Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. 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 methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track
Building Full-Stack Data Analytics Applications with Spark
Author: Russell Jurney
Publisher: "O'Reilly Media, Inc."
Data science teams looking to turn research into useful analytics applications require not only the right tools, but also the right approach if they’re to succeed. With the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools. Author Russell Jurney demonstrates how to compose a data platform for building, deploying, and refining analytics applications with Apache Kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. You’ll learn an iterative approach that lets you quickly change the kind of analysis you’re doing, depending on what the data is telling you. Publish data science work as a web application, and affect meaningful change in your organization. Build value from your data in a series of agile sprints, using the data-value pyramid Extract features for statistical models from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future via classification and regression Translate predictions into actions Get feedback from users after each sprint to keep your project on track
XP 2019 Workshops, Montréal, QC, Canada, May 21-25, 2019, Proceedings
Author: Rashina Hoda
Publisher: Springer Nature
Category: Agile software development
This open access book constitutes the research workshops, doctoral symposium and panel summaries presented at the 20th International Conference on Agile Software Development, XP 2019, held in Montreal, QC, Canada, in May 2019. XP is the premier agile software development conference combining research and practice. It is a hybrid forum where agile researchers, academics, practitioners, thought leaders, coaches, and trainers get together to present and discuss their most recent innovations, research results, experiences, concerns, challenges, and trends. Following this history, for both researchers and seasoned practitioners XP 2019 provided an informal environment to network, share, and discover trends in Agile for the next 20 years. Research papers and talks submissions were invited for the three XP 2019 research workshops, namely, agile transformation, autonomous teams, and large scale agile. This book includes 15 related papers. In addition, a summary for each of the four panels at XP 2019 is included. The panels were on security and privacy; the impact of the agile manifesto on culture, education, and software practices; business agility - agile's next frontier; and Agile - the next 20 years.
Gain a practical introduction to DataOps, a new discipline for delivering data science at scale used at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action or useful data products. This book provides the approach and methods to ensure continuous rapid use of data to create analytical applications, steer decision making, and derive learning in order to satisfy the customer. Practical DataOps shows you how to optimize the data supply chain from raw data to final analytical application, whether the goal is a machine learning model or other data-orientated output. Adapting and borrowing principles from Agile and DevOps methods, the book provides an approach to align data science with the rest of the organization and rapidly increase the number of reproducible analytics projects through automation and integration. This book helps you to improve the speed and accuracy at which analytical applications can be developed through data management practices that securely expand data access, and through lean thinking that improves quality via rigorous focus on testing, measurement, and eliminating wasted effort. The book shows how to collect feedback and monitor performance to manage and continuously improve your data products based on how they are used. What You'll Learn Develop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data science at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI and machine learning Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals facing barriers in getting data science and machine learning into customer-facing production, who are looking for an approach to rapidly deliver and scale deployments, and to learn from outcomes.
Advances in technology have resulted in new and advanced methods to support decision-making. For example, artificial intelligence has enabled people to make better decisions hrough the use of Intelligent Decision Support Systems (DSS). Emerging research in DSS demonstrates that decision makers can operate in a more timely manner using real-time data, more accurately due to data mining and 'big data' methods, more strategically by considering a greater number of factors, more precisely and inclusively due to the availability of social networking data, and with a wider media reach with video and audio technology._x000D_ _x000D_This book presents the proceedings of the IFIP TC8/Working Group 8.3 conference held at the Université Pierre et Marie Curie in Paris, France, in June 2014. Throughout its history the conference has aimed to present the latest innovations and achievements in Decision Support Systems. This year the conference looks to the next generation with the theme of new technologies to enable DSS2.0. The topics covered include theoretical, empirical and design science research; case-based approaches in decision support systems; decision models in the real-world; healthcare information technology; decision making theory; knowledge management; knowledge and resource discovery; business intelligence; group decision support systems; collaborative decision making; analytics and ‘big data’; rich language for decision support; multimedia tools for DSS; Web 2.0 systems in decision support; context-based technologies for decision making; intelligent systems and technologies in decision support; organizational decision support; research methods in DSS 2.0; mobile DSS; competing on analytics; and social media analytics._x000D_ _x000D_ The book will be of interest to all those who develop or use Decision Support Systems. The variety of methods and applications illustrated by this international group of carefully reviewed papers should provide ideas and directions for future researchers and practitioners alike.
Selected papers of the 20th AGILE conference on Geographic Information Science
Author: Arnold Bregt
This book contains the full research papers presented at the 20th AGILE Conference on Geographic Information Science, held in 2017 at Wageningen University & Research in Wageningen, the Netherlands. The selected contributions show trends in the domain of geographic information science directed to spatio-temporal perception and spatio-temporal analysis. For that reason the book is also of interest to professionals and researchers in fields outside geographic information science, in which the application of geoinformation could be instrumental in sparking societal innovation.
Selected papers of the 19th AGILE Conference on Geographic Information Science
Author: Tapani Sarjakoski
This book collects innovative research presented at the 19th Conference of the Association of Geographic Information Laboratories in Europe (AGILE) on Geographic Information Science, held in Helsinki, Finland in 2016.
A Guide to Empowering Successful Data Reporting and Analytics
Author: Laura Madsen
Publisher: John Wiley & Sons
Category: Business & Economics
Solid business intelligence guidance uniquely designed for healthcare organizations Increasing regulatory pressures on healthcare organizations have created a national conversation on data, reporting and analytics in healthcare. Behind the scenes, business intelligence (BI) and data warehousing (DW) capabilities are key drivers that empower these functions. Healthcare Business Intelligence is designed as a guidebook for healthcare organizations dipping their toes into the areas of business intelligence and data warehousing. This volume is essential in how a BI capability can ease the increasing regulatory reporting pressures on all healthcare organizations. Explores the five tenets of healthcare business intelligence Offers tips for creating a BI team Identifies what healthcare organizations should focus on first Shows you how to gain support for your BI program Provides tools and techniques that will jump start your BI Program Explains how to market and maintain your BI Program The risk associated with doing BI/DW wrong is high, and failures are well documented. Healthcare Business Intelligence helps you get it right, with expert guidance on getting your BI program started and successfully keep it going.
Geographic Information Science as an Enabler of Smarter Cities and Communities
Author: Fernando Bacao
This is a book is a collection of articles that will be submitted as full papers to the AGILE annual international conference. These papers go through a rigorous review process and report original and unpublished fundamental scientific research. Those published cover significant research in the domain of geographic information science systems. This year the focus is on geographic information science as an enabler of smarter cities and communities, thus we expect contributions that help visualize the role and contribution of GI science in their development.