As the axiom goes: people don't leave companies; they leave their managers. And, good analysts are constantly being approached with new opportunities. So, what's the secret formula for hanging on to analytics talent? Assuming simply chaining them to their desks isn't an option, then the trick is keeping them happy and motivated. On this episode, the gang discusses their experiences and perspectives on the topic. Tim tried to quit the show just before recording, but he then discovered that Michael had chained him to his desk. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

In this podcast, Dennis Mortensen (@DennisMortensen @XdotAI) sat with Vishal Kumar from @AnalyticsWeek to discuss his entrepreneurial journey of building successful analytics startups. He shares his journey to starting advanced analytics at AI startup x.ai and how he is solving an important productivity killer using AI. He shared his challenges and opportunities of being an early entrant into the AI startup space. He also shared his thoughts on Google Wave and Google Duplex and what to expect from these technologies in the future.

Timelines: 0:28 Dennis's journey 4:46 Dennis's "why." 9:50 Dennis's success mantra. 14:45 Making of X.ai 19:03 Educating the market 22:34 Surprises on the way 30:05 Killing the inbox 35:50 Why the calendar? 39:07 About Google. duplex 50:05 Future of work 55:00 Recommended books.

Dennis's Recommended Read: The Narrow Road: A Brief Guide to the Getting of Money by Felix Dennis amzn.to/2vaJ1S4 Undisputed Truth by Mike Tyson, Larry Sloman amzn.to/2ACOypK Shoe Dog: A Memoir by the Creator of Nike by Phil Knight amzn.to/2MaFMAu

Podcast Link: https://futureofdata.org/road-to-building-a-successful-ai-startup-dennismortensen-xdotai-futureofdata-podcast/

Dennis's BIO: Dennis Mortensen is the CEO and co-founder of x.ai.

Dennis is an expert in leveraging data to solve enterprise use cases and a serial entrepreneur who’s successfully exited several companies on that theme.

His long-term vision of killing the inbox led to the formation of x.ai and the creation of Amy + Andrew, artificially intelligent assistants who schedule meetings. He frequently speaks to anyone who’ll listen, from the crowds of Web Summit to his building’s doorman, about an optimistic future for AI, productivity, and the future of work.

Dennis was also an accredited Associate Analytics Instructor at the University of British Columbia and the author of Data-Driven Insights, on collecting and analyzing digital data.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

Wanna Join? If you or any you know wants to join in, Register your interest by emailing us @ [email protected]

Want to sponsor? Email us @ [email protected]

Keywords: FutureOfData,

DataAnalytics,

Leadership,

Futurist,

Podcast,

BigData,

Strategy

Kafka Streams in Action teaches you everything you need to know to implement stream processing on data flowing into your Kafka platform, allowing you to focus on getting more from your data without sacrificing time or effort. About the Technology Not all stream-based applications require a dedicated processing cluster. The lightweight Kafka Streams library provides exactly the power and simplicity you need for message handling in microservices and real-time event processing. With the Kafka Streams API, you filter and transform data streams with just Kafka and your application. About the Book Kafka Streams in Action teaches you to implement stream processing within the Kafka platform. In this easy-to-follow book, you’ll explore real-world examples to collect, transform, and aggregate data, work with multiple processors, and handle real-time events. You’ll even dive into streaming SQL with KSQL! Practical to the very end, it finishes with testing and operational aspects, such as monitoring and debugging. What's Inside Using the KStreams API Filtering, transforming, and splitting data Working with the Processor API Integrating with external systems About the Reader Assumes some experience with distributed systems. No knowledge of Kafka or streaming applications required. About the Author Bill Bejeck is a Kafka Streams contributor and Confluent engineer with over 15 years of software development experience. Quotes A great way to learn about Kafka Streams and how it is a key enabler of event-driven applications. - From the Foreword by Neha Narkhede, Cocreator of Apache Kafka A comprehensive guide to Kafka Streams—from introduction to production! - Bojan Djurkovic, Cvent Bridges the gap between message brokering and real-time streaming analytics. - Jim Mantheiy Jr., Next Century Valuable both as an introduction to streams as well as an ongoing reference. - Robin Coe, TD Bank

Summary

Every business with a website needs some way to keep track of how much traffic they are getting, where it is coming from, and which actions are being taken. The default in most cases is Google Analytics, but this can be limiting when you wish to perform detailed analysis of the captured data. To address this problem, Alex Dean co-founded Snowplow Analytics to build an open source platform that gives you total control of your website traffic data. In this episode he explains how the project and company got started, how the platform is architected, and how you can start using it today to get a clearer view of how your customers are interacting with your web and mobile applications.

Preamble

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline you’ll need somewhere to deploy it, so check out Linode. With private networking, shared block storage, node balancers, and a 40Gbit network, all controlled by a brand new API you’ve got everything you need to run a bullet-proof data platform. Go to dataengineeringpodcast.com/linode to get a $20 credit and launch a new server in under a minute. You work hard to make sure that your data is reliable and accurate, but can you say the same about the deployment of your machine learning models? The Skafos platform from Metis Machine was built to give your data scientists the end-to-end support that they need throughout the machine learning lifecycle. Skafos maximizes interoperability with your existing tools and platforms, and offers real-time insights and the ability to be up and running with cloud-based production scale infrastructure instantaneously. Request a demo at dataengineeringpodcast.com/metis-machine to learn more about how Metis Machine is operationalizing data science. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat This is your host Tobias Macey and today I’m interviewing Alexander Dean about Snowplow Analytics

Interview

Introductions How did you get involved in the area of data engineering and data management? What is Snowplow Analytics and what problem were you trying to solve when you started the company? What is unique about customer event data from an ingestion and processing perspective? Challenges with properly matching up data between sources Data collection is one of the more difficult aspects of an analytics pipeline because of the potential for inconsistency or incorrect information. How is the collection portion of the Snowplow stack designed and how do you validate the correctness of the data?

Cleanliness/accuracy

What kinds of metrics should be tracked in an ingestion pipeline and how do you monitor them to ensure that everything is operating properly? Can you describe the overall architecture of the ingest pipeline that Snowplow provides?

How has that architecture evolved from when you first started? What would you do differently if you were to start over today?

Ensuring appropriate use of enrichment sources What have been some of the biggest challenges encountered while building and evolving Snowplow? What are some of the most interesting uses of your platform that you are aware of?

Keep In Touch

Alex

@alexcrdean on Twitter LinkedIn

Snowplow

@snowplowdata on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Links

Snowplow

GitHub

Deloitte Consulting OpenX Hadoop AWS EMR (Elastic Map-Reduce) Business Intelligence Data Warehousing Google Analytics CRM (Customer Relationship Management) S3 GDPR (General Data Protection Regulation) Kinesis Kafka Google Cloud Pub-Sub JSON-Schema Iglu IAB Bots And Spiders List Heap Analytics

Podcast Interview

Redshift SnowflakeDB Snowplow Insights Googl

Unstructured data is the most voluminous form of data in the world, and several elements are critical for any advanced analytics practitioner leveraging SAS software to effectively address the challenge of deriving value from that data. This book covers the five critical elements of entity extraction, unstructured data, entity resolution, entity network mapping and analysis, and entity management. By following examples of how to apply processing to unstructured data, readers will derive tremendous long-term value from this book as they enhance the value they realize from SAS products.

Tell me about a time you produced an amazing analysis. Please provide your response in the form of a Jupyter notebook that uses Python or R (or both!) to pull words from a corpus that contains all words in the OED stored in a BigQuery table. I mean, that's a fair question to ask, right? No? Well, what questions and techniques are effective for assessing an analyst's likelihood of succeeding in your organization? How should those techniques differ when looking for a technical analyst as opposed to a more business-oriented one? On this episode of the show -- recorded while our recording service clearly thought it was in a job interview that it needed to deliberately tank -- Simon Rumble from Snowflake Analytics joined the gang to share ideas on the topic. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

In this podcast Jason Carmel(@defenestrate99) Chief Data Officer @ POSSIBLE talks about his journey leading data analytics practice of digital marketing agency. He sheds light on some methodologies for building a sound data science practice. He sheds light on using data science chops for doing some good while creating traditional value. He shared his perspective on keeping team-high on creativity to keep creating innovative solutions. This is a great podcast for anyone looking to understanding the digital marketing landscape and how to create a sound data science practice.

Timelines: 0:29 Jason's journey. 6:40 Advantage of having a legal background for a data scientist. 9:15 Understanding emotions based on data. 13:54 The empathy model. 14:53 From idea to inception to execution. 23:40 The role of digital agencies. 30:20 Measuring the right amount of data. 32:40 Management in a creative agency. 34:40 Leadership qualities that promote creativity. 38:14 Leader's playbook in a digital agency. 40:50 Qualities of a great data science team in the digital agency. 44:30 Leadership's role in data creativity. 47:00 Opportunites as a data scientist in the digital agency. 49:18 Future of data in digital media. 51:38 Jason's success mantra. 53:30 Jason's favorite reads. 57:11 Key takeaways.

Jason's Recommended Read: Trendology: Building an Advantage through Data-Driven Real-Time Marketing by Chris Kerns amzn.to/2zMhYkV Venomous: How Earth's Deadliest Creatures Mastered Biochemistry by Christie Wilcox amzn.to/2LhqI76

Podcast Link: https://futureofdata.org/jason-carmel-defenestrate99-possible-leading-analytics-data-digital-marketing/

Jason's BIO: Jason Carmel is Chief Data Officer at Possible. With nearly 20 years of digital data and marketing experience, Jason has worked with clients such as Coca Cola, Ford, and Microsoft to evolve digital experiences based on real-time feedback and behavioral data. Jason manages a global team of 100 digital analysts across POSSIBLE, a digital advertising agency that uses traditional and unconventional data sets and models to help brands connect more effectively with their customers.

Of particular interest is Jason’s work using data and machine learning to define and understand the emotional components of human conversation. Jason spearheaded the creation of POSSIBLE’s Empathy Model, with translates the raw, unstructured content of social media into a quantitative understanding of what customers are actually feeling about a given topic, event, or brand.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

Wanna Join? If you or any you know wants to join in, Register your interest by mailing us @ [email protected]

Want to sponsor? Email us @ [email protected]

Keywords: FutureOfData,

DataAnalytics,

Leadership,

Futurist,

Podcast,

BigData,

Strategy

This book provides an understanding of the different types of healthcare service providers, corresponding information technologies, analytic methods, and data issues that play a vital role in transforming the healthcare industry. A follow-up to Healthcare Informatics: Improving Efficiency and Productivity, this latest book includes new content that examines the evolution of Big Data and how it is revolutionizing the healthcare industry. Presenting strategies for achieving national goals for the meaningful use of health information technology, the book describes how to enhance process efficiency by linking technologies, data, and analytics with strategic initiatives.

"Data Science with SQL Server Quick Start Guide" introduces you to leveraging SQL Server's most recent features for data science projects. You will explore the integration of data science techniques using R, Python, and Transact-SQL within SQL Server's environment. What this Book will help me do Use SQL Server's capabilities for data science projects effectively. Understand and preprocess data using SQL queries and statistics. Design, train, and evaluate machine learning models in SQL Server. Visualize data insights through advanced graphing techniques. Deploy and utilize machine learning models within SQL Server environments. Author(s) Dejan Sarka is a data science and SQL Server expert with years of industry experience. He specializes in melding database systems with advanced analytics, offering practical guidance through real-world scenarios. His writing provides clear, step-by-step methods, making complex topics accessible. Who is it for? This book is tailored for professionals familiar with SQL Server who are looking to delve into data science. It is also ideal for data scientists aiming to incorporate SQL Server into their analytics workflows. The content assumes basic exposure to SQL Server, ensuring a straightforward learning curve for its audience.

This podcast spends time discussing Tim O'Reilly's futuristic perspective on data, analytics, AI, jobs, and organization. He sheds light on what are somethings businesses could do to stay relevant and future proof. He discussed his book and shared some of the key insights relevant to anyone thinking of staying relevant in the World led by technology and impacting the future. A must video for anyone working!

Timeline: 00:28 Tim's journey. 06:03 Tim's current occupation. 10:50 Interesting work for interesting people. 15:08 Thinking behind the title "What's the future". 23:41 Culture and technology evolution. 26:29 Creating value for the shareholder. 35:06 Learning a new skill. 38:12 Labor and technology. 47:07 Investing in humans or technology? 56:02 The role of AI in Media. 59:45 How can an employee stay relevant? 1:04:28 Tim's favorite books. 1:09:38 Key takeaways.

Tim's Book: WTF?: What's the Future and Why It's Up to Us by Tim O'Reilly https://amzn.to/2N5WhOn

Tim's Recommended Read: AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee https://amzn.to/2N8VGLL Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal and Joshua Gans https://amzn.to/2ugQBKr The Long Twentieth Century: Money, Power and the Origins of Our Times by Giovanni Arrighi https://amzn.to/2ufhb6R Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist by Kate Raworth https://amzn.to/2LcbLQc Winners Take All: The Elite Charade of Changing the World by Anand Giridharadas https://amzn.to/2utgeXF New Power: How Power Works in Our Hyperconnected World--and How to Make It Work for You by Jeremy Heimans and Henry Timms https://amzn.to/2NbBJ77 Seeing like a State: How Certain Schemes to Improve the Human Condition Have Failed by James C. Scott https://amzn.to/2ztnoRz The Struggle for Survival: An Historical, political, and Socioeconomic Perspective of St. Lucia by Anderson Reynolds https://amzn.to/2uqF22w

Podcast Link: https://futureofdata.org/discussing-jobs-data-and-whatsthefuture-with-timoreilly-futureofdata-podcast/

Tim's BIO: Tim O’Reilly is the founder and CEO of O’Reilly Media, Inc. His original business plan was “interesting work for interesting people,” which worked out pretty well. O’Reilly Media delivers online learning, publishes books, runs conferences, urges companies to create more value than they capture, and tries to change the world by spreading and amplifying the knowledge of innovators.

Tim has a history of convening conversations that reshape the computer industry. In 1993, he launched the first commercial, ad-supported site on the internet. In 1998, he organized the meeting where the term “open source software” was agreed on and helped the business world understand its importance. In 2004, with the Web 2.0 Summit, he defined how “Web 2.0” represented not only the resurgence of the web after the dot com bust, but a new model for the computer industry, based on big data, collective intelligence, and the internet as a platform. In 2009, with his “Gov 2.0 Summit,” he framed a conversation about the modernization of government technology that has shaped policy and spawned initiatives at the Federal, State, and local level and around the world. He has now turned his attention to the implications of AI, the on-demand economy, and other technologies that are transforming the nature of work and the future shape of the business world. This is the subject of his forthcoming book from Harper Business, WTF: What’s the Future and Why It’s Up to Us.

About #Podcast:

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

Wanna Join? If you or any you know wants to join in or sponsor, Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Futurist #Podcast #BigData #Strategy

With "Qlik Sense Cookbook," you will gain practical knowledge to harness the capabilities of Qlik Sense for effective business intelligence. This book is packed with step-by-step recipes that guide you in leveraging this powerful tool's data analytics features to create intuitive interactive dashboards and derive actionable insights. What this Book will help me do Master the process of sourcing, previewing, and distributing data through efficient interactive dashboards. Utilize the latest visualization options and learn best practices for creating impactful visuals. Develop scripts for automation and customize functionality using Qlik Sense subroutines. Enhance your Qlik Sense dashboard with advanced UI customizations and interactive elements. Leverage Qlik Sense's advanced aggregation functions like AGGR to perform multidimensional insights. Author(s) The authors of "Qlik Sense Cookbook" bring years of professional expertise in business intelligence and analytics. They have extensive experience working with Qlik platforms and have authored numerous industry-relevant resources. With a practical and accessible writing style, they thrive in breaking down complex concepts into manageable, actionable knowledge. Who is it for? This book is perfect for data analysts, business intelligence specialists, and Qlik Sense practitioners who want to advance their skills. It's suitable for beginners aiming to develop proficiency in Qlik Sense, as well as for professionals experienced with other tools like QlikView. Basic business intelligence knowledge is recommended for getting the most out of this book.

In this podcast, Don Kettl, Professor, LBJ School, the University of Texas at Austin, talks about the future of the public sector in the mid of data and analytics capability disruptions. Don talked about some of the biggest opportunities in the public policy space. He sheds light on how the future public policy officers would design the organizations that grow with time. He sheds light on the future of jobs in the public sector and how data could disrupt the space to increase its impact. This session is great for people interested in learning about public sector data and jobs impact through big data evolution.

TIMELINE: 0:28 Don's journey. 5:16 Premise of "Little bites of big data policy". 7:16 Data in the government sector. 11:18 Example of good data framework in state governments. 13:49 The need for good cooperation between the private and public sectors. 17:56 Opportunities for data in the public sector. 21:37 The failure of data in the public sector. 27:54 Perspective on open data. 33:58 Future of data in the public sector. 41:42 The role of government in data businesses. 48:58 Can government data policies go global? 55:56 Don's success mantra. 59:43 Don's reading list. 1:01:30 How does Don avoid bias? 1:07:00 Key takeaways.

Don's Book: Little Bites of Big Data for Public Policy by Donald F Kettl amzn.to/2zfpKDn Politics of the Administrative Process by Donald F Kettl amzn.to/2KS34KY and more at: amzn.to/2u12gg8

Podcast Link: https://futureofdata.org/future-of-public-sector-and-jobs-in-bigdata-world-futureofdata-podcast/

Don's BIO: Donald F. Kettl is a professor at the Lyndon B. Johnson School of Public Affairs at the University of Texas at Austin. He is also a nonresident senior fellow at the Volcker Alliance and the Brookings Institution.

Kettl is the author or editor of numerous books, including Can Governments Earn Our Trust? (2017); Little Bites of Big Data for Public Policy (2017); The Politics of the Administrative Process (7th edition, 2017). Three of his books have received national best-book awards. The Transformation of Governance (2002); and System under Stress: Homeland Security and American Politics (2005) and Escaping Jurassic Government: How to Recover America’s Lost Commitment to Competence.

He has received three-lifetime achievement awards: the American Political Science Association’s John Gaus Award, the Warner W. Stockberger Achievement Award of the International Public Management Association, and the Donald C. Stone Award of the American Society for Public Administration, for significant contributions to the field of intergovernmental relations.

Kettl holds a Ph.D. in political science from Yale University. Before his appointment at the University of Maryland, he taught at the University of Pennsylvania, Columbia University, the University of Virginia, Vanderbilt University, and the University of Wisconsin-Madison. He is a fellow of Phi Beta Kappa and the National Academy of Public Administration.

He has appeared frequently in national and international media, including National Public Radio, the Fox News Channel, Good Morning America, ABC World News Tonight, NBC Nightly News, CBS Evening News, CNN’s “Anderson Cooper 360” and “The Situation Room,” the Huffington Post, as well as public television’s News Hour and the BBC.

Kettl is a shareholder of the Green Bay Packers, along with his wife, Sue.

About #Podcast:

FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

Wanna Join? If you or any you know wants to join in, Register your interest @ analyticsweek.com/

Want to sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Business Analytics: A Data-Driven Decision Making Approach for Business-Part I,/i> provides an overview of business analytics (BA), business intelligence (BI), and the role and importance of these in the modern business decision-making. The book discusses all these areas along with three main analytics categories: (1) descriptive, (2) predictive, and (3) prescriptive analytics with their tools and applications in business. This volume focuses on descriptive analytics that involves the use of descriptive and visual or graphical methods, numerical methods, as well as data analysis tools, big data applications, and the use of data dashboards to understand business performance. The highlights of this volume are: Business analytics at a glance; Business intelligence (BI), data analytics; Data, data types, descriptive analytics; Data visualization tools; Data visualization with big data; Descriptive analytics-numerical methods; Case analysis with computer applications.

Develop applications for the big data landscape with Spark and Hadoop. This book also explains the role of Spark in developing scalable machine learning and analytics applications with Cloud technologies. Beginning Apache Spark 2 gives you an introduction to Apache Spark and shows you how to work with it. Along the way, you’ll discover resilient distributed datasets (RDDs); use Spark SQL for structured data; and learn stream processing and build real-time applications with Spark Structured Streaming. Furthermore, you’ll learn the fundamentals of Spark ML for machine learning and much more. After you read this book, you will have the fundamentals to become proficient in using Apache Spark and know when and how to apply it to your big data applications. What You Will Learn Understand Spark unified data processing platform Howto run Spark in Spark Shell or Databricks Use and manipulate RDDs Deal with structured data using Spark SQL through its operations and advanced functions Build real-time applications using Spark Structured Streaming Develop intelligent applications with the Spark Machine Learning library Who This Book Is For Programmers and developers active in big data, Hadoop, and Java but who are new to the Apache Spark platform.

Business Intelligence. It's a term that's been around for a few decades, but that is every bit as difficult to nail down as "data science," "big data," or a jellyfish. Think too hard about it, and you might actually find yourself struggling to define "analytics!" With the latest generation of BI tools, though, it's a topic that is making the rounds at cocktail parties the world over! (Cocktail parties just aren't what they used to be.) On this episode, the crew snags Taylor Udell from Heap to join in a discussion on the subject, and Moe (unsuccessfully) attempts to end the episode after six minutes. Possibly because neither Tableau nor Superset can definitively prove where avocado toast originated (but Wikipedia backs her up). But we all know Tim can't be shut up that quickly, right?! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms. Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills. What You'll Learn Understand analytics and basic data concepts Use an analytical approach to solve Industrial business problems Build predictive model with machine learning techniques Create and apply analytical strategies Who This Book Is For Data scientists, developers, statisticians, engineers, and research students with a great theoretical understanding of data and statistics who would like to enhance their skills by getting practical exposure in data modeling.

In this podcast @BesaBauta from MeryFirst talks about the compliance and privacy challenges faced in the hyper regulated industry. With her experience in health informatics, Besa shared some best practices and challenges faced by data science groups in health informatics and other similar groups in regulated space. This podcast is great for anyone looking to learn about data science compliance and privacy challenges.

TIMELINE: 0:28 Besa's journey. 6:05 Besa's current role. 9:30 Privacy and compliance in health informatics. 14:44 Are the current privacy regulations sufficient? 16:15 Data management in different organizations. 22:37 The negatives for compliance policies on data. 26:28 Hiring a good chief data officer. 30:20 Vetting a company as a CDO. 32:38 Challenges for a startup in the healthcare sector. 36:25 Common challenges for data officers in the healthcare sector. 38:29 Millenials and technology. 40:05 Leadership dealing with compliance policies. 46:26 Requirements for working in health informatics. 49:18 Ingredients of a perfect hire. 50:40 Besa's success mantra. 52:35 How does Besa stay updated? 54:37 Besa's favorite read. 57:04 Key takeaway. Besa's Recommended Read: The Art Of War by Sun Tzu and Lionel Giles https://amzn.to/2Jx2PYm

Podcast Link: https://futureofdata.org/compliance-and-privacy-in-health-informatics-by-besabauta/

Besa's BIO: Dr. Besa Bauta is the Chief Data Officer and Chief Compliance Officer for MercyFirst, a social service organization providing health and mental health services to children and adolescents in New York City. She oversees the Research, Evaluation, Analytics, and Compliance for Health (REACH) division, including data governance and security measures, analytics, risk mitigation, and policy initiatives. She is also an Adjunct Assistant Professor at NYU and previously worked as a Research Director for a USAID project in Afghanistan and as the Senior Director of Research and Evaluation at the Center for Evidence-Based Implementation and Research (CEBIR). She holds a Ph.D. in implementation science with a focus on health services, an MPH in Global Health, and an MSW. Her research has focused on health systems, mental health, and technology integration to improve population-level outcomes.

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 sponsor? Email us @ [email protected]

Keywords:

FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Navigate the fascinating intersection of healthcare and data science with the book "Healthcare Analytics Made Simple." This comprehensive guide empowers you to use Python and machine learning techniques to analyze and improve real healthcare systems. Demystify intricate concepts with Python code and SQL to gain actionable insights and build predictive models for healthcare. What this Book will help me do Understand healthcare incentives, policies, and datasets to ground your analysis in practical knowledge. Master the use of Python libraries and SQL for healthcare data analysis and visualization. Develop skills to apply machine learning for predictive and descriptive analytics in healthcare. Learn to assess quality metrics and evaluate provider performance using robust tools. Get acquainted with upcoming trends and future applications in healthcare analytics. Author(s) The authors, None Kumar and None Khader, are experts in data science and healthcare informatics. They bring years of experience teaching, researching, and applying data analytics in healthcare. Their approach is hands-on and clear, aiming to make complex topics accessible and engaging for their audience. Who is it for? This book is perfect for data science professionals eager to specialize in healthcare analytics. Additionally, clinicians aiming to leverage computing and data analytics in improving healthcare processes will find valuable insights. Programming enthusiasts and students keen to enter healthcare analytics will also greatly benefit. Tailored for beginners in this field, it is an educational yet robust resource.

Mastering Kibana 6.x is your guide to leveraging Kibana for creating impactful data visualizations and insightful dashboards. From setting up basic visualizations to exploring advanced analytics and machine learning integrations, this book equips you with the necessary skills to dive deep into your data and gain actionable insights at scale. You'll also learn to effectively manage and monitor data with powerful tools such as X-Pack and Beats. What this Book will help me do Build sophisticated dashboards to visualize elastic stack data effectively. Understand and utilize Timelion expressions for analyzing time series data. Incorporate X-Pack capabilities to enhance security and monitoring in Kibana. Extract, analyze, and visualize data from Elasticsearch for advanced analytics. Set up monitoring and alerting using Beats components for reliable data operations. Author(s) With extensive experience in big data technologies, the author brings a practical approach to teaching advanced Kibana topics. Having worked on real-world data analytics projects, their aim is to make complex concepts accessible while showing how to tackle analytics challenges using Kibana. Who is it for? This book is ideal for data engineers, DevOps professionals, and data scientists who want to optimize large-scale data visualizations. If you're looking to manage Elasticsearch data through insightful dashboards and visual analytics, or enhance your data operations with features like machine learning, then this book is perfect for you. A basic understanding of the Elastic Stack is helpful, though not required.

Uncover the power of Microsoft Power BI with this accessible and practical guide. This book introduces you to the concepts of data modeling, transformation, and visualization, ensuring that you can build effective dashboards and gain valuable insights. You'll be empowered to productively utilize Power BI in your organization to achieve your analytics goals. What this Book will help me do Connect to various data sources and harness the capabilities of the Query Editor. Transform and clean data for analysis, learning to use languages like M and R. Build robust data models with relationships and powerful DAX expressions. Create impactful reports with efficient and custom visualizations in Power BI. Deploy and administer Power BI solutions both in the cloud and on-premise. Author(s) The authors, Devin Knight, Mitchell Pearson, and Manuel Quintana, are seasoned experts in Business Intelligence and Power BI. They bring years of experience simplifying complex data challenges. Their writing is approachable and hands-on, equipping readers with the skills to solve real-world problems. Who is it for? This book is perfectly suited for professionals in Business Intelligence roles, data analysts, or those aiming to adopt Power BI solutions. Whether you're new to Power BI or have basic BI knowledge, this guide will take you from fundamentals to advanced implementations. Ideal for anyone aiming to unlock actionable insights from their data.