Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that?s so clouded in hype? This insightful book, based on Columbia University?s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you?re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: * Statistical inference, exploratory data analysis, and the data science process * Algorithms * Spam filters, Naive Bayes, and data wrangling * Logistic regression * Financial modeling * Recommendation engines and causality * Data visualization * Social networks and data journalism * Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O?Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun.
Data Science steht derzeit wie kein anderer Begriff für die Auswertung großer Datenmengen mit analytischen Konzepten des Machine Learning oder der künstlichen Intelligenz. Nach der bewussten Wahrnehmung der Big Data und dabei insbesondere der Verfügbarmachung in Unternehmen sind Technologien und Methoden zur Auswertung dort gefordert, wo klassische Business Intelligence an ihre Grenzen stößt. Dieses Buch bietet eine umfassende Einführung in Data Science und deren praktische Relevanz für Unternehmen. Dabei wird auch die Integration von Data Science in ein bereits bestehendes Business-Intelligence-Ökosystem thematisiert. In verschiedenen Beiträgen werden sowohl Aufgabenfelder und Methoden als auch Rollen- und Organisationsmodelle erläutert, die im Zusammenspiel mit Konzepten und Architekturen auf Data Science wirken. Neben den Grundlagen werden unter anderem folgende Themen behandelt: - Data Science und künstliche Intelligenz - Konzeption und Entwicklung von Data-driven Products - Deep Learning - Self-Service im Data-Science-Umfeld - Data Privacy und Fragen zur digitalen Ethik - Customer Churn mit Keras/TensorFlow und H2O - Wirtschaftlichkeitsbetrachtung bei der Auswahl und Entwicklung von Data Science - Predictive Maintenance - Scrum in Data-Science-Projekten Zahlreiche Anwendungsfälle und Praxisbeispiele geben Einblicke in die aktuellen Erfahrungen bei Data-Science-Projekten und erlauben dem Leser einen direkten Transfer in die tägliche Arbeit.
Discover how data science can help you gain in-depth insight into your business - the easy way! Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, reading this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here´s what to expect: Provides a background in big data and data engineering before moving on to data science and how it´s applied to generate value Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate It´s a big, big data world out there--let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.
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. 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 advantage * Treat data as a business asset that requires careful investment if you?re to gain real value * Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way * Learn general concepts for actually extracting knowledge from data * Apply data science principles when interviewing data science job candidates
Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a ´´big data´´ problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you´ll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis. You´ll learn how to: - Analyze malware using static analysis - Observe malware behavior using dynamic analysis - Identify adversary groups through shared code analysis - Catch 0-day vulnerabilities by building your own machine learning detector - Measure malware detector accuracy - Identify malware campaigns, trends, and relationships through data visualization Whether you´re a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve.
This invaluable addition to any data scientist´s library shows you how to apply the R programming language and useful statistical techniques to everyday business situations as well as how to effectively present results to audiences of all levels. To answer the ever-increasing demand for machine learning and analysis, this new edition boasts additional R tools, modeling techniques, and more. Practical Data Science with R, Second Edition takes a practice oriented approach to explaining basic principles in the ever-expanding field of data science. You´ll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Key features - Data science and statistical analysis for the business professional - Numerous instantly familiar real-world use cases - Keys to effective data presentations - Modeling and analysis techniques like boosting, regularized regression, and quadratic discriminant analysis Audience While some familiarity with basic statistics and R is assumed, this book is accessible to readers with or without a background in data science. About the technology 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
Linear algebra relies heavily on coordinates, however, which can make many geometric programming tasks very specific and complex-often a lot of effort is required to bring about even modest performance enhancements. This title presents a compelling alternative to the limitations of linear algebra.