Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. About the Technology Data collected from customers, scientific measurements, IoT sensors, and so on is valuable only if you understand it. Data scientists revel in the interesting and rewarding challenge of observing, exploring, analyzing, and interpreting this data. Getting started with data science means more than mastering analytic tools and techniques, however; the real magic happens when you begin to think like a data scientist. This book will get you there. About the Book Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you'll put this knowledge together using a structured process for data science. When you've finished, you'll have a strong foundation for a lifetime of data science learning and practice. What's Inside The data science process, step-by-step How to anticipate problems Dealing with uncertainty Best practices in software and scientific thinking About the Reader Readers need beginner programming skills and knowledge of basic statistics. About the Author Brian Godsey has worked in software, academia, finance, and defense and has launched several data-centric start-ups. Quotes Explains difficult concepts and techniques concisely and approachably. - Jenice Tom, CVS Health Goes beyond simple tools and techniques and helps you to conceptualize and solve challenging, real-world data science problems. - Casimir Saternos, Synchronoss Technologies A successful attempt to put the mind of a data scientist on paper. - David Krief, Altansia The book that changed my career path! - Nicolas Boulet-Lavoie, DL Innov

Geospatial data, or data with location information, is generated in huge volumes every day by billions of mobile phones, IoT sensors, drones, nanosatellites, and many other sources in an unending stream. This practical ebook introduces you to the landscape of tools and methods for making sense of all that data, and shows you how to apply geospatial analytics to a variety of issues, large and small. Authors Aurelia Moser, Jon Bruner, and Bill Day provide a complete picture of the geospatial analysis options available, including low-scale commercial desktop GIS tools, medium-scale options such as PostGIS and Lucene-based searching, and true big data solutions built on technologies such as Hadoop. You’ll learn when it makes sense to move from one type of solution to the next, taking increased costs and complexity into account. Explore the structure of basic webmaps, and the challenges and constraints involved when working with geo data Dive into low- to medium-scale mapping tools for use in backend and frontend web development Focus on tools for robust medium-scale geospatial projects that don’t quite justify a big data solution Learn about innovative platforms and software packages for solving issues of processing and storage of large-scale data Examine geodata analysis use cases, including disaster relief, urban planning, and agriculture and environmental monitoring

An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.

Why have stream-oriented data systems become so popular, when batch-oriented systems have served big data needs for many years? In this report, author Dean Wampler examines the rise of streaming systems for handling time-sensitive problems—such as detecting fraudulent financial activity as it happens. You’ll explore the characteristics of fast data architectures, along with several open source tools for implementing them. Batch-mode processing isn’t going away, but exclusive use of these systems is now a competitive disadvantage. You’ll learn that, while fast data architectures are much harder to build, they represent the state of the art for dealing with mountains of data that require immediate attention. Learn step-by-step how a basic fast data architecture works Understand why event logs are the core abstraction for streaming architectures, while message queues are the core integration tool Use methods for analyzing infinite data sets, where you don’t have all the data and never will Take a tour of open source streaming engines, and discover which ones work best for different use cases Get recommendations for making real-world streaming systems responsive, resilient, elastic, and message driven Explore an example streaming application for the IoT: telemetry ingestion and anomaly detection for home automation systems

In this session, Beena Ammanath, Data Science Products at General Electric, sat with Vishal Kumar, CEO AnalyticsWeek and shared her journey as an analytics executive, life @ GE, future of analytics in the industrial sector, how Predix is helping other industrial companies cope up with growing data, and some challenges/Opportunities she's observing as an analytics executive.

Timeline: 0:29 Beena's journey. 5:19 Data science in the manufacturing sector. 7:03 Driving data science in the manufacturing sector. 9:39 Bringing in the data culture into the manufacturing sector. 11:35 Upskilling and being relevant as a data scientist. 13:27 Hacks to managing data teams well. 16:08 What's Predix? 19:06 Investment opportunities for data science in manufacturing. 21:07 Challenges manufacturing businesses in data. 24:46 IoT and manufacturing. 25:18 Dealing with IoT vendors at Predix. 26:24 Ontology of data at Predix. 29:43 Dealing with the new rules and laws in the IoT sector. 31:30 Interesting use cases in the manufacturing industry. 34:37 Open source vs. enterprise. 35:35 Getting recruited as a data scientist in manufacturing. 40:07 Pitching your product for a manufacturing company.

Podcast link: https://futureofdata.org/leadership-playbook-with-beena-ammanath-ge/

Here's Beena's Bio: Beena Ammanath is Board Director at ChickTech and Head of Data Science Products at General Electric. She is a seasoned technology leader with over 24 years of a proven track record of building, and leading high-performance teams from the ground-up focused on strategy and successful execution of industrial scale technology products and services. She has an impressive track record, having worked at recognized international organizations British Telecom, E*trade, Thomson Reuters, Bank of America, and Silicon Valley startups in engineering and management positions.

She is also helping build the next-gen of computer scientists through her role on the Industry Advisory Board for Cal Poly. She holds a Masters in Computer Science and an MBA in Finance. She has been a featured speaker on the topics of data science, big data, technology transformation, and women in leadership at numerous industry conferences.

Throughout her career in technology, Beena has been a strong advocate for women in positions of technology leadership and has established herself as a voice for resolving gender disparities.

Follow @beena_ammanath

The podcast is sponsored by: TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Want to Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

In this session, David Rose, CEO, Ditto Labs, sat with Vishal Kumar, CEO AnalyticsWeek and shared his journey as a data driven executive, best practices, shared some thought leadership in visualizations and usability. Some challenges/opportunities he's observing as an analytics-driven startup.

Timeline: 0:29 David's journey. 4:50 Bringing technology to everyday objects. 9:37 Sensor and photosensor. 13:02 Choosing the right use cases. 16:54 On deep learning. 21:49 Working on new use cases in image processing. 26:05 Ditto Labs's allure classifiers. 28:15 Challenges as an entrepreneur in an image processing company. 32:50 Technical challenges for Ditto faces. 36:58 Privacy and IoT. 40:17 Different countries, different legal norms on privacy. 42:55 Data culture and image processing company. 44:46 Opportunities in the image processing stacks.

Podcast Link: https://futureofdata.org/analyticsweek-leadership-podcast-with-david-rose-ditto-labs/

If interested in vision catalog (as discussed in the video): http://www.slideshare.net/davidloring...

David's website: enchantedobjects.com

Here's David's Bio: David is the CEO at Ditto Labs, an image-recognition software platform that scours social media photos to find brands and products.

His new book, Enchanted Objects, focuses on the future of the internet of things and how these technologies will impact how we live and work.

Prior to Ditto, David founded and was CEO at Vitality, a company that reinvented medication packaging now distributed by CVS, Walgreens, and Express Scripts.

He founded Ambient Devices, which pioneered glanceable technology: embedding internet information in everyday objects like lamps, mirrors, and umbrellas.

David holds patents for photo sharing, interactive TV, ambient information displays, and medical devices. His work has been featured at the MoMA, covered in the New York Times, WIRED, and The Economist, and parodied on the Colbert Report.

David co-teaches a popular course in tangible user interfaces at the MIT Media Lab with Hiroshi Ishii. He is a frequent speaker to corporations and design and technology conferences.

He received his BA in Physics from St. Olaf College, studied Interactive Cinema at the MIT Media Lab, and earned a Masters at Harvard.

Follow @davidrose

The podcast is sponsored by: TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers, and lead practitioners to discuss their journey to create the data-driven future.

Want to Join? If you or any you know wants to join in, Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. This book walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach, each chapter introduces use cases from a specific industry and uses publicly available datasets from that domain to unravel the intricacies of production-grade design and implementation. The domains covered in Pro Spark Streaming include social media, the sharing economy, finance, online advertising, telecommunication, and IoT. In the last few years, Spark has become synonymous with big data processing. DStreams enhance the underlying Spark processing engine to support streaming analysis with a novel micro-batch processing model. Pro Spark Streaming by Zubair Nabi will enable you to become a specialist of latency sensitive applications by leveraging the key features of DStreams, micro-batch processing, and functional programming. To this end, the book includes ready-to-deploy examples and actual code. Pro Spark Streaming will act as the bible of Spark Streaming. What You'll Learn Discover Spark Streaming application development and best practices Work with the low-level details of discretized streams Optimize production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios Ingest data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver Integrate and couple with HBase, Cassandra, and Redis Take advantage of design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model Implement real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR Use streaming machine learning, predictive analytics, and recommendations Mesh batch processing with stream processing via the Lambda architecture Who This Book Is For Data scientists, big data experts, BI analysts, and data architects.

Using log data to create value isn’t new to mechanized industries. But in today’s data-driven environment—particularly with the rise of the Internet of Things—this type of data exhaust can be converted from inactive, latent assets to critical-path components of an overall production ecosystem. In this report, Cameron Turner provides three real-world case studies in which his company, The Data Guild, served as a product co-development consultancy. You’ll learn how an energy efficiency firm, a tech company, and a healthcare organization combined their historical logs with newly generated sensor data from the IoT. By leveraging machine learning to proactively identify efficiency and opportunity through prediction and recommendation, each company was able to deploy an ROI-generating solution and gain a significant business advantage. This report also provides advice for successfully implementing IoT data, as well as key factors to consider when performing data analysis.

This book introduces the problems facing Internet of Things developers and explores current technologies and techniques to help you manage, mine, and make sense of the data being collected through the use of the world’s most popular database on the Internet - MySQL. The IoT is poised to change how we interact with and perceive the world around us, and the possibilities are nearly boundless. As more and more connected devices generate data, we will need to solve the problem of how to collect, store, and make sense of IoT data by leveraging the power of database systems. The book begins with an introduction of the MySQL database system and storage of sensor data. Detailed instructions and examples are provided to show how to add database nodes to IoT solutions including how to leverage MySQL high availability, including examples of how to protect data from node outages using advanced features of MySQL. The book closes with a comparison of raw and transformed data showing how transformed data can improve understandability and help you cut through a clutter of superfluous data toward the goal of mining nuggets of useful knowledge.

The need for fast data applications is growing rapidly, driven by the IoT, the surge in machine-to-machine (M2M) data, global mobile device proliferation, and the monetization of SaaS platforms. So how do you combine real-time, streaming analytics with real-time decisions in an architecture that’s reliable, scalable, and simple? In this O’Reilly report, Ryan Betts and John Hugg from VoltDB examine ways to develop apps for fast data, using pre-defined patterns. These patterns are general enough to suit both the do-it-yourself, hybrid batch/streaming approach, as well as the simpler, proven in-memory approach available with certain fast database offerings. Their goal is to create a collection of fast data app development recipes. We welcome your contributions, which will be tested and included in future editions of this report.

Message Queuing Telemetry Transport (MQTT) is a messaging protocol designed for the Internet of Things (IoT). It is lightweight enough to be supported by the smallest devices, yet robust enough to ensure that important messages get to their destinations every time. With MQTT devices, such as energy meters, cars, trains, mobile phones and tablets, and personal health care devices, devices can communicate with each other and with other systems or applications. IBM® MessageSight is a messaging appliance designed to handle the scale and security of a robust IoT solution. MessageSight allows you to easily secure connections, configure policies for messaging, and scale to up to a million concurrently connected devices. This IBM Redbooks® publication introduces MQTT and MessageSight through a simple key fob remote MQTT application. It then dives into the architecture and development of a robust, cross-platform Ride Share and Taxi solution (PickMeUp) with real-time voice, GPS location sharing, and chat among a variety of mobile platforms. The publication also includes an addendum describing use cases in a variety of other domains, with sample messaging topology and suggestions for design.

Enterprise leaders are driving continuous transformation to stay at the forefront of real-time intelligent growth. Join us to learn how Forza Steel collaborated with Kyndryl and Microsoft to build a unified AI platform with Microsoft Fabric, Power BI, and Azure IoT to drive real-time insights, automation, and predictive analytics across manufacturing, logistics, and finance, as well as migrate their on-prem SAP to RISE on Azure.