Practical Data Design
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| Author | : Rex Hogan |
| Publisher | : CRC Press |
| Total Pages | : 463 |
| Release | : 2018-03-08 |
| Genre | : Computers |
| ISBN | : 1351265466 |
Fully updated and expanded from the previous edition, A Practical Guide to Database Design, Second Edition is intended for those involved in the design or development of a database system or application. It begins by illustrating how to develop a Third Normal Form data model where data is placed “where it belongs”. The reader is taken step-by-step through the Normalization process, first using a simple then a more complex set of data requirements. Next, usage analysis for each Logical Data Model is reviewed and a Physical Data Model is produced that will satisfy user performance requirements. Finally, each Physical Data Model is used as input to create databases using both Microsoft Access and SQL Server. The book next shows how to use an industry-leading data modeling tool to define and manage logical and physical data models, and how to create Data Definition Language statements to create or update a database running in SQL Server, Oracle, or other type of DBMS. One chapter is devoted to illustrating how Microsoft Access can be used to create user interfaces to review and update underlying tables in that database as well as tables residing in SQL Server or Oracle. For users involved with Cyber activity or support, one chapter illustrates how to extract records of interest from a log file using PERL, then shows how to load these extracted records into one or more SQL Server “tracking” tables adding status flags for analysts to use when reviewing activity of interest. These status flags are used to flag/mark collected records as “Reviewed”, “Pending” (currently being analyzed) and “Resolved”. The last chapter then shows how to build a web-based GUI using PHP to query these tracking tables and allow an analyst to review new activity, flag items that need to be investigated, and finally flag items that have been investigated and resolved. Note that the book has complete code/scripts for both PERL and the PHP GUI.
| Author | : Peter Bruce |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 322 |
| Release | : 2017-05-10 |
| Genre | : Computers |
| ISBN | : 1491952911 |
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data
| Author | : Ofer Mendelevitch |
| Publisher | : Addison-Wesley Professional |
| Total Pages | : 463 |
| Release | : 2016-12-08 |
| Genre | : Computers |
| ISBN | : 0134029720 |
The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials. The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP). This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. Learn What data science is, how it has evolved, and how to plan a data science career How data volume, variety, and velocity shape data science use cases Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark Data importation with Hive and Spark Data quality, preprocessing, preparation, and modeling Visualization: surfacing insights from huge data sets Machine learning: classification, regression, clustering, and anomaly detection Algorithms and Hadoop tools for predictive modeling Cluster analysis and similarity functions Large-scale anomaly detection NLP: applying data science to human language
| Author | : Nina Zumel |
| Publisher | : Manning Publications |
| Total Pages | : 416 |
| Release | : 2014-04-10 |
| Genre | : Computers |
| ISBN | : 9781617291562 |
Summary Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics. Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels. This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed. What's Inside Data science for the business professional Statistical analysis using the R language Project lifecycle, from planning to delivery Numerous instantly familiar use cases Keys to effective data presentations About the Authors Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com. Table of Contents PART 1 INTRODUCTION TO DATA SCIENCE The data science process Loading data into R Exploring data Managing data PART 2 MODELING METHODS Choosing and evaluating models Memorization methods Linear and logistic regression Unsupervised methods Exploring advanced methods PART 3 DELIVERING RESULTS Documentation and deployment Producing effective presentations
| Author | : Hector Cuesta |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 330 |
| Release | : 2016-09-30 |
| Genre | : Computers |
| ISBN | : 1785286668 |
A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.
| Author | : Brian S. Yandell |
| Publisher | : Routledge |
| Total Pages | : 452 |
| Release | : 2017-11-22 |
| Genre | : Mathematics |
| ISBN | : 1351422995 |
Placing data in the context of the scientific discovery of knowledge through experimentation, Practical Data Analysis for Designed Experiments examines issues of comparing groups and sorting out factor effects and the consequences of imbalance and nesting, then works through more practical applications of the theory. Written in a modern and accessible manner, this book is a useful blend of theory and methods. Exercises included in the text are based on real experiments and real data.
| Author | : Prabhanjan Tattar |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 428 |
| Release | : 2017-06-29 |
| Genre | : Computers |
| ISBN | : 178712326X |
Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization
| Author | : Martin Kleppmann |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 658 |
| Release | : 2017-03-16 |
| Genre | : Computers |
| ISBN | : 1491903104 |
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? 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. 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
| Author | : Marc Delisle |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 132 |
| Release | : 2006-01-01 |
| Genre | : Computers |
| ISBN | : 1847190200 |
A short guide for everyone on how to structure your data and set-up your MySQL database tables efficiently and easily.
| Author | : Sam Ladner |
| Publisher | : Routledge |
| Total Pages | : 167 |
| Release | : 2016-08-25 |
| Genre | : Social Science |
| ISBN | : 1315422239 |
Ethnography is an increasingly important research method in the private sector, yet ethnographic literature continues to focus on an academic audience. Sam Ladner fills the gap by advancing rigorous ethnographic practice that is tailored to corporate settings where colleagues are not steeped in social theory, research time lines may be days rather than months or years, and research sponsors expect actionable outcomes and recommendations. Ladner provides step-by-step guidance at every turn--covering core methods, research design, using the latest mobile and digital technologies, project and client management, ethics, reporting, and translating your findings into business strategies. This book is the perfect resource for private-sector researchers, designers, and managers seeking robust ethnographic tools or academic researchers hoping to conduct research in corporate settings. More information on the book is available at http://www.practicalethnography.com/.
