new chapters on Cascading Style Sheets, Level 2.1, including an alphabetical reference of all properties and values. Behavior layer: JavaScript and scripting with the Document Object Model (DOM) Web environment: New web standards, browsers, display devices, accessibility, and internationalization Web graphics optimization: Producing lean and mean GIF, JPEG, PNG, and animated GIFs Multimedia: Web audio, video, Flash, and PDF Organized so that readers can find answers quickly, Web Design in a Nutshell, Third Edition helps experienced designers come up to speed quickly on standards-based web design, and serves as a quick reference for those already familiar with the new standards and technology. There are many books for web designers, but none that address such a wide variety of topics. Find out why nearly half a million buyers have made this the most popular web design book available. |
| data science in a nutshell: The Model Thinker Scott E. Page, 2018-11-27 Work with data like a pro using this guide that breaks down how to organize, apply, and most importantly, understand what you are analyzing in order to become a true data ninja. From the stock market to genomics laboratories, census figures to marketing email blasts, we are awash with data. But as anyone who has ever opened up a spreadsheet packed with seemingly infinite lines of data knows, numbers aren't enough: we need to know how to make those numbers talk. In The Model Thinker, social scientist Scott E. Page shows us the mathematical, statistical, and computational models—from linear regression to random walks and far beyond—that can turn anyone into a genius. At the core of the book is Page's many-model paradigm, which shows the reader how to apply multiple models to organize the data, leading to wiser choices, more accurate predictions, and more robust designs. The Model Thinker provides a toolkit for business people, students, scientists, pollsters, and bloggers to make them better, clearer thinkers, able to leverage data and information to their advantage. |
| data science in a nutshell: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| data science in a nutshell: Thinking Data Science Poornachandra Sarang, 2023-03-01 This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”. The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book. Thinking Data Science will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big. |
| data science in a nutshell: Sequence Analysis in a Nutshell: A Guide to Tools Scott Markel, Darryl Leon, 2003-01-27 This work pulls together all of the vital information about the most commonly used databases, analytical tools, and tables used in sequence analysis. |
| data science in a nutshell: Data Analysis with Open Source Tools Philipp K. Janert, 2010-11-11 Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you. Use graphics to describe data with one, two, or dozens of variables Develop conceptual models using back-of-the-envelope calculations, as well asscaling and probability arguments Mine data with computationally intensive methods such as simulation and clustering Make your conclusions understandable through reports, dashboards, and other metrics programs Understand financial calculations, including the time-value of money Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations Become familiar with different open source programming environments for data analysis Finally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, Mozilla An indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora |
| data science in a nutshell: Machine Learning Engineering in Action Ben Wilson, 2022-05-17 Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists who know machine learning and the basics of object-oriented programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer. |
| data science in a nutshell: Python in a Nutshell Alex Martelli, 2006-07-14 This volume offers Python programmers a straightforward guide to the important tools and modules of this open source language. It deals with the most frequently used parts of the standard library as well as the most popular and important third party extensions. |
| data science in a nutshell: The Universe in a Nutshell Stephen W. Hawking, 2005-01 Stephen Hawking s A Brief History of Time was a publishing phenomenon. Translated into thirty languages, it has sold over nine million copies worldwide. It continues to captivate and inspire new readers every year. When it was first published in 1988 the ideas discussed in it were at the cutting edge of what was then known about the universe. In the intervening years there have been extraordinary advances in our understanding of the space and time. The technology for observing the micro- and macro-cosmic world has developed in leaps and bounds. During the same period cosmology and the theoretical sciences have entered a new golden age. Professor Stephen Hawking has been at the heart of this new scientific renaissance. Now, in The Universe in a Nutshell, Stephen Hawking brings us fully up-to-date with the advances in scientific thinking. We are now nearer than we have ever been to a full understanding of the universe. In a fascinating and accessible discussion that ranges from quantum mechanics, to time travel, black holes to uncertainty theory, to the search for science s Holy Grail the unified field theory (or in layman s terms the theory of absolutely everything ) Professor Hawking once more takes us to the cutting edge of modern thinking. Beautifully illustrated throughout, with original artwork commissioned for this project, The Universe in a Nutshell is guaranteed to be the biggest science book of 2001. |
| data science in a nutshell: Nanotechnology in a Nutshell Christian Ngô, Marcel Van de Voorde, 2014-01-04 A new high-level book for professionals from Atlantis Press providing an overview of nanotechnologies now and their applications in a broad variety of fields, including information and communication technologies, environmental sciences and engineering, societal life, and medicine, with provision of customized treatments. The book shows where nanotechnology is now - a fascinating time when the science is transitioning into complex systems with impact on new products. Present and future developments are addressed, as well as a larger number of new industrial and research opportunities deriving from this domain. An overview for professionals, researchers and policy-makers of this very rapidly expanding field. Brief chapters and colour figures with a contained overall length make the book attractive at an attractive price - a must for every professional’s shelf. Mihail C. Roco, National Science Foundation and National Nanotechnology Initiative, wrote the preface underlying the importance and weight of the present book to this exciting and epoch-awakening field of research and applications: “Nanotechnology is well recognized as a science and technology megatrend for the beginning of the 21st century. This book aims to show where nanotechnology is now - transitioning to complex systems and fundamentally new products - and communicates the societal promise of nanotechnology to specialists and the public. Most of what has already made it into the marketplace is in the form of “First Generation” products, passive nanostructures with steady behaviour. Many companies have “Second Generation” products, active nanostructures with changing behaviour during use, and embryonic “Third Generation” products, including 3-dimensional nanosystems. Concepts for “Fourth Generation” products, including heterogeneous molecular nanosystems, are only in research.” |
| data science in a nutshell: Astrophysics in a Nutshell Dan Maoz, 2016-02-23 The ideal one-semester astrophysics introduction for science undergraduates—now expanded and fully updated Winner of the American Astronomical Society's Chambliss Award, Astrophysics in a Nutshell has become the text of choice in astrophysics courses for science majors at top universities in North America and beyond. In this expanded and fully updated second edition, the book gets even better, with a new chapter on extrasolar planets; a greatly expanded chapter on the interstellar medium; fully updated facts and figures on all subjects, from the observed properties of white dwarfs to the latest results from precision cosmology; and additional instructive problem sets. Throughout, the text features the same focused, concise style and emphasis on physics intuition that have made the book a favorite of students and teachers. Written by Dan Maoz, a leading active researcher, and designed for advanced undergraduate science majors, Astrophysics in a Nutshell is a brief but thorough introduction to the observational data and theoretical concepts underlying modern astronomy. Generously illustrated, it covers the essentials of modern astrophysics, emphasizing the common physical principles that govern astronomical phenomena, and the interplay between theory and observation, while also introducing subjects at the forefront of modern research, including black holes, dark matter, dark energy, and gravitational lensing. In addition to serving as a course textbook, Astrophysics in a Nutshell is an ideal review for a qualifying exam and a handy reference for teachers and researchers. The most concise and current astrophysics textbook for science majors—now expanded and fully updated with the latest research results Contains a broad and well-balanced selection of traditional and current topics Uses simple, short, and clear derivations of physical results Trains students in the essential skills of order-of-magnitude analysis Features a new chapter on extrasolar planets, including discovery techniques Includes new and expanded sections and problems on the physics of shocks, supernova remnants, cosmic-ray acceleration, white dwarf properties, baryon acoustic oscillations, and more Contains instructive problem sets at the end of each chapter Solutions manual (available only to professors) |
| data science in a nutshell: R for Data Science Hadley Wickham, Garrett Grolemund, 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 |
| data science in a nutshell: Behavioral Data Analysis with R and Python Florent Buisson, 2021-06-15 Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis. Advanced experimental design helps you get the most out of your A/B tests, while causal diagrams allow you to tease out the causes of behaviors even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, thispractical book provides complete examples and exercises in R and Python to help you gain more insight from your data--immediately. Understand the specifics of behavioral data Explore the differences between measurement and prediction Learn how to clean and prepare behavioral data Design and analyze experiments to drive optimal business decisions Use behavioral data to understand and measure cause and effect Segment customers in a transparent and insightful way |
| data science in a nutshell: The Nutshell Studies of Unexplained Death Corinne May Botz, 2004-09-28 The Nutshell Studies of Unexplained Death offers readers an extraordinary glimpse into the mind of a master criminal investigator. Frances Glessner Lee, a wealthy grandmother, founded the Department of Legal Medicine at Harvard in 1936 and was later appointed captain in the New Hampshire police. In the 1940s and 1950s she built dollhouse crime scenes based on real cases in order to train detectives to assess visual evidence. Still used in forensic training today, the eighteen Nutshell dioramas, on a scale of 1:12, display an astounding level of detail: pencils write, window shades move, whistles blow, and clues to the crimes are revealed to those who study the scenes carefully. Corinne May Botz's lush color photographs lure viewers into every crevice of Frances Lee's models and breathe life into these deadly miniatures, which present the dark side of domestic life, unveiling tales of prostitution, alcoholism, and adultery. The accompanying line drawings, specially prepared for this volume, highlight the noteworthy forensic evidence in each case. Botz's introductory essay, which draws on archival research and interviews with Lee's family and police colleagues, presents a captivating portrait of Lee. |
| data science in a nutshell: C# 10 in a Nutshell Joseph Albahari, 2022-02-15 When you have questions about C# 10 or .NET 6, this best-selling guide has the answers you need. C# is a language of unusual flexibility and breadth, and with its continual growth, there's always so much more to learn. In the tradition of O'Reilly's Nutshell guides, this thoroughly updated edition is simply the best one-volume reference to the C# language available today. Organized around concepts and use cases, this comprehensive and complete reference provides intermediate and advanced programmers with a concise map of C# and .NET that also plumbs significant depths. Get up to speed on C#, from syntax and variables to advanced topics such as pointers, closures, and patterns Dig deep into LINQ, with three chapters dedicated to the topic Explore concurrency and asynchrony, advanced threading, and parallel programming Work with .NET features, including regular expressions, networking, assemblies, spans, reflection, and cryptography |
| data science in a nutshell: How to Lead in Data Science Jike Chong, Yue Cathy Chang, 2021-12-28 A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples. In How To Lead in Data Science you will learn: Best practices for leading projects while balancing complex trade-offs Specifying, prioritizing, and planning projects from vague requirements Navigating structural challenges in your organization Working through project failures with positivity and tenacity Growing your team with coaching, mentoring, and advising Crafting technology roadmaps and championing successful projects Driving diversity, inclusion, and belonging within teams Architecting a long-term business strategy and data roadmap as an executive Delivering a data-driven culture and structuring productive data science organizations How to Lead in Data Science is full of techniques for leading data science at every seniority level—from heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas. About the technology Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. About the book How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It’s filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You’ll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you’ll build practical skills to grow and improve your team, your company’s data culture, and yourself. What's inside How to coach and mentor team members Navigate an organization’s structural challenges Secure commitments from other teams and partners Stay current with the technology landscape Advance your career About the reader For data science practitioners at all levels. About the author Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies. Table of Contents 1 What makes a successful data scientist? PART 1 THE TECH LEAD: CULTIVATING LEADERSHIP 2 Capabilities for leading projects 3 Virtues for leading projects PART 2 THE MANAGER: NURTURING A TEAM 4 Capabilities for leading people 5 Virtues for leading people PART 3 THE DIRECTOR: GOVERNING A FUNCTION 6 Capabilities for leading a function 7 Virtues for leading a function PART 4 THE EXECUTIVE: INSPIRING AN INDUSTRY 8 Capabilities for leading a company 9 Virtues for leading a company PART 5 THE LOOP AND THE FUTURE 10 Landscape, organization, opportunity, and practice 11 Leading in data science and a future outlook |
| data science in a nutshell: Positive Psychology in a Nutshell: the Science of Happiness Ilona Boniwell, 2017-06-16 The best general introduction to positive psychology available. Dr Alex Linley, University of Leicester, UK Dr Ilona Boniwell is recognized as Europe’s leading researcher, innovator and thinker in the expanding world of positive psychology. Positive Psychology in a Nutshell offers something for everyone with an interest in discovering how to live optimally. This brilliant littlebook is packed with scientific evidence identifying the key ingredients that help to create a happy life. Read it and learn how to change yours for the better. Dr Cecilia d'Felice, Consultant Psychologist, Author and Columnist for The Times and The Metro Positive Psychology in a Nutshell is a little gem of a book, beautifully and engagingly written, and having the marks of a cogent teacher who has mastered the contemporary structure, bounds and outreach of her field. This is a 'must read', and a welcome antidote for all thoseengaged in the caring professions. Richard Whitfield, Human Development Specialist, Educator, Poet and Chairman of Trustees of the Face-to-Face Trust As good an introduction to positive psychology as you can read. A must-read book for all those involved in the education and health industries. Dr Anthony Seldon, Master, Wellington College, Berkshire, UK Positive Psychology in a Nutshell is a comprehensive, user friendly, thoughtful introduction and critique of the field. Simply put, it is the best overview out there that can be read in a couple of sittings. Those with no psychology background find it fascinating and informative; those with serious credentials find it to be a credible overview and critique of the field. Dr Carol Kauffman, Co-founder and Director of the Coaching and Positive Psychology Initiative, Harvard Medical School, USA In a nutshell, I could scarcely put down this intelligent, balanced and irresistible introduction to positive psychology! Dr Sean Cameron, Co-Director, Practitioner Doctorate in Educational Psychology, University College London, UK It is very readable, seductively so, and is no doubt as good an introduction to the subject as you can get ... Emotional wellbeing is complex and there are useful insights here to shore up the flabby phrases tossed around by politicians ... There are some parts of this book I will use and anyone who wants to find out about positive psychology should start here. Mike Shooter is a child psychiatrist and President of BACP, UK When you hear the words 'positive psychology' or 'the science of well-being', do you wonder what it's all about? 'What makes us fulfilled?' and 'Is happiness necessary for a good life?' Discover the latest thinking on the topics of happiness, flow, optimism, motivation, character strengths and love, and learn how to apply it to your life. Ilona Boniwell presents an engaging overview of the science of optimal functioning and well-being, which combines real readability with a broad academic base applied to day-to-day life. Now fully updated and enhanced with new material on how to: Change your mindset Practice mindfulness Develop better resilience Enhance your well-being at work Adopt positive leadership Introducing positive psychology in a friendly, straightforward way, this international bestseller is peppered with many simple tools and tips for daily living that will help you love your life. |
| data science in a nutshell: Practical Natural Language Processing Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, Harshit Surana, 2020-06-17 Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective |
| data science in a nutshell: Process Mining in Action Lars Reinkemeyer, 2020-03-14 This book describes process mining use cases and business impact along the value chain, from corporate to local applications, representing the state of the art in domain know-how. Providing a set of industrial case studies and best practices, it complements academic publications on the topic. Further the book reveals the challenges and failures in order to offer readers practical insights and guidance on how to avoid the pitfalls and ensure successful operational deployment. The book is divided into three parts: Part I provides an introduction to the topic from fundamental principles to key success factors, and an overview of operational use cases. As a holistic description of process mining in a business environment, this part is particularly useful for readers not yet familiar with the topic. Part II presents detailed use cases written by contributors from a variety of functions and industries. Lastly, Part III provides a brief overview of the future of process mining, both from academic and operational perspectives. Based on a solid academic foundation, process mining has received increasing interest from operational businesses, with many companies already reaping the benefits. As the first book to present an overview of successful industrial applications, it is of particular interest to professionals who want to learn more about the possibilities and opportunities this new technology offers. It is also a valuable resource for researchers looking for empirical results when considering requirements for enhancements and further developments. |
| data science in a nutshell: Interpretable Machine Learning with Python Serg Masís, 2021-03-26 A deep and detailed dive into the key aspects and challenges of machine learning interpretability, complete with the know-how on how to overcome and leverage them to build fairer, safer, and more reliable models Key Features Learn how to extract easy-to-understand insights from any machine learning model Become well-versed with interpretability techniques to build fairer, safer, and more reliable models Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models Book DescriptionDo you want to gain a deeper understanding of your models and better mitigate poor prediction risks associated with machine learning interpretation? If so, then Interpretable Machine Learning with Python deserves a place on your bookshelf. We’ll be starting off with the fundamentals of interpretability, its relevance in business, and exploring its key aspects and challenges. As you progress through the chapters, you'll then focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. You’ll also get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, this book will also help you interpret model outcomes using examples. You’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning. What you will learn Recognize the importance of interpretability in business Study models that are intrinsically interpretable such as linear models, decision trees, and Naïve Bayes Become well-versed in interpreting models with model-agnostic methods Visualize how an image classifier works and what it learns Understand how to mitigate the influence of bias in datasets Discover how to make models more reliable with adversarial robustness Use monotonic constraints to make fairer and safer models Who this book is for This book is primarily written for data scientists, machine learning developers, and data stewards who find themselves under increasing pressures to explain the workings of AI systems, their impacts on decision making, and how they identify and manage bias. It’s also a useful resource for self-taught ML enthusiasts and beginners who want to go deeper into the subject matter, though a solid grasp on the Python programming language and ML fundamentals is needed to follow along. |
| data science in a nutshell: Spectral Methods for Data Science Yuxin Chen, 2021 This monograph presents a systematic, yet accessible introduction to spectral methods from a modern statistical perspective. It is essential reading for all students, researchers and practitioners working in Data Science. |
| data science in a nutshell: What Is Data Science? Mike Loukides, 2011-04-10 We've all heard it: according to Hal Varian, statistics is the next sexy job. Five years ago, in What is Web 2.0, Tim O'Reilly said that data is the next Intel Inside. But what does that statement mean? Why do we suddenly care about statistics and about data? This report examines the many sides of data science -- the technologies, the companies and the unique skill sets.The web is full of data-driven apps. Almost any e-commerce application is a data-driven application. There's a database behind a web front end, and middleware that talks to a number of other databases and data services (credit card processing companies, banks, and so on). But merely using data isn't really what we mean by data science. A data application acquires its value from the data itself, and creates more data as a result. It's not just an application with data; it's a data product. Data science enables the creation of data products. |
| data science in a nutshell: Practical Statistics for Data Scientists Peter Bruce, Andrew Bruce, 2017-05-10 Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data |
| data science in a nutshell: Effective Data Science Infrastructure Ville Tuulos, 2022-08-30 Simplify data science infrastructure to give data scientists an efficient path from prototype to production. In Effective Data Science Infrastructure you will learn how to: Design data science infrastructure that boosts productivity Handle compute and orchestration in the cloud Deploy machine learning to production Monitor and manage performance and results Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, Conda, and Docker Architect complex applications for multiple teams and large datasets Customize and grow data science infrastructure Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you’ll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You’ll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science. About the technology Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from startups to the largest enterprises. About the book Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company’s specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems. What's inside Handle compute and orchestration in the cloud Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, AWS, and the Python data ecosystem Architect complex applications that require large datasets and models, and a team of data scientists About the reader For infrastructure engineers and engineering-minded data scientists who are familiar with Python. About the author At Netflix, Ville Tuulos designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure. Table of Contents 1 Introducing data science infrastructure 2 The toolchain of data science 3 Introducing Metaflow 4 Scaling with the compute layer 5 Practicing scalability and performance 6 Going to production 7 Processing data 8 Using and operating models 9 Machine learning with the full stack |
| data science in a nutshell: Evolution and Intelligent Design in a Nutshell Thomas Y. Lo, Paul K. Chien, Eric H. Anderson, 2020-05-19 Are life and the universe a mindless accident--the blind outworking of laws governing cosmic, chemical, and biological evolution? That's the official story many of us were taught somewhere along the way. But what does the science actually say? Drawing on recent discoveries in astronomy, cosmology, chemistry, biology, and paleontology, Evolution and Intelligent Design in a Nutshell shows how the latest scientific evidence suggests a very different story. |
| data science in a nutshell: Think Like a Data Scientist Brian Godsey, 2017-03-09 Summary Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Data collected from customers, scientific measurements, IoT sensors, and so on is valuable only if you understand it. Data scientists revel in the interesting and rewarding challenge of observing, exploring, analyzing, and interpreting this data. Getting started with data science means more than mastering analytic tools and techniques, however; the real magic happens when you begin to think like a data scientist. This book will get you there. About the Book Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you'll put this knowledge together using a structured process for data science. When you've finished, you'll have a strong foundation for a lifetime of data science learning and practice. What's Inside The data science process, step-by-step How to anticipate problems Dealing with uncertainty Best practices in software and scientific thinking About the Reader Readers need beginner programming skills and knowledge of basic statistics. About the Author Brian Godsey has worked in software, academia, finance, and defense and has launched several data-centric start-ups. Table of Contents PART 1 - PREPARING AND GATHERING DATA AND KNOWLEDGE Philosophies of data science Setting goals by asking good questions Data all around us: the virtual wilderness Data wrangling: from capture to domestication Data assessment: poking and prodding PART 2 - BUILDING A PRODUCT WITH SOFTWARE AND STATISTICS Developing a plan Statistics and modeling: concepts and foundations Software: statistics in action Supplementary software: bigger, faster, more efficient Plan execution: putting it all together PART 3 - FINISHING OFF THE PRODUCT AND WRAPPING UP Delivering a product After product delivery: problems and revisions Wrapping up: putting the project away |
| data science in a nutshell: Knowledge in a Nutshell: Quantum Physics Sten Odenwald, 2020-05-01 Quantum theory is at the heart of modern physics, but how does it actually work? NASA scientist and communicator Sten Odenwald demystifies the subject and makes this crucial topic accessible to everyone. Featuring topics such as Schrodinger's cat, the wave-particle duality and the newly emerging theories of quantum gravity, as well as the personalities behind the science, such as Max Planck, Neils Bohr, Werner Heisenberg, Richard Feynman and many more, Knowledge in a Nutshell: Quantum Physics provides an essential introduction to cutting edge science. Presented in an easy-to-understand format, with diagrams, illustrations and simple summary sections at the end of each chapter, this new addition to the 'Knowledge in a Nutshell' series brings clarity to some of the great mysteries of physics. ABOUT THE SERIES: The 'Knowledge in a Nutshell' series by Arcturus Publishing provides engaging introductions to many fields of knowledge, including philosophy, psychology and physics, and the ways in which human kind has sought to make sense of our world. |
| data science in a nutshell: Java in a Nutshell David Flanagan, 1997 Java in a Nutshell, Deluxe Editionis a Java programmer's dream come true in one small package. The heart of this Deluxe Edition is the Java Reference Library on CD-ROM, which brings together five volumes for Java developers and programmers, linking related info across books. It includes:Exploring Java, 2nd Edition,Java Language Reference, 2nd Edition,Java Fundamental Classes Reference,Java AWT Reference, andJava in a Nutshell, 2nd Edition, included both on the CD-ROM and in a companion desktop edition.Java in a Nutshell, Deluxe Editionis an indispensable resource for anyone doing serious programming with Java 1.1. The Java Reference Library alone is also available by subscription on the World Wide Web. Please seehttp://online-books.oreilly.com/books/javaref/for details. The electronic text on the Web and on the CD is fully searchable and includes a complete index to all five volumes. It also includes the sample code found in the printed volumes. Exploring Java, 2nd Editionintroduces the basics of Java 1.1 and offers a clear, systematic overview of the language. It covers the essentials of hot topics like Beans and RMI, as well as writing applets and other applications, such as networking programs, content and protocol handlers, and security managers. TheJava Language Reference, 2nd Editionis a complete reference that describes all aspects of the Java language, including syntax, object-oriented programming, exception handling, multithreaded programming, and differences between Java and C/C++. The second edition covers the new language features that have been added in Java 1.1, such as inner classes, class literals, and instance initializers. TheJava Fundamental Classes Referenceprovides complete reference documentation on the core Java 1.1 classes that comprise thejava.lang,java.io,java.net,java.util,java.text,java.math,java.lang.reflect, andjava.util.zippackages. These classes provide general-purpose functionality that is fundamental to every Java application. TheJava AWT Referenceprovides complete reference documentation on the Abstract Window Toolkit (AWT), a large collection of classes for building graphical user interfaces in Java. Java in a Nutshell, 2nd Edition, the bestselling book on Java and the one most often recommended on the Internet, is a complete quick-reference guide to Java, containing descriptions of all of the classes in the Java 1.1 core API, with a definitive listing of all methods and variables, with the exception of the still-evolving Enterprise APIs. These APIs will be covered in a future volume. Highlights of the library include: History and principles of Java How to integrate applets into the World Wide Web A detailed look into Java's style of object-oriented programming Detailed coverage of all the essential classes injava.lang,java.io,java.util,java.net,java.awt Using threads Network programming Content and protocol handling A detailed explanation of Java's image processing mechanisms Material on graphics primitives and rendering techniques Writing a security manager System requirements: The CD-ROM is readable on all Windows and UNIX platforms. Current implementations of the Java Virtual Machine for the Mac platform do not support the Java search applet in this CD-ROM. Mac users can purchase the World Wide Web version (seehttp://online-books.oreilly.com/books/javaref/for more information). A Web browser that supports HTML 3.2, Java, and JavaScript, such as Netscape 3.0 or Internet Explorer 3.0, is required. |
| data science in a nutshell: Analyzing the Analyzers Harlan Harris, Sean Murphy, Marck Vaisman, 2013-06-10 Despite the excitement around data science, big data, and analytics, the ambiguity of these terms has led to poor communication between data scientists and organizations seeking their help. In this report, authors Harlan Harris, Sean Murphy, and Marck Vaisman examine their survey of several hundred data science practitioners in mid-2012, when they asked respondents how they viewed their skills, careers, and experiences with prospective employers. The results are striking. Based on the survey data, the authors found that data scientists today can be clustered into four subgroups, each with a different mix of skillsets. Their purpose is to identify a new, more precise vocabulary for data science roles, teams, and career paths. This report describes: Four data scientist clusters: Data Businesspeople, Data Creatives, Data Developers, and Data Researchers Cases in miscommunication between data scientists and organizations looking to hire Why T-shaped data scientists have an advantage in breadth and depth of skills How organizations can apply the survey results to identify, train, integrate, team up, and promote data scientists |
| data science in a nutshell: MySQL in a Nutshell Russell J.T. Dyer, 2008-04-15 When you need to find the right SQL keyword or MySQL client command-line option right away, turn to this convenient reference, known for the same speed and flexibility as the system it covers so thoroughly. MySQL is packed with so many capabilities that the odds of remembering a particular function or statement at the right moment are pretty slim. With MySQL in a Nutshell, you get the details you need, day in and day out, in one concise and extremely well organized book. The new edition contains all the commands and programming information for version 5.1, including new features and language interfaces. It's ideal for anyone using MySQL, from novices who need to get up to speed to advanced users who want a handy reference. Like all O'Reilly Nutshell references, it's easy to use and highly authoritative, written by the editor of the MySQL Knowledge Base at MySQL AB, the creator and owner of MySQL. Inside, you'll find: A thorough reference to MySQL statements, functions, and administrative utilities Several tutorial chapters to help newcomers get started Programming language APIs for PHP, Perl, and C Brief tutorials at the beginning of each API chapter to help anyone, regardless of experience level, understand and master unfamiliar territory New chapters on replication, triggers, and stored procedures Plenty of new examples of how MySQL is used in practice Useful tips to help you get through the most difficult subjects Whether you employ MySQL in a mission-critical, heavy-use environment or for applications that are more modest, this book puts a wealth of easy-to-find information at your fingertips, saving you hundreds of hours of trial and error and tedious online searching. If you're ready to take advantage of everything MySQL has to offer, MySQL in a Nutshell has precisely what it takes. |
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Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business …
Lecture 1 Introduction to Data Science - Stanford University
Datasci 112 is now the gateway course for the B.A. and the B.S. in Data Science. This course is designed for freshmen and sophomores who are exploring Data Science as a major, but …
Intro to Data Science - Duke University
What is Data Science? Data science is an emer ging discipline that builds on t ools from mathematics, statistics, and computer science t o extract knowledge from data.
Introduction to Data Science - Guide to Intelligent Data Science
Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.
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CSE217 INTRODUCTION TO DATA SCIENCE LECTURE 1: DS & ML. Spring 2019 Marion Neumann. LECTURE 1: DS & ML. WHAT IS DATA SCIENCE? 2. …solving problems with …
1.1 What is data science? - University of Arizona
Data science is the practice of using data to try to understand and solve real-world prob-lems. This concept isn’t exactly new; people have been analyzing sales figures and trends since the …
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convincing argument that data science is a course distinct from applied statistics The American Statistician Modern Data Science with R is a comprehensive data science textbook for …
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Core courses ensure adequate coverage in essential Data Science knowledge and skills, while the wide variety of electives allows students to customize their Data Science degree progra m …
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Welcome to the online book Introduction to Data Science. This book is created to provide a great resource for asynchronous online learning to deal with the current pandemic, where physical …
Introduction to Data Science A Beginner's Guide
Data science is about using already stored raw and unstructured data in organization’s repository, which process through systematic, programming and business skills in creative ways to …
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In addition to providing basics of data and data processing, the book teaches standard tools and techniques. It also examines implications of the use of data in areas such as privacy, ethics, …
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Students in the 90-credit track must complete the M.S. Data Science Thesis option. Student and Academic Advisor prepare and submit Plan of Study for Ph.D. each year . Students must meet …
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Researchers and innovators openly share data across technologies, disciplines, and countries to address the grand challenges of society. RDA builds the social and technical bridges that …
CHAPTER INTRODUCTION TO DATA SCIENCE - Wiley
Data science combines the • data‐driven approach of statistical data analysis, • the computational power and programming acumen of computer science, and • domain‐specific business …
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An interdisciplinary field, data science uses scientific systems, algorithms, processes, and other methods to gain insight and knowledge from data in different forms, both unstructured and …
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Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of …
INTRODUCTION TO DATA SCIENCE LECTURE NOTES UNIT
Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business …
Lecture 1 Introduction to Data Science - Stanford University
Datasci 112 is now the gateway course for the B.A. and the B.S. in Data Science. This course is designed for freshmen and sophomores who are exploring Data Science as a major, but …
Intro to Data Science - Duke University
What is Data Science? Data science is an emer ging discipline that builds on t ools from mathematics, statistics, and computer science t o extract knowledge from data.
Introduction to Data Science - Guide to Intelligent Data …
Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.
CSE217 INTRODUCTION TO DATA SCIENCE LECTURE 1: DS
CSE217 INTRODUCTION TO DATA SCIENCE LECTURE 1: DS & ML. Spring 2019 Marion Neumann. LECTURE 1: DS & ML. WHAT IS DATA SCIENCE? 2. …solving problems with …
1.1 What is data science? - University of Arizona
Data science is the practice of using data to try to understand and solve real-world prob-lems. This concept isn’t exactly new; people have been analyzing sales figures and trends since the …
Data Science In A Nutshell Full PDF - cie-advances.asme.org
convincing argument that data science is a course distinct from applied statistics The American Statistician Modern Data Science with R is a comprehensive data science textbook for …
DATA SCIENCE M.S. PROGRAM - web.wpi.edu
Core courses ensure adequate coverage in essential Data Science knowledge and skills, while the wide variety of electives allows students to customize their Data Science degree progra m …
Introduction to Data Science - GitHub Pages
Welcome to the online book Introduction to Data Science. This book is created to provide a great resource for asynchronous online learning to deal with the current pandemic, where physical …
Introduction to Data Science A Beginner's Guide
Data science is about using already stored raw and unstructured data in organization’s repository, which process through systematic, programming and business skills in creative ways to …
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Data science = statistics + data processing + machine learning + scientific inquiry + visualization + business analytics + big data + ... What is data science? What is data science not? Machine …
The Complete Collection of Data Science Cheat Sheets
VIP cheat sheets are a data science goldmine that contains bit size information about data science and its core subjects. The cheat sheets include the basic information about data …
A Hands-On Introduction to Data Science - Cambridge …
In addition to providing basics of data and data processing, the book teaches standard tools and techniques. It also examines implications of the use of data in areas such as privacy, ethics, …
UNIT-2 SYLLABUS The data science process: Overview of the …
The data science process: Overview of the data science process: Don’t be a slave to the process, Defining research goals and creating a project charter: Spend time understanding the goals …
WPI DATA SCIENCE Ph.D. MILESTONES in a NUTSHELL
Students in the 90-credit track must complete the M.S. Data Science Thesis option. Student and Academic Advisor prepare and submit Plan of Study for Ph.D. each year . Students must meet …
The Research Data Alliance (RDA) in a nutshell
Researchers and innovators openly share data across technologies, disciplines, and countries to address the grand challenges of society. RDA builds the social and technical bridges that …
CHAPTER INTRODUCTION TO DATA SCIENCE - Wiley
Data science combines the • data‐driven approach of statistical data analysis, • the computational power and programming acumen of computer science, and • domain‐specific business …
Data Science Principles Syllabus - Harvard Online
Data Science Principles makes the fundamental topics in data science approachable and relevant by using real-world examples and prompts learners to think critically about applying these new …
Data Science from Scratch: The #1 Data Science Guide for …
An interdisciplinary field, data science uses scientific systems, algorithms, processes, and other methods to gain insight and knowledge from data in different forms, both unstructured and …
Basics of Data Science - S. T. Hindu College Of Arts & Science
Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of …
