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Keywords: FutureOfData Data Analytics Leadership Podcast Big Data Strategy
In this podcast, Robin discussed how an analytics organization functions in a collaborative culture. He shed some light on preparing a robust framework while working on policy rich setup. This talk is a must for anyone building an analytics organization with a culture-rich or policy rich environment.
Timeline: 0:29 Robin's journey. 6:02 Challenges in working as a chief data scientist. 9:50 Two breeds of data scientists. 13:38 Introducing data science into large companies. 16:57 Creating a center of excellence with data. 19:52 Challenges in working with a government agency. 22:57 Creating a self-serving system. 26:29 Defining chief data officer, chief analytics officer, chief data scientist. 28:28 Designing an architecture for a rapidly changing company culture. 31:39 Future of analytics and data leaders. 35:47 Art of doing business and science of doing business. 42:26 Perfect data science hire. 45:08 Closing remarks.
Podcast link: https://futureofdata.org/futureofdata-with-robin-thottungal-chief-data-scientist-at-epa/
Here's Robin's bio on his current EPA Role: - Leading the Data Analytics effort of 15,000+ member agency through providing strategic vision, program development, evangelizing the value of data-driven decision making, bringing a lean-start up approach to the public sector & building advanced data analytics platform capable of real-time/batch analysis.
-Serving as Chief data scientist for the agency, including directing, coordinating, and overseeing the division’s leadership of EPA’s multimedia data analytics, visualization, and predictive analysis work along with related tools, application development, and services.
-Develop and oversee the implementation of Agency policy on integration analysis of environmental data, including multimedia analysis and assessments of environmental quality, status, and trends.
-Develop, market, and implement tactical and strategic plans for the Agency’s data management, advanced data analytics, and predictive analysis work.
-Lead crossfederal, state, tribal, and local government data partnerships as well as information partnerships with other entities.
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The security challenges of a particular business may often be proportional to the amount of data they need to capture, process, and interpret. As businesses grow their security needs become ever more complex and challenging as the volume, velocity, and variety of data increases. Forward thinking organizations using data science to better process and interpret vast data stores both on-premise and in the cloud to identify threats and intrusions to their local networks and beyond.
Join us to participate in a dynamic discussion from practitioners with deep experience in the areas of data science or information security including:
• Bob Rudis, Chief Security Data Scientist, Rapid7, frequent blogger at rud.is, co-author of Data Driven Security, and ardent R open source contributor. Follow Bob on the web here. Previously, Bob was at Verizon and responsible for the Data Breach Investigations Report (DBIR) known in the security industry as "an unparalleled source of information on cybersecurity threats."
• Mark Gerner, Sr. Economic Data Scientist / Analytics Leader with 10+ years of experience designing, implementing, and communicating the results of analyses in support of customer engagement, strategic planning, and programmatic portfolio management related activities.
• Kalpesh Sheth, Co-founder & CEO, Yaxa, With 20+ years of technical expertise in data networking, network security, Intelligence Surveillance and Reconnaissance (ISR), and Cluster Computing. Before co-founding Yaxa, Sheth was Senior Technical Director at DRS Technologies (acquired by Finmeccanica S.p.A.), Director at RiverDelta Networks (acquired by Motorola and now part of Arris) and fifth employee of Digital Technology (acquired by Agilent Technologies). He is a co-author of VITA 41.6 an ANSI standard, and has spoken at numerous trade conferences as an expert panel member.
Venue Sponsor: @BoozAllen Media Sponsor: X.TAO.ai
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Keywords: FutureOfData Data Analytics Leadership Podcast Big Data Strategy
This book introduces you to the world of Big Data processing using Apache Spark and Python. You will learn to set up and run Spark on different systems, process massive datasets, and create solutions to real-world Big Data challenges with over 15 hands-on examples included. What this Book will help me do Understand the basics of Apache Spark and its ecosystem. Learn how to process large datasets with Spark RDDs using Python. Implement machine learning models with Spark's MLlib library. Master real-time data processing with Spark Streaming modules. Deploy and run Spark jobs on cloud clusters using AWS EMR. Author(s) Frank Kane spent 9 years working at Amazon and IMDb, handling and solving real-world machine learning and Big Data problems. Today, as an instructional designer and educator, he brings his wealth of experience to learners around the globe by creating accessible, practical learning resources. His teaching is clear, engaging, and designed to prepare students for real-world applications. Who is it for? This book is ideal for data scientists or data analysts seeking to delve into Big Data processing with Apache Spark. Readers who have foundational knowledge of Python, as well as some understanding of data processing principles, will find this book useful to sharpen their skills further. It is designed for those eager to learn the practical applications of Big Data tools in today's industry environments. By the end of this book, you should feel confident tackling Big Data challenges using Spark and Python.
Dive into the world of predictive analytics with 'Practical Predictive Analytics.' This comprehensive guide walks you through analyzing current and historical data to predict future outcomes. Using tools like R and Spark, you will master practical skills, solve real-world challenges, and apply predictive analytics across domains like marketing, healthcare, and retail. What this Book will help me do Learn the six steps for successfully implementing predictive analytics projects. Acquire practical skills in data cleaning, input, and model deployment using tools like R and Spark. Understand core predictive analytics algorithms and their applications in various industries. Apply data analytics techniques to solve problems in fields such as healthcare and marketing. Master methods for handling big data analytics using Databricks and Spark for effective prediction. Author(s) The author, None Winters, is an experienced data scientist and technical educator. With extensive background in predictive analytics, Winters specializes in applying statistical methods and techniques to real-world consultation scenarios. Winters brings a practical and accessible approach to this text, ensuring that learners can follow along and apply their newfound expertise effectively. Who is it for? This book is ideal for statisticians and analysts with some programming background in languages like R, who want to master predictive analytics skills. It caters to intermediate learners who aim to enhance their ability to solve complex analytical problems. Whether you're looking to advance your career or improve your proficiency in data science, this book will serve as a valuable resource for learning and growth.
In this session, Jon talks about analytics in the agency business. He discussed best practices and some operational hacks to help leaders become successful in the world of analytics in the marketing domain(one of the early adopter of technology)
Timeline: 0:29 John's journey. 6:07 Use cases for the benchmark studies at L2. 7:16 The struggles and challenges in the digital industry. 11:30 How much data is good data. 14:55 Staying relevant during times of disruption. 20:18 Analysing data of various cultures for a global company. 24:30 Art of doing business and science of doing business. 27:22 Jon's current role. 30:06 How much of L2 in facing and out facing? 31:45 Qualifying a source/platform. 35:20 Integrating a new source into the existing algorithm. 38:16 Building classifiers. 40:00 Jon's leadership style. 43:00 Client facing a leadership. 45:12 Jon's magic data science hire. 47:28 Suggestion for starting a data practice in a dissimilar industry. 50:55 World without survey. 53:11 Future of data in the digital industry.
Podcast link: https://futureofdata.org/futureofdata-jon-gibs-chief-data-officer-l2-inc/
Bio- Jon Gibs is the Chief Data Officer and Chief Data Scientist at L2, a digital research, benchmarking, and advisory services company recently acquired by the Gartner Group. Prior to his time at L2, Jon founded and was the group vice president of data science and analytics at Huge, a digital agency in Brooklyn, and spent 10 years at Nielsen running its digital analytics practice.
Jon's graduate work has been in Geography and spatial statistics at The University at Buffalo.
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Big data is flooding the business world. And we need a new generation of business analysts to make sense of it. A report from McKinsey predicts that the US workforce will be short 1.5 million big data managers and analysts by 2018. MIT Sloan is rising to the challenge with a new Master of Business Analytics Program, launched in 2016.
We speak with Dimitris Bertsimas, Professor of Management, and Director of the new MBAn program at MIT Sloan.Plus, we speak with MIT Sloan alum Ali Almossawi. His business school experience set him on a career path in data visualization; he now works for Apple. His new book, “Bad Choices,” explains computer algorithms to a wide audience.
Data & Analytics Bi-Weekly Newsletter Cast June 22, 2017
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Keywords: FutureOfData Data Analytics Leadership Podcast Big Data Strategy
Antoinette Schoar is using big data to hold the personal finance industry accountable to the people it serves. The finance professor was a founding member of the Consumer Advisory Board for the Consumer Financial Protection Bureau. We speak with Antoinette about prolific credit card offers, shoddy financial advisors, and how what’s good for the consumer can also be good for the market.
Organizations are rapidly consuming more data than ever before, and to drive their competitive advantage, they’re demanding interactive visualizations and interactive analyses of that data be embedded in their applications and business processes. This will enable them to make faster and more effective decisions based on data, not guesses. This practical book examines the considerations that software developers, product managers, and vendors need to take into account when making visualization and analytics a seamlessly integrated part of the applications they deliver, as well as the impact of migrating their applications to modern data platforms. Authors Federico Castanedo (Vodafone Group) and Andy Oram (O’Reilly Media) explore the basic requirements for embedding domain expertise with fast, powerful, and interactive visual analytics that will delight and inform customers more than spreadsheets and custom-generated charts. Particular focus is placed on the characteristics of effective visual analytics for big and fast data. Learn the impact of trends driving embedded analytics Review examples of big data applications and their analytics requirements in retail, direct service, cybersecurity, the Internet of Things, and logistics Explore requirements for embedding visual analytics in modern data environments, including collection, storage, retrieval, data models, speed, microservices, parallelism, and interactivity Take a deep dive into the characteristics of effective visual analytics and criteria for evaluating modern embedded analytics tools Use a self-assessment rating chart to determine the value of your organization’s BI in the modern data setting
Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You'll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage. What You'll Learn Use MATLAB for deep learning Discover neural networks and multi-layer neural networks Work with convolution and pooling layers Build a MNIST example with these layers Who This Book Is For Those who want to learn deep learning using MATLAB. Some MATLAB experience may be useful.
Rapid technology change is impacting organizations large and small. Mobile and Cloud computing, the Internet of Things (IoT), and “Big Data” are driving forces in organizational digital transformation. Decision support and analytics are available to many people in a business or organization. Business professionals need to learn about and understand computerized decision support for organizations to succeed. This text is targeted to busy managers and students who need to grasp the basics of computerized decision support, including: What is analytics? What is a decision support system? What is “Big Data”? What are “Big Data” business use cases? Overall, it addresses 61 fundamental questions. In a short period of time, readers can “get up to speed” on decision support, analytics, and business intelligence. The book then provides a quick reference to important recurring questions.
Data & Analytics Bi-Weekly Newsletter Cast June 8, 2017
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Keywords: FutureOfData Data Analytics Leadership Podcast Big Data Strategy
Discover how to harness the power of Apache Spark 2.x for your Big Data processing projects. In this book, you will explore over 70 cloud-ready recipes that will guide you to perform distributed data analytics, structured streaming, machine learning, and much more. What this Book will help me do Effectively install and configure Apache Spark with various cluster managers and platforms. Set up and utilize development environments tailored for Spark applications. Operate on schema-aware data using RDDs, DataFrames, and Datasets. Perform real-time streaming analytics with sources such as Apache Kafka. Leverage MLlib for supervised learning, unsupervised learning, and recommendation systems. Author(s) None Yadav is a seasoned data engineer with a deep understanding of Big Data tools and technologies, particularly Apache Spark. With years of experience in the field of distributed computing and data analysis, Yadav brings practical insights and techniques to enrich the learning experience of readers. Who is it for? This book is ideal for data engineers, data scientists, and Big Data professionals who are keen to enhance their Apache Spark 2.x skills. If you're working with distributed processing and want to solve complex data challenges, this book addresses practical problems. Note that a basic understanding of Scala is recommended to get the most out of this resource.
Learn how to transition from Excel-based business intelligence (BI) analysis to enterprise stacks of open-source BI tools. Select and implement the best free and freemium open-source BI tools for your company's needs and design, implement, and integrate BI automation across the full stack using agile methodologies. Business Intelligence Tools for Small Companies provides hands-on demonstrations of open-source tools suitable for the BI requirements of small businesses. The authors draw on their deep experience as BI consultants, developers, and administrators to guide you through the extract-transform-load/data warehousing (ETL/DWH) sequence of extracting data from an enterprise resource planning (ERP) database freely available on the Internet, transforming the data, manipulating them, and loading them into a relational database. The authors demonstrate how to extract, report, and dashboard key performance indicators (KPIs) in a visually appealing format from the relational database management system (RDBMS). They model the selection and implementation of free and freemium tools such as Pentaho Data Integrator and Talend for ELT, Oracle XE and MySQL/MariaDB for RDBMS, and Qliksense, Power BI, and MicroStrategy Desktop for reporting. This richly illustrated guide models the deployment of a small company BI stack on an inexpensive cloud platform such as AWS. What You'll Learn You will learn how to manage, integrate, and automate the processes of BI by selecting and implementing tools to: Implement and manage the business intelligence/data warehousing (BI/DWH) infrastructure Extract data from any enterprise resource planning (ERP) tool Process and integrate BI data using open-source extract-transform-load (ETL) tools Query, report, and analyze BI data using open-source visualization and dashboard tools Use a MOLAP tool to define next year's budget, integrating real data with target scenarios Deploy BI solutions and big data experiments inexpensively on cloud platforms Who This Book Is For Engineers, DBAs, analysts, consultants, and managers at small companies with limited resources but whose BI requirements have outgrown the limitations of Excel spreadsheets; personnel in mid-sized companies with established BI systems who are exploring technological updates and more cost-efficient solutions
"Data Lake for Enterprises" is a comprehensive guide to building data lakes using the Lambda Architecture. It introduces big data technologies like Hadoop, Spark, and Flume, showing how to use them effectively to manage and leverage enterprise-scale data. You'll gain the skills to design and implement data systems that handle complex data challenges. What this Book will help me do Master the use of Lambda Architecture to create scalable and effective data management systems. Understand and implement technologies like Hadoop, Spark, Kafka, and Flume in an enterprise data lake. Integrate batch and stream processing techniques using big data tools for comprehensive data analysis. Optimize data lakes for performance and reliability with practical insights and techniques. Implement real-world use cases of data lakes and machine learning for predictive data insights. Author(s) None Mishra, None John, and Pankaj Misra are recognized experts in big data systems with a strong background in designing and deploying data solutions. With a clear and methodical teaching style, they bring years of experience to this book, providing readers with the tools and knowledge required to excel in enterprise big data initiatives. Who is it for? This book is ideal for software developers, data architects, and IT professionals looking to integrate a data lake strategy into their enterprises. It caters to readers with a foundational understanding of Java and big data concepts, aiming to advance their practical knowledge of building scalable data systems. If you're eager to delve into cutting-edge technologies and transform enterprise data management, this book is for you.
Data & Analytics Bi-Weekly Newsletter Cast May 25, 2017
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Keywords: FutureOfData Data Analytics Leadership Podcast Big Data Strategy
In this session, Nathaniel discussed how NFPA uses data to empower fire stations worldwide with data-driven insights. We discussed the future of fire in this tech-driven world.
Timeline: 0:29 Nathaniel's journey. 3:50 What's NFPA? 6:12 Nathaniel's role in NFPA. 8:50 Nathaniel's book. 12:21 The data science team at NFPA. 15:01 Working with the government. 18:50 Interesting use cases of NFPA. 25:49 Fining tuning the data model at NFPA. 28:11 NFPA alliance with the Insurance industry. 31:33 Recruiting an idea concept or tool. 33:16 How to approach NFPA? 36:03 Nathaniel's role: in facing or outfacing? 40:41 Suggestions for Non-profits to build a data science practice. 43:49 Putting together a data science team. 46:34 Predicting the fire outcome. 48:11 Closing remarks.
Podcast link: https://futureofdata.org/futureofdata-nathaniel-lin-chief-data-scientist-nfpa/
Bio- Nathaniel Lin has an extensive background in business and marketing analytics with strategic roles in both start-ups and Fortune 500 companies. He offers the National Fire Protection Association (NFPA) agency and client perspective gleaned from his work at Fidelity Investments, OgilvyOne, Aspen Marketing, and IBM Worldwide. During his tenure with IBM Asia Pacific, he also built and led a marketing analytics group that won a DMA/NCDM Gold Award in B2B Marketing.
Lin served as an adjunct professor of business analytics at Boston College and Georgia Tech College of Management. He is also the founder of two LinkedIn groups related to big data analytics and is the 2014 author of Applied Business Analytics – Integrating Business Process, Big Data, and Advanced Analytics. Lin has an MBA in Management of Technology/Sloan Fellows from MIT Sloan School of Management and earned both a Ph.D. In Environmental Engineering and an Honors B.S from Birmingham University in England.
Founded in 1896, NFPA is a global, nonprofit organization devoted to eliminating death, injury, property, and economic loss due to fire, electrical and related hazards. The association delivers information and knowledge through more than 300 consensus codes and standards, research, training, education, outreach, and advocacy; and partner with others who share an interest in furthering the NFPA mission. For more information, visit www.nfpa.org.
The podcast is sponsored by: TAO.ai(https://tao.ai), Artificial Intelligence Driven Career Coach
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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
Data & Analytics Bi-Weekly Newsletter Cast May 11, 2017
Onalytica link: http://www.onalytica.com/blog/posts/big-data-2017-top-100-influencers-brands/
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Keywords: FutureOfData Data Analytics Leadership Podcast Big Data Strategy