Best Data Modeling & Design

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. "This timely book says out loud what has finally become apparent: in the modern world, Data is Business, and you can no longer think business without thinking data. Read this book and you will understand the Science behind thinking data." "A great book for business managers who lead or interact with data scientists, who wish to better understand the principles and algorithms available without the technical details of single-disciplinary books." What you need to know about data mining and data-analytic thinking.

Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures. The big ideas behind reliable, scalable and maintainable systems Martin is a researcher in distributed systems at the University of Cambridge.

Most books, and other information on machine learning, that I have seen fall into one of two categories, they are either textbooks that explain an algorithm in a way similar to "And then the algorithm optimizes this loss function" or they focus entirely on how to set up code to use the algorithm and how to tune the parameters. An overview of decision trees and random forests A manual example of how a human would classify a dataset, compared to how a decision tree would work How a decision tree works, and why it is prone to overfitting How decision trees get combined to form a random forest How to use that random forest to classify data and make predictions How to determine how many trees to use in a random forest Just where does the "randomness" come from Out of Bag Errors & Cross Validation - how good of a fit did the machine learning algorithm make? Gini Criteria & Entropy Criteria - how to tell which split on a decision tree is best among many possible choices And More.

Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures. Martin is a researcher in distributed systems at the University of Cambridge.

Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, the firm that now manages the electric car sharing service Autolib'.Before this he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), defense (Canada's DOD), and healthcare (blood transfusion).

You'll be guided through the process from beginning to end with practical and relevant legal information so that you can make the best decisions for you and your family. Quicken WillMaker Plus 2017 is the original will-writing software, written and updated regularly by Nolo's expert attorneys. Plus, register your copy of Quicken WillMaker Plus 2017 and get a free one-year subscription to Nolo's Online Living Trust. Describe your preferences for burial, cremation, memorials, obituaries, and more, and select someone to oversee your final arrangements. Use these documents to give them information about everything from bank accounts to the names of people you'd like contacted in the event of your illness or death. Personal Finance Documents Over a dozen forms let you handle common financial situations, such as lending money to friends or family, creating a bill of sale, and closing a credit card account. Home & Family Documents Practical forms you can use every day to help run your home and keep your family safe, including authorizations and agreements, promissory notes, limited powers of attorney, and child and elder care forms. * If you do not have a CD-ROM drive, contact Nolo's Tech Support and upon proof of purchase we’ll promptly send you a download link. It is the most respected estate planning tool on the market.

Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience. At JPMorgan Chase, she has helped improve our capabilities to explain complicated analysis to executive management and the regulators with whom we work. My team is already using the lessons Cole teaches to move people to action as they see new pearls of understanding and make a difference in the lives of others. If you are a beginner in visualization, or if you struggle to produce good charts in your everyday job with tools like Excel, Tableau, Qlik, and the like, this is a great place to start learning the core principles ." Cole Nussbaumer Knaflic understands this and has written a straightforward, accessible guide that will help anyone who communicates with data connect more effectively with their audience. ".

Most books, and other information on machine learning, that I have seen fall into one of two categories, they are either textbooks that explain an algorithm in a way similar to "And then the algorithm optimizes this loss function" or they focus entirely on how to set up code to use the algorithm and how to tune the parameters. An overview of decision trees and random forests A manual example of how a human would classify a dataset, compared to how a decision tree would work How a decision tree works, and why it is prone to overfitting How decision trees get combined to form a random forest How to use that random forest to classify data and make predictions How to determine how many trees to use in a random forest Just where does the "randomness" come from Out of Bag Errors & Cross Validation - how good of a fit did the machine learning algorithm make? Gini Criteria & Entropy Criteria - how to tell which split on a decision tree is best among many possible choices And More.

This is the eBook version of the printed book. Essential Information about Algorithms and Data Structures. The latest version of Sedgewick’s best-selling series, reflecting an indispensable body of knowledge developed over the past several decades. Full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing, including fifty algorithms every programmer should know. New Java implementations written in an accessible modular programming style, where all of the code is exposed to the reader and ready to use. Algorithms are studied in the context of important scientific, engineering, and commercial applications. Clients and algorithms are expressed in real code, not the pseudo-code found in many other books. Engages reader interest with clear, concise text, detailed examples with visuals, carefully crafted code, historical and scientific context, and exercises at all levels. Develops precise statements about performance, supported by appropriate mathematical models and empirical studies validating those models. Robert Sedgewick has been a Professor of Computer Science at Princeton University since 1985, where he was the founding Chairman of the Department of Computer Science.

Using the Smarter Way to Learn method, you actually learn HTML/CSS, you don’t just read about it. Read the reviews that call The Smarter Way of learning fun, involving, frustration-free, and confidence-building. Then, if you want to go beyond reading about HTML & CSS and actually learn the skills, do it the smarter way.

The added chapters contain (1) a crisp condensation of all the propositions asserted in the original book, including Brooks' central argument in The Mythical Man-Month: that large programming projects suffer management problems different from small ones due to the division of labor; that the conceptual integrity of the product is therefore critical; and that it is difficult but possible to achieve this unity; (2) Brooks' view of these propositions a generation later; (3) a reprint of his classic 1986 paper "No Silver Bullet"; and (4) today's thoughts on the 1986 assertion, "There will be no silver bullet within ten years." My co-authors of that study, and our executive secretary, Robert L. Patrick, were invaluable in bringing me back into touch with real-world large software projects. In preparing my retrospective and update of The Mythical Man-Month, I was struck by how few of the propositions asserted in it have been critiqued, proven, or disproven by ongoing software engineering research and experience. In hopes that these bald statements will invite arguments and facts to prove, disprove, update, or refine those propositions, I have included this outline as Chapter 18. For a wonderful willingness to share views, to comment thoughtfully on drafts, and to re-educate me, I am indebted to Barry Boehm, Ken Brooks, Dick Case, James Coggins, Tom DeMarco, Jim McCarthy, David Parnas, Earl Wheeler, and Edward Yourdon. I thank Gordon Bell, Bruce Buchanan, Rick Hayes-Roth, my colleagues on the Defense Science Board Task Force on Military Software, and, most especially, David Parnas for their insights and stimulating ideas for, and Rebekah Bierly for technical production of, the paper printed here as Chapter 16. Analyzing the software problem into the categories of essence and accident was inspired by Nancy Greenwood Brooks, who used such analysis in a paper on Suzuki violin pedagogy. Two persons' contributions should be especially cited: Norman Stanton, then Executive Editor, and Herbert Boes, then Art Director. Boes developed the elegant style, which one reviewer especially cited: "wide margins, and imaginative use of typeface and layout."

Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details. He was also a founder and CTO of Wifirst from 2002 to 2012, a leading Wireless ISP in France, and a founder and CTO of Polyconseil in 2001, the firm that now manages the electric car sharing service Autolib'.Before this he worked as an engineer in a variety of domains: finance (JP Morgan and Société Générale), defense (Canada's DOD), and healthcare (blood transfusion).

In CODE, they show us the ingenious ways we manipulate language and invent new means of communicating with each other. Charles Petzold's latest book, Code: The Hidden Language of Computer Hardware and Software , crosses over into general-interest nonfiction from his usual programming genre. From Louis Braille's development of his eponymous raised-dot code to Intel Corporation's release of its early microprocessors, Petzold presents stories of people trying to communicate with (and by means of) mechanical and electrical devices. The real value of Code is in its explanation of technologies that have been obscured for years behind fancy user interfaces and programming environments, which, in the name of rapid application development, insulate the programmer from the machine.

User experience (UX) strategy requires a careful blend of business strategy and UX design, but until now, there hasn’t been an easy-to-apply framework for executing it.

Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart , author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. "When Mr. Foreman interviewed for a job at my company, he arrived dressed in a 'Kentucky Colonel' kind of suit and spoke about nonsensical things like barbecue, lasers, and orange juice pulp. After reading this book, you too will learn how to use math and basic spreadsheet formulas to improve your business or, at the very least, how to trick senior executives into hiring you as their data scientist." This book shows you the significant data science techniques, how they work, how to use them, and how they benefit your business, large or small. Artificial intelligence using the general linear model, ensemble methods, and naive Bayes Clustering via k-means, spherical k-means, and graph modularity Mathematical optimization, including non-linear programming and genetic algorithms Working with time series data and forecasting with exponential smoothing Using Monte Carlo simulation to quantify and address risk Detecting outliers in single or multiple dimensions Exploring the data-science-focused R language. As an analytics consultant, John has created data science solutions for The Coca-Cola Company, Royal Caribbean International, Intercontinental Hotels Group, Dell, the Department of Defense, the IRS, and the FBI.