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Designing Data-Intensive Applications: The Big Ideas Behind Relia...
by Martin Kleppmann

Language

English

Pages

616

Publication Date

March 16, 2017

Product Description
Customer Reviews
<p>Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?</p><p>In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.</p><ul><li>Peer under the hood of the systems you already use, and learn how to use and operate them more effectively</li><li>Make informed decisions by identifying the strengths and weaknesses of different tools</li><li>Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity</li><li>Understand the distributed systems research upon which modern databases are built</li><li>Peek behind the scenes of major online services, and learn from their architectures</li></ul>
Database Internals: A Deep Dive into How Distributed Data Systems...
by Alex Petrov

Language

English

Pages

376

Publication Date

September 13, 2019

Product Description
Customer Reviews
<p>When it comes to choosing, using, and maintaining a database, understanding its internals is essential. But with so many distributed databases and tools available today, it’s often difficult to understand what each one offers and how they differ. With this practical guide, Alex Petrov guides developers through the concepts behind modern database and storage engine internals.</p><p>Throughout the book, you’ll explore relevant material gleaned from numerous books, papers, blog posts, and the source code of several open source databases. These resources are listed at the end of parts one and two. You’ll discover that the most significant distinctions among many modern databases reside in subsystems that determine how storage is organized and how data is distributed.</p><p>This book examines:</p><ul><li><b>Storage engines:</b> Explore storage classification and taxonomy, and dive into B-Tree-based and immutable Log Structured storage engines, with differences and use-cases for each</li><li><b>Storage building blocks:</b> Learn how database files are organized to build efficient storage, using auxiliary data structures such as Page Cache, Buffer Pool and Write-Ahead Log</li><li><b>Distributed systems:</b> Learn step-by-step how nodes and processes connect and build complex communication patterns</li><li><b>Database clusters:</b> Which consistency models are commonly used by modern databases and how distributed storage systems achieve consistency</li></ul>
Data Science from Scratch: First Principles with Python
by Joel Grus

Language

English

Pages

408

Publication Date

April 12, 2019

Product Description
Customer Reviews
<p>To really learn data science, you should not only master the tools—data science libraries, frameworks, modules, and toolkits—but also understand the ideas and principles underlying them. Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.</p><p>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 the hacking skills you need to get started as a data scientist. Packed with new material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.</p><ul><li>Get a crash course in Python</li><li>Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science</li><li>Collect, explore, clean, munge, and manipulate data</li><li>Dive into the fundamentals of machine learning</li><li>Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering</li><li>Explore recommender systems, natural language processing, network analysis, MapReduce, and databases</li></ul>
The Definitive Guide to DAX: Business intelligence for Microsoft ...
by , Alberto Ferrari

Language

English

Pages

768

Publication Date

July 02, 2019

Product Description
Customer Reviews
<p>Now expanded and updated with modern best practices, this is the most complete guide to Microsoft’s DAX language for business intelligence, data modeling, and analytics. Expert Microsoft BI consultants Marco Russo and Alberto Ferrari help you master everything from table functions through advanced code and model optimization. You’ll learn exactly what happens under the hood when you run a DAX expression, and use this knowledge to write fast, robust code. This edition focuses on examples you can build and run with the free Power BI Desktop, and helps you make the most of the powerful syntax of variables (VAR) in Power BI, Excel, or Analysis Services. Want to leverage all of DAX’s remarkable capabilities? This no-compromise “deep dive” is exactly what you need.</p> <p> <br /> </p> <p>Perform powerful data analysis with DAX for Power BI, SQL Server, and Excel</p> <p>·         Master core DAX concepts, including calculated columns, measures, and calculation groups</p> <p>·         Work efficiently with basic and advanced table functions</p> <p>·         Understand evaluation contexts and the CALCULATE and CALCULATETABLE functions</p> <p>·         Perform time-based calculations</p> <p>·         Use calculation groups and calculation items</p> <p>·         Use syntax of variables (VAR) to write more readable, maintainable code</p> <p>·         Express diverse and unusual relationships with DAX, including many-to-many relationships and bidirectional filters</p> <p>·         Master advanced optimization techniques, and improve performance in aggregations</p> <p>·         Optimize data models to achieve better compression</p> <p> </p> <p>·         Measure DAX query performance with DAX Studio and learn how to optimize your DAX </p>
SQL Antipatterns: Avoiding the Pitfalls of Database Programming (...
by Bill Karwin

Language

English

Pages

329

Publication Date

June 25, 2010

Product Description
Customer Reviews
<p>Bill Karwin has helped thousands of people write better SQL and build stronger relational databases. Now he's sharing his collection of antipatterns--the most common errors he's identified in those thousands of requests for help.<br /><br />Most developers aren't SQL experts, and most of the SQL that gets used is inefficient, hard to maintain, and sometimes just plain wrong. This book shows you all the common mistakes, and then leads you through the best fixes. What's more, it shows you what's <i>behind</i> these fixes, so you'll learn a lot about relational databases along the way.<br /><br />Each chapter in this book helps you identify, explain, and correct a unique and dangerous antipattern. The four parts of the book group the anti​patterns in terms of logical database design, physical database design, queries, and application development. <br /><br />The chances are good that your application's database layer already contains problems such as <i>Index Shotgun</i>, <i>Keyless Entry</i>, <i>Fear of the Unknown</i>, and <i>Spaghetti Query</i>. This book will help you and your team find them. Even better, it will also show you how to fix them, and how to avoid these and other problems in the future.<br /><br />SQL Antipatterns gives you a rare glimpse into an SQL expert's playbook. Now you can stamp out these common database errors once and for all.<br /><br />Whatever platform or programming language you use, whether you're a junior programmer or a Ph.D., <i>SQL Antipatterns</i> will show you how to design and build databases, how to write better database queries, and how to integrate SQL programming with your application like an expert. You'll also learn the best and most current technology for full-text search, how to design code that is resistant to SQL injection attacks, and other techniques for success.</p>
Trustworthy Online Controlled Experiments: A Practical Guide to A...
by , Ya Xu

Language

English

Pages

200

Publication Date

March 31, 2020

Product Description
Customer Reviews
Getting numbers is easy; getting numbers you can trust is hard. 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.<br /><br />"A/B testing is the gold standard of creating verifiable and repeatable experiments, and <b>this book is its definitive text</b>" -- <b>Steve Blank</b>, father of modern entrepreneurship, author of The Startup Owner's Manual and The Four Steps to the Epiphany<br /><br />"This book is a <b>great resource for executives, leaders, researchers or engineers</b> looking to use online controlled experiments" -- <b>Harry Shum</b>, Executive Vice President, Microsoft Artificial Intelligence and Research Group<br /><br />"A great book that is both <b>rigorous and accessible</b>. Readers will learn how to bring trustworthy controlled experiments, which have <b>revolutionized internet product development</b>, to their organizations" -- <b>Adam D'Angelo</b>, Co-founder and CEO of Quora and prior CTO of Facebook <br /><br />"Kohavi, Tang and Xu have a <b>wealth of experience and excellent advice</b> to convey, so the book has lots of practical real world examples and lessons learned over many years of the application of these techniques at scale." -- <b>Jeff Dean</b>, Google Senior Fellow, and SVP, Google Research<br /><br />"<b>The secret sauce</b> for a successful online business is experimentation. But it is a secret no longer. Here three <b>masters of the art</b> describe the ABCs of A/B testing so that you too can continuously improve your online services." -- <b>Hal Varian</b>, Chief Economist, Google, and author of Intermediate Microeconomics: A Modern Approach<br /><br />"This is <b>the new bible</b> of how to get from data to decisions in the digital age." -- <b>Scott Cook</b>, Intuit Co-founder & Chairman of the executive committee<br /><br />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<ul><li> Use the scientific method to evaluate hypotheses using controlled experiments.</li><li> Define key metrics and ideally an Overall Evaluation Criterion.</li><li> Test for trustworthiness of the results and alert experimentersto violated assumptions. </li><li> Build a scalable platform that lowers the marginal cost of experiments close to zero.</li><li> Avoid pitfalls like carryover effects and Twyman's law * Understand how statistical issues play out in practice.</li></ul>
The Hundred-Page Machine Learning Book
by Andriy Burkov

Language

English

Pages

Publication Date

January 12, 2019

Product Description
Customer Reviews
<b>WARNING: will not work on e-ink Kindle devices!</b><br /><br /><b>Peter Norvig</b>, Research Director at Google, co-author of <b>AIMA</b>, the most popular AI textbook in the world: <i>"Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics — both theory and practice — that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."</i><br /><br /><b>Aurélien Géron</b>, Senior AI Engineer, author of the bestseller <b>Hands-On Machine Learning with Scikit-Learn and TensorFlow</b>: <i>"The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."</i><br /><br /><b>Karolis Urbonas</b>, Head of Data Science at <b>Amazon</b>: <i>"A great introduction to machine learning from a world-class practitioner."</i> <br /><br /><b>Chao Han</b>, VP, Head of R&D at <b>Lucidworks</b>: <i>"I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."</i><br /><br /><b>Sujeet Varakhedi</b>, Head of Engineering at <b>eBay</b>: <i>"Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''</i><br /><br /><b>Deepak Agarwal</b>, VP of Artificial Intelligence at <b>LinkedIn</b>: <i>"A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''</i><br /><br /><b>Vincent Pollet</b>, Head of Research at <b>Nuance</b>: <i>"The Hundred-Page Machine Learning Book is an excellent read to get started with Machine Learning.''</i><br /><br /><b>Gareth James</b>, Professor of Data Sciences and Operations, co-author of the bestseller <b>An Introduction to Statistical Learning, with Applications in R</b>: <i>"This is a compact “how to do data science” manual and I predict it will become a go-to resource for academics and practitioners alike. At 100 pages (or a little more), the book is short enough to read in a single sitting. Yet, despite its length, it covers all the major machine learning approaches, ranging from classical linear and logistic regression, through to modern support vector machines, deep learning, boosting, and random forests. There is also no shortage of details on the various approaches and the interested reader can gain further information on any particular method via the innovative companion book wiki. The book does not assume any high level mathematical or statistical training or even programming experience, so should be accessible to almost anyone willing to invest the time to learn about these methods. It should certainly be required reading for anyone starting a PhD program in this area and will serve as a useful reference as they progress further. Finally, the book illustrates some of the algorithms using Python code, one of the most popular coding languages for machine learning. I would highly recommend “The Hundred-Page Machine Learning Book” for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."</i><br /><br /><b>Everything you really need to know in Machine Learning in a hundred pages.</b><br /><br />This is the first of its kind <i>"read first, buy later"</i> book. You can find the book online, read it, and then come back to pay for it if you liked the book or found it useful for your work, business or studies.
Data Science for Business: What You Need to Know about Data Minin...
by , Tom Fawcett

Language

English

Pages

414

Publication Date

July 27, 2013

Product Description
Customer Reviews
<p>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 guide also helps you understand the many data-mining techniques in use today.</p><p>Based on an MBA course Provost has taught at New York University over the past ten years, <i>Data Science for Business</i> provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.</p><ul><li>Understand how data science fits in your organization—and how you can use it for competitive advantage</li><li>Treat data as a business asset that requires careful investment if you’re to gain real value</li><li>Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way</li><li>Learn general concepts for actually extracting knowledge from data</li><li>Apply data science principles when interviewing data science job candidates</li></ul>
The Data Warehouse Toolkit: The Definitive Guide to Dimensional M...
by , Margy Ross

Language

English

Pages

600

Publication Date

July 01, 2013

Product Description
Customer Reviews
<p><b>Updated new edition of Ralph Kimball's groundbreaking book on dimensional modeling for data warehousing and business intelligence!</b></p> <p>The first edition of Ralph Kimball's <i>The Data Warehouse Toolkit</i> introduced the industry to dimensional modeling, and now his books are considered the most authoritative guides in this space. This new third edition is a complete library of updated dimensional modeling techniques, the most comprehensive collection ever. It covers new and enhanced star schema dimensional modeling patterns, adds two new chapters on ETL techniques, includes new and expanded business matrices for 12 case studies, and more.</p> <ul> <li>Authored by Ralph Kimball and Margy Ross, known worldwide as educators, consultants, and influential thought leaders in data warehousing and business intelligence</li> <li>Begins with fundamental design recommendations and progresses through increasingly complex scenarios</li> <li>Presents unique modeling techniques for business applications such as inventory management, procurement, invoicing, accounting, customer relationship management, big data analytics, and more</li> <li>Draws real-world case studies from a variety of industries, including retail sales, financial services, telecommunications, education, health care, insurance, e-commerce, and more</li> </ul> <p>Design dimensional databases that are easy to understand and provide fast query response with <i>The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition</i>.</p>
The Model Thinker: What You Need to Know to Make Data Work for Yo...
by Scott E. Page

Language

English

Pages

398

Publication Date

November 27, 2018

Product Description
Customer Reviews
<b>How anyone can become a data ninja</b><br /><br /> 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 <i>The Model Thinker</i>, 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. <i>The Model Thinker </i>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.<br /><br />

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