Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics. At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches. Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid. Presents the wisdom of community experts, derived from a summit on software analytics Provides contributed chapters that share discrete ideas and technique from the trenches Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data Presented in clear chapters designed to be applicable across many domains

"Simulation for Data Science with R" introduces data professionals to fundamental and advanced simulation techniques using R. You'll understand essential statistical modeling concepts and learn to apply simulation methods to tackle data challenges and enhance your decision-making skills. What this Book will help me do Master five popular simulation methodologies including Monte Carlo and Agent-Based Modeling. Learn to simulate real-world data to uncover patterns and enhance predictions. Enhance your R programming expertise by exploring its advanced statistical features. Gain hands-on experience solving statistical problems through practical examples. Develop comprehensive statistical models aimed at real-world decision support. Author(s) Matthias Templ is a seasoned data science expert with extensive experience in statistical modeling and simulations using R. His work is rooted in real-world problem solving, outlining frameworks that are practical and research-driven. With a dedication to education, Matthias conveys his knowledge in an accessible and supportive manner. Who is it for? If you're experienced in computational methods and wish to refine your understanding of R for advanced statistical simulations, this book is for you. It's ideal for analysts or scientists aiming to enhance their decision-making with simulated data models. Prior experience with R is recommended to fully engage with the rigorous concepts presented.

Master the art of building analytical models using R About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Build and customize publication-quality visualizations of powerful and stunning R graphs Develop key skills and techniques with R to create and customize data mining algorithms Use R to optimize your trading strategy and build up your own risk management system Discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R Who This Book Is For This course is for data scientist or quantitative analyst who are looking at learning R and take advantage of its powerful analytical design framework. It's a seamless journey in becoming a full-stack R developer. What You Will Learn Describe and visualize the behavior of data and relationships between data Gain a thorough understanding of statistical reasoning and sampling Handle missing data gracefully using multiple imputation Create diverse types of bar charts using the default R functions Familiarize yourself with algorithms written in R for spatial data mining, text mining, and so on Understand relationships between market factors and their impact on your portfolio Harness the power of R to build machine learning algorithms with real-world data science applications Learn specialized machine learning techniques for text mining, big data, and more In Detail The R learning path created for you has five connected modules, which are a mini-course in their own right. As you complete each one, you'll have gained key skills and be ready for the material in the next module! This course begins by looking at the Data Analysis with R module. This will help you navigate the R environment. You'll gain a thorough understanding of statistical reasoning and sampling. Finally, you'll be able to put best practices into effect to make your job easier and facilitate reproducibility. The second place to explore is R Graphs, which will help you leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. You'll learn how to produce, customize, and publish advanced visualizations using this popular and powerful framework. With the third module, Learning Data Mining with R, you will learn how to manipulate data with R using code snippets and be introduced to mining frequent patterns, association, and correlations while working with R programs. The Mastering R for Quantitative Finance module pragmatically introduces both the quantitative finance concepts and their modeling in R, enabling you to build a tailor-made trading system on your own. By the end of the module, you will be well-versed with various financial techniques using R and will be able to place good bets while making financial decisions. Finally, we'll look at the Machine Learning with R module. With this module, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. You'll also learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, and so on. Style and approach Learn data analysis, data visualization techniques, data mining, and machine learning all using R and also learn to build models in quantitative finance using this powerful language.

Provides an introduction of the data industry to the field of economics This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises’ business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees. Defines and develops the concept of a “Data Industry,” and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics. CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudan’s Institute for Data Industry and proposed the concept of the “data industry”. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.

Unleash the power of Python and its robust data science capabilities About This Book Unleash the power of Python 3 objects Learn to use powerful Python libraries for effective data processing and analysis Harness the power of Python to analyze data and create insightful predictive models Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics Who This Book Is For Entry-level analysts who want to enter in the data science world will find this course very useful to get themselves acquainted with Python's data science capabilities for doing real-world data analysis. What You Will Learn Install and setup Python Implement objects in Python by creating classes and defining methods Get acquainted with NumPy to use it with arrays and array-oriented computing in data analysis Create effective visualizations for presenting your data using Matplotlib Process and analyze data using the time series capabilities of pandas Interact with different kind of database systems, such as file, disk format, Mongo, and Redis Apply data mining concepts to real-world problems Compute on big data, including real-time data from the Internet Explore how to use different machine learning models to ask different questions of your data In Detail The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you'll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it's time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls. Style and approach This course includes all the resources that will help you jump into the data science field with Python and learn how to make sense of data. The aim is to create a smooth learning path that will teach you how to get started with powerful Python libraries and perform various data science techniques in depth.

In 'Apache Spark Machine Learning Blueprints', you'll explore how to create sophisticated and scalable machine learning projects using Apache Spark. This project-driven guide covers practical applications including fraud detection, customer analysis, and recommendation engines, helping you leverage Spark's capabilities for advanced data science tasks. What this Book will help me do Learn to set up Apache Spark efficiently for machine learning projects, unlocking its powerful processing capabilities. Integrate Apache Spark with R for detailed analytical insights, empowering your decision-making processes. Create predictive models for use cases including customer scoring, fraud detection, and risk assessment with practical implementations. Understand and utilize Spark's parallel computing architecture for large-scale machine learning tasks. Develop and refine recommendation systems capable of handling large user bases and datasets using Spark. Author(s) Alex Liu is a seasoned data scientist and software developer specializing in machine learning and big data technology. With extensive experience in using Apache Spark for predictive analytics, Alex has successfully built and deployed scalable solutions across industries. Their teaching approach combines theory and practical insights, making cutting-edge technologies accessible and actionable. Who is it for? This book is ideal for data analysts, data scientists, and developers with a foundation in machine learning who are eager to apply their knowledge in big data contexts. If you have a basic familiarity with Apache Spark and its ecosystem, and you're looking to enhance your ability to build machine learning applications, this resource is for you. It's particularly valuable for those aiming to utilize Spark for extensive data operations and gain practical, project-based insights.

I'm joined this week by Jon Morra, director of data science at eHarmony to discuss a variety of ways in which machine learning and data science are being applied to help connect people for successful long term relationships. Interesting open source projects mentioned in the interview include Face-parts, a web service for detecting faces and extracting a robust set of fiducial markers (features) from the image, and Aloha, a Scala based machine learning library. You can learn more about these and other interesting projects at the eHarmony github page. In the wrap up, Jon mentioned the LA Machine Learning meetup which he runs. This is a great resource for LA residents separate and complementary to datascience.la groups, so consider signing up for all of the above and I hope to see you there in the future.

Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science. About the Technology Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering what you need to learn to begin a career as a data scientist can seem bewildering. This book is designed to help you get started. About the Book Introducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You'll explore data visualization, graph databases, the use of NoSQL, and the data science process. You'll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you'll have the solid foundation you need to start a career in data science. What's Inside Handling large data Introduction to machine learning Using Python to work with data Writing data science algorithms About the Reader This book assumes you're comfortable reading code in Python or a similar language, such as C, Ruby, or JavaScript. No prior experience with data science is required. About the Authors Davy Cielen, Arno D. B. Meysman, and Mohamed Ali are the founders and managing partners of Optimately and Maiton, where they focus on developing data science projects and solutions in various sectors. Quotes Read this book if you want to get a quick overview of data science, with lots of examples to get you started! - Alvin Raj, Oracle The map that will help you navigate the data science oceans. - Marius Butuc, Shopify Covers the processes involved in data science from end to end… A complete overview. - Heather Campbell, Kainos A must-read for anyone who wants to get into the data science world. - Hector Cuesta, Big Data Bootcamp

Practical Data Analysis Cookbook takes you on a comprehensive journey to mastering data exploration and analysis using Python. From data cleaning and transformation to building predictive and classification models, this book provides practical recipes for tackling real-world data challenges and extracting valuable insights. What this Book will help me do Efficiently clean, transform, and explore datasets using tools like pandas and OpenRefine. Develop predictive models for time series and other datasets using Python libraries such as scikit-learn and Statsmodels. Apply clustering and classification techniques to real-world data problems to gain actionable insights. Explore advanced topics like natural language processing and graph theory concepts using specialized tools. Build the skills to solve practical data modeling problems encountered in a data science role. Author(s) None Drabas is an experienced data scientist and author who specializes in Python-based data analysis. With a background in tackling intricate data-driven problems, None brings real-world experience to the readers. In creating this Cookbook, None adopts a step-by-step approach, making complex techniques accessible to learners of all backgrounds. Who is it for? If you are a data analyst, data scientist, or someone interested in exploring Python for practical data problems, this book is for you. It suits beginners starting their data journey and intermediate professionals looking to enhance their toolset. With clear instructions, it's ideal for anyone willing to build practical skills and tackle real-world challenges in data analysis.

Are you a data scientist? Have you pondered whether you're really a growth hacker? Well...get over yourself! Picking up on a debate that started onstage at eMetrics, Michael, Jim, and Tim discuss whether a fundamental shift in the role (and requisite skills) of the web analyst are changing. You know, getting more "science-y" (if "science" is "more technical and more maths"). all in 2,852 seconds (each second of which can be pulled into R and used to build a predictive model showing the expected ROI of listening to future episodes; at least, we assume that's what a data scientist could do).

This book, 'R Machine Learning by Example,' offers a hands-on approach to learning about machine learning using R. You will not only understand the theoretical aspects but also learn to apply machine learning algorithms to solve real-world problems. Through guided examples, you'll explore predictive modeling, data analysis, and other machine learning techniques implemented in R. What this Book will help me do Master the use of R for advanced data handling and exploration. Visualize multidimensional data effectively to derive insights. Understand and implement key machine learning algorithms in R. Solve practical, industry-relevant problems across multiple domains using R. Learn to optimize and fine-tune machine learning models for better results. Author(s) Raghav Bali, the author, is a seasoned data scientist with expertise in machine learning. With years of experience using R in data science, he has taught both professionals and enthusiasts how to use machine learning effectively. His approachable and clear writing style ensures that learners of various skill levels can benefit from his insights and guidance. Who is it for? This book is perfect for analysts, data scientists, or enthusiasts who want to leverage R for machine learning. It is suitable for beginners familiar with basic R concepts and intermediate learners looking to deepen their understanding of machine learning applications. If you are aiming to solve practical problems using data, this book will serve as a comprehensive guide.

Traditional engineering is built upon a world of knowledge and scientific laws, with components and systems that operate predictably. But what happens when a large number of these devices are interconnected? You get a complex system that’s no longer deterministic, but probabilistic. That’s happening today in many industries, including manufacturing, petroleum, transportation, and energy. In this O’Reilly report, Sean Patrick Murphy, Chief Data Scientist at PingThings, describes how data science is helping electric utilities make sense of a stochastic world filled with increasing uncertainty—including fundamental changes to the energy market and random phenomena such as weather and solar activity. Murphy also reviews several cutting-edge tools for storing and processing big data that he’s used in his work with electric utilities—tools that can help traditional engineers pursue a data-driven approach in many industries. Topics in this report include: Key drivers that have changed the electric grid from a deterministic machine into probabilistic system Fundamental differences that put traditional engineering and data science at odds with one another Why the time is right for engineering organizations to adopt a complete data-driven approach Contemporary tools that traditional engineers can use to store and process big data A PingThings case study for dealing with random geomagnetic disturbances to the energy grid

Digging for answers to your pressing business questions probably won’t resemble those tidy case studies that lead you step-by-step from data collection to cool insights. Data science is not so clear-cut in the real world. Instead of high-quality data with the right velocity, variety, and volume, many data scientists have to work with missing or sketchy information extracted from people in the organization. In this O’Reilly report, Jerry Overton—Distinguished Engineer at global IT leader DXC—introduces practices for making good decisions in a messy and complicated world. What he simply calls “data science that works” is a trial-and-error process of creating and testing hypotheses, gathering evidence, and drawing conclusions. These skills are far more useful for practicing data scientists than, say, mastering the details of a machine-learning algorithm. Adapted and expanded from a series of articles Overton published on O’Reilly Radar and on the CSC Blog, each chapter is ideal for current and aspiring data scientists who want to go pro, as well as IT execs and managers looking to hire in this field. The report covers: Using the scientific method to gain a competitive advantage The skill set you need to look for when choosing a data scientist Why practical induction is a key part of thinking like a data scientist Best practices for writing solid code in your data science gig How agile experimentation lets you find answers (or dead ends) much faster Advice for surviving (and even thriving) as a data scientist in your organization

Hadoop has revolutionized data processing and enterprise data warehousing, but its explosive growth has come with a large amount of uncertainty, hype, and confusion. With this report, enterprise decision makers will receive a concise crash course on what Hadoop is and why it’s important. Hadoop represents a major shift from traditional enterprise data warehousing and data analytics, and its technology can be daunting at first. Donald Miner, founder of the data science firm Miner & Kasch, covers just enough ground so you can make intelligent decisions about Hadoop in your enterprise. By the end of this report, you’ll know the basics of technologies such as HDFS, MapReduce, and YARN, without becoming mired in the details. Not only will you learn the basics of how Hadoop works and why it’s such an important technology, you’ll get examples of how you should probably be using it.

How well prepared is your organization to innovate, using data science? In this report, two leading data scientists at the consulting firm Booz Allen Hamilton describe ten characteristics of a mature data science capability. After spending years helping clients such as the US government and commercial organizations worldwide build innovative data science capabilities, Peter Guerra and Dr. Kirk Borne identified these characteristics to help you measure your company’s competence in this area. This report provides a detailed discussion of each of the 10 signs of data science maturity, which—among many other things—encourage you to: Give members of your organization access to all your available data Use Agile and leverage "DataOps"—DevOps for data product development Help your data science team sharpen its skills through open or internal competitions Personify data science as a way of doing things, and not a thing to do

Dive into the world of regression analysis guided by Python in this comprehensive book. From simple linear regression to complex models, you'll gain a deep understanding of how to analyze data and predict outcomes. By the end of this book, you will be equipped with the skills to tidy data, build models, and apply regression techniques to real-world problems. What this Book will help me do Understand and format datasets to prepare them for regression analysis efficiently. Build and implement various regression models, such as linear and logistic regression, to solve data science problems. Develop techniques to combat overfitting and ensure predictive accuracy. Learn to scale and adapt regression models to large datasets and apply incremental learning. Apply the skills gained to make informed business decisions using predictive insights from regression models. Author(s) Luca Massaron and Alberto Boschetti are seasoned data professionals with years of expertise in data science, regression analysis, and Python programming. They are passionate about teaching and have crafted this book to demystify regression for learners interested in predictive analytics. Their approachable style ensures concepts are accessible yet comprehensive. Who is it for? This book is ideal for Python developers and data scientists who have a foundational knowledge of math and statistics. Whether you're looking to delve deeper into predictive modeling or efficiently analyze datasets, this book provides step-by-step guidance. If you've dabbled in data science and wish to expand your skillset to include regression analysis, this book is for you!

Now in its fifth year, O’Reilly’s annual Big Data Now report recaps the trends, tools, applications, and forecasts we’ve talked about over the past year. For 2015, we’ve included a collection of blog posts, authored by leading thinkers and experts in the field, that reflect a unique set of themes we’ve identified as gaining significant attention and traction. Our list of 2015 topics include: Data-driven cultures Data science Data pipelines Big data architecture and infrastructure The Internet of Things and real time Applications of big data Security, ethics, and governance Is your organization on the right track? Get a hold of this free report now and stay in tune with the latest significant developments in big data.

Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.

Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions.

A practical guide to data mining using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to leverage the two most popular tools for data query and analysis—SQL and Excel—to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases. You'll learn the fundamental techniques before moving into the "where" and "why" of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples include SQL and Excel code, and the appendix shows how non-standard constructs are implemented in other major databases, including Oracle and IBM DB2/UDB. The companion website includes datasets and Excel spreadsheets, and the book provides hints, warnings, and technical asides to help you every step of the way. Data Analysis Using SQL and Excel, 2nd Edition shows you how to perform a wide range of sophisticated analyses using these simple tools, sparing you the significant expense of proprietary data mining tools like SAS. Understand core analytic techniques that work with SQL and Excel Ensure your analytic approach gets you the results you need Design and perform your analysis using SQL and Excel Data Analysis Using SQL and Excel, 2nd Edition shows you how to best use the tools you already know to achieve expert results.