Written by Wes McKinney, the main author of the pandas library, this hands-on book is packed with practical cases studies. It’s ideal for analysts new to Python and for Python programmers new to scientific computing.Use the IPython interactive shell as your primary development environmentLearn basic and advanced NumPy (Numerical Python) featuresGet started with data analysis tools in the pandas libraryUse high-performance tools to load, clean, transform, merge, and reshape dataCreate scatter plots and static or interactive visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsMeasure data by points in time, whether it’s specific instances, fixed periods, or intervalsLearn how to solve problems in web analytics, social sciences, finance, and economics, through detailed examples
This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
This book supersedes ISBN 9780596550066, from O'Reilly.
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 PythonLearn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data scienceCollect, explore, clean, munge, and manipulate dataDive into the fundamentals of machine learningImplement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clusteringExplore recommender systems, natural language processing, network analysis, MapReduce, and databases
Updated to reflect recent advances in MySQL and InnoDB performance, features, and tools, this third edition not only offers specific examples of how MySQL works, it also teaches you why this system works as it does, with illustrative stories and case studies that demonstrate MySQL’s principles in action. With this book, you’ll learn how to think in MySQL.Learn the effects of new features in MySQL 5.5, including stored procedures, partitioned databases, triggers, and viewsImplement improvements in replication, high availability, and clusteringAchieve high performance when running MySQL in the cloudOptimize advanced querying features, such as full-text searchesTake advantage of modern multi-core CPUs and solid-state disksExplore backup and recovery strategies—including new tools for hot online backups
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business 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.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates
If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn Detail
Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.Style and approach
Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
Let's face it, SQL is a deceptively simple language to learn, and many database developers never go far beyond the simple statement: SELECT columns FROM table WHERE conditions. But there is so much more you can do with the language. In the SQL Cookbook, experienced SQL developer Anthony Molinaro shares his favorite SQL techniques and features. You'll learn about:
Window functions, arguably the most significant enhancement to SQL in the past decade. If you're not using these, you're missing out
Powerful, database-specific features such as SQL Server's PIVOT and UNPIVOT operators, Oracle's MODEL clause, and PostgreSQL's very useful GENERATE_SERIES function
Pivoting rows into columns, reverse-pivoting columns into rows, using pivoting to facilitate inter-row calculations, and double-pivoting a result set
Bucketization, and why you should never use that term in Brooklyn.
How to create histograms, summarize data into buckets, perform aggregations over a moving range of values, generate running-totals and subtotals, and other advanced, data warehousing techniques
The technique of walking a string, which allows you to use SQL to parse through the characters, words, or delimited elements of a string
Written in O'Reilly's popular Problem/Solution/Discussion style, the SQL Cookbook is sure to please. Anthony's credo is: "When it comes down to it, we all go to work, we all have bills to pay, and we all want to go home at a reasonable time and enjoy what's still available of our days." The SQL Cookbook moves quickly from problem to solution, saving you time each step of the way.
The important stuff you need to know:Dive into relational data. Solve problems quickly by connecting and combining data from different tables.Create professional documents. Publish reports, charts, invoices, catalogs, and other documents with ease.Access data anywhere. Use FileMaker Go on your iPad or iPhone—or share data on the Web.Harness processing power. Use new calculation and scripting tools to crunch numbers, search text, and automate tasks.Run your database on a secure server. Learn the high-level features of FileMaker Pro Advanced.Keep your data safe. Set privileges and allow data sharing with FileMaker’s streamlined security features.
Sams Teach Yourself SQL in 10 Minutes, Fourth Edition
New full-color code examples help you see how SQL statements are structured
Whether you're an application developer, database administrator, web application designer, mobile app developer, or Microsoft Office users, a good working knowledge of SQL is an important part of interacting with databases. And Sams Teach Yourself SQL in 10 Minutes offers the straightforward, practical answers you need to help you do your job.
Expert trainer and popular author Ben Forta teaches you just the parts of SQL you need to know–starting with simple data retrieval and quickly going on to more complex topics including the use of joins, subqueries, stored procedures, cursors, triggers, and table constraints.
You'll learn methodically, systematically, and simply–in 22 short, quick lessons that will each take only 10 minutes or less to complete.
With the Fourth Edition of this worldwide bestseller, the book has been thoroughly updated, expanded, and improved. Lessons now cover the latest versions of IBM DB2, Microsoft Access, Microsoft SQL Server, MySQL, Oracle, PostgreSQL, SQLite, MariaDB, and Apache Open Office Base. And new full-color SQL code listings help the beginner clearly see the elements and structure of the language.
10 minutes is all you need to learn how to...
Use the major SQL statements Construct complex SQL statements using multiple clauses and operators Retrieve, sort, and format database contents Pinpoint the data you need using a variety of filtering techniques Use aggregate functions to summarize data Join two or more related tables Insert, update, and delete data Create and alter database tables Work with views, stored procedures, and more Table of Contents
1 Understanding SQL
2 Retrieving Data
4 Filtering Data
5 Advanced Data Filtering
6 Using Wildcard Filtering
7 Creating Calculated Fields
8 Using Data Manipulation Functions
9 Summarizing Data
10 Grouping Data
11 Working with Subqueries
12 Joining Tables
13 Creating Advanced Joins
14 Combining Queries
15 Inserting Data
16 Updating and Deleting Data
17 Creating and Manipulating Tables
18 Using Views
19 Working with Stored Procedures
20 Managing Transaction Processing
21 Using Cursors
22 Understanding Advanced SQL Features
Appendix A: Sample Table Scripts
Appendix B: Working in Popular Applications
Appendix C : SQL Statement Syntax
Appendix E: SQL Reserved Words
The world of data warehousing has changed remarkably since the first edition of The Data Warehouse Lifecycle Toolkit was published in 1998. In that time, the data warehouse industry has reached full maturity and acceptance, hardware and software have made staggering advances, and the techniques promoted in the premiere edition of this book have been adopted by nearly all data warehouse vendors and practitioners. In addition, the term "business intelligence" emerged to reflect the mission of the data warehouse: wrangling the data out of source systems, cleaning it, and delivering it to add value to the business.
Ralph Kimball and his colleagues have refined the original set of Lifecycle methods and techniques based on their consulting and training experience. The authors understand first-hand that a data warehousing/business intelligence (DW/BI) system needs to change as fast as its surrounding organization evolves. To that end, they walk you through the detailed steps of designing, developing, and deploying a DW/BI system. You'll learn to create adaptable systems that deliver data and analyses to business users so they can make better business decisions.
As the most significant update since 2008, Microsoft SQL Server 2012 boasts updates and new features that are critical to understand. Whether you manage and administer SQL Server 2012 or are planning to get your MCSA: SQL Server 2012 certification, this book is the perfect supplement to your learning and preparation. From understanding SQL Server's roles to implementing business intelligence and reporting, this practical book explores tasks and scenarios that a working SQL Server DBA faces regularly and shows you step by step how to handle them.Includes practice exams and coverage of exam objectives for those seeking MSCA: SQL Server 2012 certification Explores the skills you'll need on the job as a SQL Server 2012 DBA Discusses designing and implementing database solutions Walks you through administrating, maintaining, and securing SQL Server 2012 Addresses implementing high availability and data distribution Includes bonus videos where the author walks you through some of the more difficult tasks expected of a DBA
Featuring hands-on exercises and real-world scenarios, this resource guides you through the essentials of implementing, maintaining, and repairing SQL Server 2012 databases.
Updated for the latest versions of this popular database, this edition covers many complex features that have been added to MySQL 5.0 and 5.1, including a section dedicated to stored procedures and triggers. After a brief introduction on installation and initial setup, the book explains: How to configure MySQL, such as setting the root passwordMySQL data types, including numerics, strings, dates, and complex typesSQL syntax, commands, data types, operators, and functionsArithmetic, comparison and logical operatorsAggregate and general functionsStored procedures and triggers, including procedure definition, procedure calls, procedure management, cursors, and triggersYou don't have time to stop and thumb through an exhaustive reference when you're hard at work. This portable and affordable guide is small enough to fit into your pocket, and gives you a convenient reference that you can consult anywhere. When you reach a sticking point and need to get to a solution quickly, the MySQL Pocket Reference is the book you want to have.
Updated for R 2.14 and 2.15, this second edition includes new and expanded chapters on R performance, the ggplot2 data visualization package, and parallel R computing with Hadoop.Get started quickly with an R tutorial and hundreds of examplesExplore R syntax, objects, and other language detailsFind thousands of user-contributed R packages online, including BioconductorLearn how to use R to prepare data for analysisVisualize your data with R’s graphics, lattice, and ggplot2 packagesUse R to calculate statistical fests, fit models, and compute probability distributionsSpeed up intensive computations by writing parallel R programs for HadoopGet a complete desktop reference to R
Google BigQuery Analytics is the perfect guide for business and data analysts who want the latest tips on running complex queries and writing code to communicate with the BigQuery API. The book uses real-world examples to demonstrate current best practices and techniques, and also explains and demonstrates streaming ingestion, transformation via Hadoop in Google Compute engine, AppEngine datastore integration, and using GViz with Tableau to generate charts of query results. In addition to the mechanics of BigQuery, the book also covers the architecture of the underlying Dremel query engine, providing a thorough understanding that leads to better query results.Features a companion website that includes all code and data sets from the book Uses real-world examples to explain everything analysts need to know to effectively use BigQuery Includes web application examples coded in Python
You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find these patterns useful for working on your own data applications.
Patterns include:Recommending music and the Audioscrobbler data setPredicting forest cover with decision treesAnomaly detection in network traffic with K-means clusteringUnderstanding Wikipedia with Latent Semantic AnalysisAnalyzing co-occurrence networks with GraphXGeospatial and temporal data analysis on the New York City Taxi Trips dataEstimating financial risk through Monte Carlo simulationAnalyzing genomics data and the BDG projectAnalyzing neuroimaging data with PySpark and Thunder
The potential user base and the wide array of available high-performance devices makes Android an attractive target for aspiring game developers. Do you have an awesome idea for the next break-through mobile gaming title? Beginning Android Games will help you kick-start your project. This book will guide you through the process of making several example game apps using APIs available in new Android SDK and earlier SDK releases for Android smartphones and tablets:The fundamentals of game development and design suitable for Android smartphones and tablets The Android platform basics to apply those fundamentals in the context of making a game, including new File Manager system and better battery life management The design of 2D and 3D games and their successful implementation on the Android platform
This book lets developers see and use some Android SDK Jelly Bean; however, this book is structured so that app developers can use earlier Android SDK releases. This book is backward compatible like the Android SDK.
The example code for this unique data science book is maintained in a public GitHub repository. It’s designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of IPython Notebooks.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
This updated second edition provides guidance for database developers, advanced configuration for system administrators, and an overview of the concepts and use cases for other people on your project. Ideal for NoSQL newcomers and experienced MongoDB users alike, this guide provides numerous real-world schema design examples.Get started with MongoDB core concepts and vocabularyPerform basic write operations at different levels of safety and speedCreate complex queries, with options for limiting, skipping, and sorting resultsDesign an application that works well with MongoDBAggregate data, including counting, finding distinct values, grouping documents, and using MapReduceGather and interpret statistics about your collections and databasesSet up replica sets and automatic failover in MongoDBUse sharding to scale horizontally, and learn how it impacts applicationsDelve into monitoring, security and authentication, backup/restore, and other administrative tasks
Rather than run through all possible scenarios, this pragmatic operations guide calls out what works, as demonstrated in critical deployments.Get a high-level overview of HDFS and MapReduce: why they exist and how they workPlan a Hadoop deployment, from hardware and OS selection to network requirementsLearn setup and configuration details with a list of critical propertiesManage resources by sharing a cluster across multiple groupsGet a runbook of the most common cluster maintenance tasksMonitor Hadoop clusters—and learn troubleshooting with the help of real-world war storiesUse basic tools and techniques to handle backup and catastrophic failure
Detailing the hows and the whys of successful Essbase implementation, the book arms you with simple yet powerful tools to meet your immediate needs, as well as the theoretical knowledge to proceed to the next level with Essbase. Infrastructure, data sourcing and transformation, database design, calculations, automation, APIs, reporting, and project implementation are covered by subject matter experts who work with the tools and techniques on a daily basis. In addition to practical cases that illustrate valuable lessons learned, the book offers:
Undocumented Secrets—Dan Pressman describes the previously unpublished and undocumented inner workings of the ASO Essbase engine. Authoritative Experts—If you have questions that no one else can solve, these 12 Essbase professionals are the ones who can answer them. Unpublished—Includes the only third-party guide to infrastructure. Infrastructure is easy to get wrong and can doom any Essbase project. Comprehensive—Let there never again be a question on how to create blocks or design BSO databases for performance—Dave Farnsworth provides the answers within. Innovative—Cameron Lackpour and Joe Aultman bring new and exciting solutions to persistent Essbase problems.
With a list of contributors as impressive as the program of presenters at a leading Essbase conference, this book offers unprecedented access to the insights and experiences of those at the forefront of the field. The previously unpublished material presented in these pages will give you the practical knowledge needed to use this powerful and intuitive tool to build highly useful analytical models, reporting systems, and forecasting applications.
"Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss:
How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes.
Important data warehouse technologies and practices.
Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture.Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouseDemystifies data vault modeling with beginning, intermediate, and advanced techniquesDiscusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0
By reading this definitive tutorial and reference, you'll gain the knowledge and experience to create stunning, cutting-edge Android apps that can make you money, while keeping you agile enough to respond to changes in the future.
Authors Ben Spivey and Joey Echeverria provide in-depth information about the security features available in Hadoop, and organize them according to common computer security concepts. You’ll also get real-world examples that demonstrate how you can apply these concepts to your use cases.Understand the challenges of securing distributed systems, particularly HadoopUse best practices for preparing Hadoop cluster hardware as securely as possibleGet an overview of the Kerberos network authentication protocolDelve into authorization and accounting principles as they apply to HadoopLearn how to use mechanisms to protect data in a Hadoop cluster, both in transit and at restIntegrate Hadoop data ingest into enterprise-wide security architectureEnsure that security architecture reaches all the way to end-user access
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power.
Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention.
Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.
• Learning the Bayesian “state of mind” and its practical implications
• Understanding how computers perform Bayesian inference
• Using the PyMC Python library to program Bayesian analyses
• Building and debugging models with PyMC
• Testing your model’s “goodness of fit”
• Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works
• Leveraging the power of the “Law of Large Numbers”
• Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning
• Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes
• Selecting appropriate priors and understanding how their influence changes with dataset size
• Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough
• Using Bayesian inference to improve A/B testing
• Solving data science problems when only small amounts of data are available
Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.
This book will help you:Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification
Corresponding data sets are available at www.wiley.com/go/9781118876138.
Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!
Using Hadoop 2 exclusively, author Tom White presents new chapters on YARN and several Hadoop-related projects such as Parquet, Flume, Crunch, and Spark. You’ll learn about recent changes to Hadoop, and explore new case studies on Hadoop’s role in healthcare systems and genomics data processing.Learn fundamental components such as MapReduce, HDFS, and YARNExplore MapReduce in depth, including steps for developing applications with itSet up and maintain a Hadoop cluster running HDFS and MapReduce on YARNLearn two data formats: Avro for data serialization and Parquet for nested dataUse data ingestion tools such as Flume (for streaming data) and Sqoop (for bulk data transfer)Understand how high-level data processing tools like Pig, Hive, Crunch, and Spark work with HadoopLearn the HBase distributed database and the ZooKeeper distributed configuration service
Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shellLeverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlibUse one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and StormLearn how to deploy interactive, batch, and streaming applicationsConnect to data sources including HDFS, Hive, JSON, and S3Master advanced topics like data partitioning and shared variables
This book offers practical answers to some of the hardest questions faced by PL/SQL developers, including:What is the best way to write the SQL logic in my application code?
How should I write my packages so they can be leveraged by my entire team of developers?
How can I make sure that all my team's programs handle and record errors consistently?Oracle PL/SQL Best Practices summarizes PL/SQL best practices in nine major categories: overall PL/SQL application development; programming standards; program testing, tracing, and debugging; variables and data structures; control logic; error handling; the use of SQL in PL/SQL; building procedures, functions, packages, and triggers; and overall program performance.
This book is a concise and entertaining guide that PL/SQL developers will turn to again and again as they seek out ways to write higher quality code and more successful applications.
"This book presents ideas that make the difference between a successful project and one that never gets off the ground. It goes beyond just listing a set of rules, and provides realistic scenarios that help the reader understand where the rules come from. This book should be required reading for any team of Oracle database professionals."
--Dwayne King, President, KRIDAN Consulting
Implementations, as well as interesting, real-world examples of each data structure and algorithm, are included.
Using both a programming style and a writing style that are exceptionally clean, Kyle Loudon shows you how to use such essential data structures as lists, stacks, queues, sets, trees, heaps, priority queues, and graphs. He explains how to use algorithms for sorting, searching, numerical analysis, data compression, data encryption, common graph problems, and computational geometry. And he describes the relative efficiency of all implementations. The compression and encryption chapters not only give you working code for reasonably efficient solutions, they offer explanations of concepts in an approachable manner for people who never have had the time or expertise to study them in depth.
Anyone with a basic understanding of the C language can use this book. In order to provide maintainable and extendible code, an extra level of abstraction (such as pointers to functions) is used in examples where appropriate. Understanding that these techniques may be unfamiliar to some programmers, Loudon explains them clearly in the introductory chapters.
Contents include:PointersRecursionAnalysis of algorithmsData structures (lists, stacks, queues, sets, hash tables, trees, heaps, priority queues, graphs)Sorting and searchingNumerical methodsData compressionData encryptionGraph algorithmsGeometric algorithms
Data is probably your company’s most important asset, so your data warehouse should serve your needs. The fully updated Second Edition of Data Warehousing For Dummies helps you understand, develop, implement, and use data warehouses, and offers a sneak peek into their future. You’ll learn to:Analyze top-down and bottom-up data warehouse designs Understand the structure and technologies of data warehouses, operational data stores, and data marts Choose your project team and apply best development practices to your data warehousing projects Implement a data warehouse, step by step, and involve end-users in the process Review and upgrade existing data storage to make it serve your needs Comprehend OLAP, column-wise databases, hardware assisted databases, and middleware Use data mining intelligently and find what you need Make informed choices about consultants and data warehousing products
Data Warehousing For Dummies, 2nd Edition also shows you how to involve users in the testing process and gain valuable feedback, what it takes to successfully manage a data warehouse project, and how to tell if your project is on track. You’ll find it’s the most useful source of data on the topic!
How is Office for iPad different from Office on your Mac or PC? Which features do the Office for iPad apps have, which do they lack, and how can you work around their limitations? How can you share your files among the different versions of Office? How quickly can you work without the physical keyboard and keyboard shortcuts you're used to -- and which keyboard shortcuts can you use if you connect a hardware keyboard to your iPad?
Whether you plan to write a few letters or your dissertation, run your home office away from home, or use your iPad for daily business, Pro Office for iPad will show you how to get the most out of Word, Excel, and other Office apps on your iPad. Enjoy your new freedom and still be as productive as ever with the skills and techniques you'll learn in Pro Office for iPad.
The Silicon Jungle is a cautionary fictional tale of data mining’s promise and peril. Baluja raises ethical questions about contemporary technological innovations, and how minute details can be routinely pieced together into rich profiles that reveal our habits, goals, and secret desires—all ready to be exploited.
Deploy and manage high-performance data transformation solutions across your enterprise using the step-by-step techniques in this fully revised guide. Hands-On Microsoft SQL Server 2008 Integration Services, Second Edition explains the tools and methods necessary to extract conclusive business intelligence from disparate corporate data. Learn how to build and secure packages, load and cleanse data, establish workflow, and optimize performance. Real-world examples, detailed illustrations, and hands-on exercises are included throughout this practical resource.Build SSIS packages using the Import and Export Wizard and Business Intelligence Development Studio Create Loop and Task Host Containers with Control Flow Designer Administer SSIS from SQL Server Management Studio and dutil Secure packages using encryption, roles, signatures, and permissions Build-in memory lookups and transformations using the Data Flow Engine Extend functionality using custom scripts and ActiveX components Use SCD transformations to load data warehouses and data marts Learn to build a Star Schema and Parallel Data Warehouse Migrate Data Transformation Services 2000 and SSIS 2005 packages
OS X and iOS Kernel Programming provides an incisive and complete introduction to the XNU kernel, which runs iPhones, iPads, iPods, and Mac OS X servers and clients. Then, you’ll expand your horizons to examine Mac OS X and iOS system architecture. Understanding Apple’s operating systems will allow you to write efficient device drivers, such as those covered in the book, using I/O Kit.
With OS X and iOS Kernel Programming, you’ll:Discover classical kernel architecture topics such as memory management and thread synchronization Become well-versed in the intricacies of the kernel development process by applying kernel debugging and profiling tools Learn how to deploy your kernel-level projects and how to successfully package them Write code that interacts with hardware devices Examine easy to understand example code that can also be used in your own projects Create network filters
Whether you’re a hobbyist, student, or professional engineer, turn to OS X andiOS Kernel Programming and find the knowledge you need to start developing
The Concept and Object Modeling Notation (COMN) is able to cover the full spectrum of analysis and design. A single COMN model can represent the objects and concepts in the problem space, logical data design, and concrete NoSQL and SQL document, key-value, columnar, and relational database implementations. COMN models enable an unprecedented level of traceability of requirements to implementation. COMN models can also represent the static structure of software and the predicates that represent the patterns of meaning in databases.
This book will teach you:the simple and familiar graphical notation of COMN with its three basic shapes and four line styles how to think about objects, concepts, types, and classes in the real world, using the ordinary meanings of English words that aren’t tangled with confused techno-speak how to express logical data designs that are freer from implementation considerations than is possible in any other notation how to understand key-value, document, columnar, and table-oriented database designs in logical and physical terms how to use COMN to specify physical database implementations in any NoSQL or SQL database with the precision necessary for model-driven development
Power View is Microsoft's ground-breaking tool for ad-hoc data visualization and analysis. It's designed to produce elegant and visually arresting output. It's also built to enhance user experience through polished interactivity. Power Map is a similarly powerful mechanism for analyzing data across geographic and political units. Power Query lets you load, shape and streamline data from multiple sources. PowerPivot can extend and develop data into a dynamic model. Power BI allows you to share your findings with colleagues, and present your insights to clients.
High Impact Data Visualization with Power View, Power Map, and Power BI helps you master this suite of powerful tools from Microsoft. You'll learn to identify data sources, and to save time by preparing your underlying data correctly. You'll also learn to deliver your powerful visualizations and analyses through the cloud to PCs, tablets and smartphones.Simple techniques take raw data and convert it into information.Slicing and dicing metrics delivers interactive insight.Visually arresting output grabs and focuses attention on key indicators.
Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps.Create analytics applications by using the agile big data development methodologyBuild value from your data in a series of agile sprints, using the data-value stackGain insight by using several data structures to extract multiple features from a single datasetVisualize data with charts, and expose different aspects through interactive reportsUse historical data to predict the future, and translate predictions into actionGet feedback from users after each sprint to keep your project on track
New to this edition:
The Backstage is introduced (a new Office 2013 feature); all the ‘external’ operations like Save, Print etc. are now in one placeThe chapter on charting is totally revised and updated – Excel 2013 differs greatly from earlier versionsIncludes many new end-of-chapter problems Most chapters have been edited to improve readability
This book supersedes ISBN 9780596550066, from O'Reilly.
This book is an in-depth guide to the use of pandas for data analysis, for either the seasoned data analysis practitioner or the novice user. It provides a basic introduction to the pandas framework, and takes users through the installation of the library and the IPython interactive environment. Thereafter, you will learn basic as well as advanced features, such as MultiIndexing, modifying data structures, and sampling data, which provide powerful capabilities for data analysis.
Android game app development continues to be one of the hottest areas where indies and existing game app developers seem to be most active. Android is the second best mobile apps eco and arguably even a hotter game apps eco than iOS. 3D makes your games come alive; so in this book you'll find that we go in depth on creating 3D games for the Android platform with OpenGL ES 2.0 using an original case study game called Drone Grid.
Moreover, this book offers an extensive case study with code that will be modular and re-useable helping you create your own games using advanced vertex and fragment shaders. Drone Grid is a game app case study that is somewhat similar to the best selling Geometry Wars game series utilizing a gravity grid and colorful abstract graphics and particles.
After reading and using this book, you'll be able to build your first 3D Android game app for smartphones and tablets. You may even be able to upload and sell from popular Android app stores like Google Play and Amazon Appstore.