Book picks similar to
Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks by Jonathan Schwabish
non-fiction
data
data-science
dataviz
Artificial Intelligence: A Modern Approach
Stuart Russell - 1994
The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. *NEW-Nontechnical learning material-Accompanies each part of the book. *NEW-The Internet as a sample application for intelligent systems-Added in several places including logical agents, planning, and natural language. *NEW-Increased coverage of material - Includes expanded coverage of: default reasoning and truth maintenance systems, including multi-agent/distributed AI and game theory; probabilistic approaches to learning including EM; more detailed descriptions of probabilistic inference algorithms. *NEW-Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises. *NEW-On-line Java software. *Makes it easy for students to do projects on the web using intelligent agents. *A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents. *Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making pa
Ambient Findability: What We Find Changes Who We Become
Peter Morville - 2005
Written by Peter Morville, author of the groundbreaking Information Architecture for the World Wide Web, the book defines our current age as a state of unlimited findability. In other words, anyone can find anything at any time. Complete navigability.Morville discusses the Internet, GIS, and other network technologies that are coming together to make unlimited findability possible. He explores how the melding of these innovations impacts society, since Web access is now a standard requirement for successful people and businesses. But before he does that, Morville looks back at the history of wayfinding and human evolution, suggesting that our fear of being lost has driven us to create maps, charts, and now, the mobile Internet.The book's central thesis is that information literacy, information architecture, and usability are all critical components of this new world order. Hand in hand with that is the contention that only by planning and designing the best possible software, devices, and Internet, will we be able to maintain this connectivity in the future. Morville's book is highlighted with full color illustrations and rich examples that bring his prose to life.Ambient Findability doesn't preach or pretend to know all the answers. Instead, it presents research, stories, and examples in support of its novel ideas. Are we truly at a critical point in our evolution where the quality of our digital networks will dictate how we behave as a species? Is findability indeed the primary key to a successful global marketplace in the 21st century and beyond. Peter Morville takes you on a thought-provoking tour of these memes and more -- ideas that will not only fascinate but will stir your creativity in practical ways that you can apply to your work immediately.
The Hundred-Page Machine Learning Book
Andriy Burkov - 2019
During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.
Machine Learning: A Probabilistic Perspective
Kevin P. Murphy - 2012
Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Mining the Social Web: Analyzing Data from Facebook, Twitter, LinkedIn, and Other Social Media Sites
Matthew A. Russell - 2011
You’ll learn how to combine social web data, analysis techniques, and visualization to find what you’ve been looking for in the social haystack—as well as useful information you didn’t know existed.Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.Get a straightforward synopsis of the social web landscapeUse adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, LinkedIn, and Google+Learn how to employ easy-to-use Python tools to slice and dice the data you collectExplore social connections in microformats with the XHTML Friends NetworkApply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detectionBuild interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits"A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data." --Alex Martelli, Senior Staff Engineer, Google
Database Design for Mere Mortals: A Hands-On Guide to Relational Database Design
Michael J. Hernandez - 1996
You d be up to your neck in normal forms before you even had a chance to wade. When Michael J. Hernandez needed a database design book to teach mere mortals like himself, there were none. So he began a personal quest to learn enough to write one. And he did.Now in its Second Edition, Database Design for Mere Mortals is a miracle for today s generation of database users who don t have the background -- or the time -- to learn database design the hard way. It s also a secret pleasure for working pros who are occasionally still trying to figure out what they were taught.Drawing on 13 years of database teaching experience, Hernandez has organized database design into several key principles that are surprisingly easy to understand and remember. He illuminates those principles using examples that are generic enough to help you with virtually any application.Hernandez s goals are simple. You ll learn how to create a sound database structure as easily as possible. You ll learn how to optimize your structure for efficiency and data integrity. You ll learn how to avoid problems like missing, incorrect, mismatched, or inaccurate data. You ll learn how to relate tables together to make it possible to get whatever answers you need in the future -- even if you haven t thought of the questions yet.If -- as is often the case -- you already have a database, Hernandez explains how to analyze it -- and leverage it. You ll learn how to identify new information requirements, determine new business rules that need to be applied, and apply them.Hernandez starts with an introduction to databases, relational databases, and the idea and objectives of database design. Next, you ll walk through the key elements of the database design process: establishing table structures and relationships, assigning primary keys, setting field specifications, and setting up views. Hernandez s extensive coverage of data integrity includes a full chapter on establishing business rules and using validation tables.Hernandez surveys bad design techniques in a chapter on what not to do -- and finally, helps you identify those rare instances when it makes sense to bend or even break the conventional rules of database design.There s plenty that s new in this edition. Hernandez has gone over his text and illustrations with a fine-tooth comb to improve their already impressive clarity. You ll find updates to reflect new advances in technology, including web database applications. There are expanded and improved discussions of nulls and many-to-many relationships; multivalued fields; primary keys; and SQL data type fields. There s a new Quick Reference database design flowchart. A new glossary. New review questions at the end of every chapter.Finally, it s worth mentioning what this book isn t. It isn t a guide to any specific database platform -- so you can use it whether you re running Access, SQL Server, or Oracle, MySQL or PostgreSQL. And it isn t an SQL guide. (If that s what you need, Michael J. Hernandez has also coauthored the superb SQL Queries for Mere Mortals). But if database design is what you need to learn, this book s worth its weight in gold. Bill CamardaBill Camarda is a consultant, writer, and web/multimedia content developer. His 15 books include Special Edition Using Word 2000 and Upgrading & Fixing Networks for Dummies, Second Edition.
Natural Language Processing with Python
Steven Bird - 2009
With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.
Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists
Philipp K. Janert - 2010
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 variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora
Python Data Science Handbook: Tools and Techniques for Developers
Jake Vanderplas - 2016
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
All of Statistics: A Concise Course in Statistical Inference
Larry Wasserman - 2003
But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.
Applied Multivariate Statistical Analysis
Richard A. Johnson - 1982
of Wisconsin-Madison) and Wichern (Texas A&M U.) present the newest edition of this college text on the statistical methods for describing and analyzing multivariate data, designed for students who have taken two or more statistics courses. The fifth edition includes the addition of seve
The Information Design Handbook
Jenn Visocky O'Grady - 2008
The Information Design Handbook celebrates graphics that are exemplars of communication and esthetics, and reveals the thought processes and design skills behind them. This comprehensive guide to creating information graphics is packed with essential design principles, case studies, color palettes, trouble-shooting tips, and much more. Designers will learn to achieve graphics that are visually striking yet concise and supremely funcitional with this must-have resource.
Say It with Charts: The Executive's Guide to Visual Communication
Gene Zelazny - 1987
What hasn't changed, however, are the basics behind creating a powerful visual - what to say, why to say it, and how to say it for the most impact. In Say It With Charts, Fourth Edition --the latest, cutting-edge edition of his best-selling presentation guide -- Gene Zelazny reveals time-tested tips for preparing effective presentations. Then, this presentation guru shows you how to combine those tips with today's hottest technologies for sharper, stronger visuals. Look to this comprehensive presentation encyclopedia for information on:* How to prepare different types of charts -- pie, bar, column, line, or dot -- and when to use each * Lettering size, color choice, appropriate chart types, and more * Techniques for producing dramatic eVisuals using animation, scanned images, sound, video, and links to pertinent websites
Threat Modeling: Designing for Security
Adam Shostack - 2014
Dobbs Jolt Award Finalist since Bruce Schneier's Secrets and Lies and Applied Cryptography!Adam Shostack is responsible for security development lifecycle threat modeling at Microsoft and is one of a handful of threat modeling experts in the world. Now, he is sharing his considerable expertise into this unique book. With pages of specific actionable advice, he details how to build better security into the design of systems, software, or services from the outset. You'll explore various threat modeling approaches, find out how to test your designs against threats, and learn effective ways to address threats that have been validated at Microsoft and other top companies.Systems security managers, you'll find tools and a framework for structured thinking about what can go wrong. Software developers, you'll appreciate the jargon-free and accessible introduction to this essential skill. Security professionals, you'll learn to discern changing threats and discover the easiest ways to adopt a structured approach to threat modeling.Provides a unique how-to for security and software developers who need to design secure products and systems and test their designs Explains how to threat model and explores various threat modeling approaches, such as asset-centric, attacker-centric and software-centric Provides effective approaches and techniques that have been proven at Microsoft and elsewhere Offers actionable how-to advice not tied to any specific software, operating system, or programming language Authored by a Microsoft professional who is one of the most prominent threat modeling experts in the world As more software is delivered on the Internet or operates on Internet-connected devices, the design of secure software is absolutely critical. Make sure you're ready with Threat Modeling: Designing for Security.
Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing
Ron Kohavi - 2020
This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to - Use the scientific method to evaluate hypotheses using controlled experiments - Define key metrics and ideally an Overall Evaluation Criterion - Test for trustworthiness of the results and alert experimenters to violated assumptions - Build a scalable platform that lowers the marginal cost of experiments close to zero - Avoid pitfalls like carryover effects and Twyman's law - Understand how statistical issues play out in practice.