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Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts. This week on Making Data Simple, we have Davor Bonaci co-founder and CEO of a Seattle-based startup Kaskada. Previously, Davor served as the chair of the Apache Beam PMC and software engineer in Google Cloud since its early days and the inception of Cloud Dataflow. Show Notes 2:39 – Are you focused on the platform or the models around event based data? 6:52 – Does your company provide knowhow or is it tooling? 9:58 – What’s your secret sauce? 11:19 – How did you end up here? 15:40 – Who’s your biggest competitor?  17:13 – Can you talk to some of the common use cases? 20:30 – Are you and IDE, how does it work? 21:18 – Are you a subscription service? 22:18 – What’s your 5 year plan? 26:44 – How feature store save time and money 29:19 – Describe the company in three bullets 31:35 – What are the top skills?  Davor Bonaci  - LinkedIn Kaskada

Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

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Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.

Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.

This week on Making Data Simple, we have Wendy Gonzalez, Wendy is an executive that is passionate about building high-performing, high-functioning teams that develop and scale innovative, impactful technology. Wendy has two decades of managerial and technology leadership experience for companies including EY, Capgemini, Cycle30 (acquired by Arrow Electronics) and General Communications Inc. Wendy is an active Board Member of the Leila Janah Foundation. Show Notes 2:39 – How does a CEO run a company from home? 4:50 - Outline the Mission Statement 7:00 – How do you hire people? 8:58 – How big is the company? 9:23 – What’s your secret sauce? 12:10 – How does this tie back to social? 18:38 – Can you talk more about your statistics? 20:55 – How do you separate your business from others? 24:22 – Are these micro models reusable? 25:59 – What is a typically engagement look like? 29:30 - How do clients find you? 30:38 – How did Sama get started? Turing the flywheel Omnivore’s Delemma Sama

Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Or how to keep our traditional java application up-to-date on everything big data. At Adyen we process tens of millions of transactions a day, a number that rises every day. This means that generating reports, training machine learning models or any other operation that requires a bird’s eye view on weeks or months of data requires the use of Big Data technologies. We recently migrated to Airflow for scheduling all batch operations on our on-premise Big Data cluster. Some of these operations require input from our merchants or our support team. Merchants can for instance subscribe to reports, choose their preferred time zone, and even specify which columns they want included. After generating the reports, these reports then need to become available in our customer portal. So how do we keep track in our Customer Area which reports have been generated in Airflow? How do we launch ad-hoc backfills when one of our merchants subscribes to a new report? How do we integrate all of this into our existing monitoring pipeline? This talk will focus on how we have successfully integrated our big data platform with our existing Java web applications and how Airflow (with some simple add-ons) played a crucial role in achieving this.

In this talk we’ll see some real world examples from Firebolt customers demonstrating how Airflow is used to orchestrate operational data analytics applications with large data volumes, while keeping query latency low.

The two most common user questions at Pinterest are: 1) why is my workflow running so long? 2) why did my workflow fail - is it my issue, or a platform issue? As with any big data organization, the workflow platform is just the orchestrator but the “real” work is done on another layer, managed by another platform. There can be plenty of these, and the challenges of figuring out the root cause of an issue can be mundane and time consuming. At Pinterest, we set out to provide additional tooling in our Airflow webserver to make it a quicker inspection process and provide smart tips such as increased runtime analysis, bottleneck identifying, rca, and an easy way for backfilling. We explore deeper the tooling provided to reduce the admin load, and empower our users.

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Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts. This week on Making Data Simple, we have Matt Cowell. Matt serves as CEO at QuantHub, a leading data upskilling and assessment platform that helps companies create a data literate workforce across the entire enterprise. Matt uses his wealth of experience as a product and tech executive to forge the company strategy to address one of the most significant corporate challenges of the 2020’s, the data skill gap. Prior to QuantHub, Matt spent 15 years running product and tech at PE-backed companies, including building a product and engineering organization at Daxko - delivering 10x revenue growth, 7 acquisitions, and 3 enormously successful recapitalization. While at Daxko, Matt led the team to deliver the first machine learning/AI solution to the gym/fitness market. Matt is passionate about facilitating the data fluency of individuals and organizations all over the world and loves focusing on the people side of the equation. Show Notes 2:25 – How do you go from SVP of Products to getting into the data learning business? 3:48 - How do you define data literacy?  5:50 – Do you teach the products? 7:36 - What’s out of scope? 12:50 – Client use case 18:07 – Solving learning problems 21:14 – What does a learning plan look like? 25:08 – Define Micro 30:20 – What’s the best way to learn? 33:14 – How do you measure success? 34:47 - Are you venture capital funded? 36:10 – Do you have a fundamental leadership belief? 38:24 – What skill do you value most in a leader?    QuantHub Upskill quanthub Monetizing innovation  Ultra Learning Matt Cowell - LinkedIn Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.

Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts. This week on Making Data Simple, we have Kordel France. Kordel is the CEO of Seekar technologies. Kordel is the founder and CEO of Seekar Technologies, a tech startup that builds AI products for a variety of industries. The applications of artificial intelligence are virtually limitless, but Kordel decided to start where it matters most: helping medical professionals save lives. Since founding Seekar, Kordell and his team have put 3 different medical AI products through 4 board-reviewed clinical trials. Show Notes 2:25 - Are you venture capital funded? 4:10 – How many of the solutions involve AI imaging? 10:08 – What technologies are behind Seekar? 14:26 – Where do you think AI is going? App- covid-ai Kordel K France – LinkedIn Kordel K France – Twitter Seekartech [email protected]   Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.

Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.

This week on Making Data Simple, we have Kordel France. Kordel is the CEO of Seekar technologies. Kordel is the founder and CEO of Seekar Technologies, a tech startup that builds AI products for a variety of industries. The applications of artificial intelligence are virtually limitless, but Kordel decided to start where it matters most: helping medical professionals save lives. Since founding Seekar, Kordell and his team have put 3 different medical AI products through 4 board-reviewed clinical trials. Show Notes 1:21 – Kordel introduces himself. 5:35 – How do you get into the medical field? 7:27 - How does a non-medical guy say “hay this is what I need to do”? 9:18 – Why is it free of charge? 13:25 – Who provides the 95% accuracy? 14:55 – What is your mission statement? 20:25 – How did you get into the app business? 24:00 – What project are you most proud of? 29:27 – Why lie detection?

Tune in for part 2 next week  App- covid-ai Kordel K France – LinkedIn Kordel K France – Twitter Seekartech [email protected]    Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Summary Google pioneered an impressive number of the architectural underpinnings of the broader big data ecosystem. Now they offer the technologies that they run internally to external users of their cloud platform. In this episode Lak Lakshmanan enumerates the variety of services that are available for building your various data processing and analytical systems. He shares some of the common patterns for building pipelines to power business intelligence dashboards, machine learning applications, and data warehouses. If you’ve ever been overwhelmed or confused by the array of services available in the Google Cloud Platform then this episode is for you.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Lak Lakshmanan about the suite of services for data and analytics in Google Cloud Platform.

Interview

Introduction How did you get involved in the area of data management? Can you start by giving an overview of the tools and products that are offered as part of Google Cloud for data and analytics?

How do the various systems relate to each other for building a full workflow? How do you balance the need for clean integration between services with the need to make them useful in isolation when used as a single component of a data platform?

What have you found to be the primary motivators for customers who are adopting GCP for some or all of their data workloads? What are some of the challenges that new users of GCP encounter when working with the data and analytics products that it offers? What are the systems that you have found to be easiest to work with?

Which are the most challenging to work with, whether due to the kinds of problems that they are solving for, or due to their user experience design?

How has your work with customers fed back into the products that you are building on top of? What are some examples of architectural or software patterns that are unique to the GCP product suite? What are the most interesting, innovative, or unexpected ways that y

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Abstract

Hosted by Al Martin, VP, Data and AI Expert Services and Learning at IBM, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.

This week on Making Data Simple, we have Dale Davis Jones, who is an IBM Vice President and Distinguished Engineer in Global Technology Services, where she leads the GTS IT Architect community and Client Innovation. We also have Hai-Nhu Tran, who is the Senior Manager of Content of Design in Data and AI at IBM. Hai-Nhu and her team are responsible for the technical content experience for a large portfolio of products and platforms.

Show Notes 11:43 - What is the context and how did you get involved? 17:10 - How do you define success? 19:40 - Are you focused on IT language? 25:03 - How do you know you’re doing it right? 32:30 - What decision have already been made? 37:13 - What other challenges have you faced? 40:00 - How do you know when you’re done? 41:29 - How can people contribute? Dale Davis Jones - LinkedIn Hai-Nhu Tran - LinkedIn Blog - Words Matter: Driving Thoughtful Change Toward Inclusive Language in Technology Call for Code for Racial Justice:  Linux Foundation

Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Summary SQL is the most widely used language for working with data, and yet the tools available for writing and collaborating on it are still clunky and inefficient. Frustrated with the lack of a modern IDE and collaborative workflow for managing the SQL queries and analysis of their big data environments, the team at Pinterest created Querybook. In this episode Justin Mejorada-Pier and Charlie Gu share the story of how the initial prototype for a data catalog ended up as one of their most widely used interfaces to their analytical data. They also discuss the unique combination of features that it offers, how it is implemented, and the path to releasing it as open source. Querybook is an impressive and unique piece of technology that is well worth exploring, so listen and try it out today.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Justin Mejorada-Pier and Charlie Gu about Querybook, an open source IDE for your big data projects

Interview

Introduction How did you get involved in the area of data management? Can you describe what Querybook is and the story behind it? What are the main use cases or workflows that Querybook is designed for?

What are the shortcomings of dashboarding/BI tools that make something like Querybook necessary?

The tag line calls out the fact that Querybook is an IDE for "big data". What are the manifestations of that focus in the feature set and user experience? Who are the target users of Querybook and how does that inform the feature priorities and user experience? Can you describe how Querybook is architected?

How have the goals and design changed or evolved since you first began working on it? What were some of the assumptions or design choices that you had to unwind in the process of open sourcing it?

What is the workflow for someone building a DataDoc with Querybook?

What is the experience of working as a collaborator on an analysis?

How do you handle lifecycle management of query results? What are your thoughts on the potential for extending Querybook beyond SQL-oriented analysis and integrating something like Jupyter kernels? What are the most interesting, innovative, or unexpected ways that you have seen Querybook used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Querybook? When is Querybook the wrong choice? What do you have planned for the future of Querybook?

Contact Info

Justin

Link

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Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.

This week on Making Data Simple, we have Lillian Pierson. Lillian is CEO of Data Mania and she supports data professionals to becoming world data leaders and entrepreneurs. Lillian started Data Mania in 2012, Lillian has had 1.2 million people take her courses or read her book. 

Show Notes 3:37 – How did you make your transition to Data Mania? 8:20 – What is your Brand now? 9:32 – If I contact Lillian @ Data Mania what will I walk away with? 14:01 – Is it methodology or career coach or is it both? 16:35 – Where did you get your experience to put people in these buckets? 19:08 – Do you provide one on one services? 22:48 – Is your team worldwide? 24:36 – What differentiates your book from another? 26:48 – Who is Data Mania targeting? 28:32 – Where do I start? Email -  [email protected] Data-mania Data Superhero Quiz Data Science For Dummies  Lillian Pierson - LinkedIn 

Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.

Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.

This week on Making Data Simple, we have Milan Shetti. Milan is the President of Rocket Software and the Z Business Unit. Milan joined Rocket about a year ago and came from HP where he ran the storage business section. Before that Milan ran a startup and was also at Sun Micro Systems.

Show Notes 4:16 – What was in that conversation? 7:26 – What were you responsible for at HP? 10:24 – What position did you start in at Rocket? 12:46 – What is your mission objective at this time? 15:05 – Are you seeing growth in Z? 18:40 – What does modernization mean in Z? 22:14 – What does Rocket do that no else does? 25:50 – Are you using AI and ML in your new role? 31:14 – What is your vision as president moving forward?  The Alchemist Paulo Coelho

Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

In 'Distributed Data Systems with Azure Databricks', you will explore the capabilities of Microsoft Azure Databricks as a platform for building and managing big data pipelines. Learn how to process, transform, and analyze data at scale while developing expertise in training distributed machine learning models and integrating them into enterprise workflows. What this Book will help me do Design and implement Extract, Transform, Load (ETL) pipelines using Azure Databricks. Conduct distributed training of machine learning models using TensorFlow and Horovod. Integrate Azure Databricks with Azure Data Factory for optimized data pipeline orchestration. Utilize Delta Engine for efficient querying and analysis of data within Delta Lake. Employ Databricks Structured Streaming to manage real-time production-grade data flows. Author(s) None Palacio is an experienced data engineer and cloud computing specialist, with extensive knowledge of the Microsoft Azure platform. With years of practical application of Databricks in enterprise settings, Palacio provides clear, actionable insights through relatable examples. They bring a passion for innovative solutions to the field of big data automation. Who is it for? This book is ideal for data engineers, machine learning engineers, and software developers looking to master Azure Databricks for large-scale data processing and analysis. Readers should have basic familiarity with cloud platforms, understanding of data pipelines, and a foundational grasp of Python and machine learning concepts. It is perfect for those wanting to create scalable and manageable data workflows.

Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.

Abstract Hosted by Al Martin, VP, Data and AI Expert Services and Learning at IBM, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts. This week on Making Data Simple, we have Nancy Hensley, Nancy is currently the Chief Marketing and Product Officer for Stats Perform. Nancy was the Chief Digital Officer at IBM.

Show Notes 1:37 – Nancy’s bio 3:10 - Are we talking Money Ball? 5:52 - On Base percentage 7:08 – Analyse examples  10:02 – Do you control the data? 11:24 – Out there statistics 14:12 - Can analytics go to far? 17:35 – Real time analysis 18:45 – Covid and sports 21:15 – Your role in sports betting 22:50 – What’s the most fascinating thing you’ve learned? 25:23 – What’s the future?

Website - Stats Perform Money Ball Stats Perform - Twitter  Bill James – Baseball Abstract  The Analyst     Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Send us a text Want to be featured as a guest on Making Data Simple? Reach out to us at [[email protected]] and tell us why you should be next.

Abstract Hosted by Al Martin, VP, IBM Expert Services Delivery, Making Data Simple provides the latest thinking on big data, A.I., and the implications for the enterprise from a range of experts.

This week on Making Data Simple, we have Orr Danon CEO at Hailo Technologies. Hailo has developed a breakthrough deep learning microprocessor based on a novel architecture which enables edge devices to run sophisticated deep learning applications that could previously run only on the cloud. Orr has a decade of software and engineering experience from the Israel Defense Forces’ elite intelligence unit. Orr coordinated many of the unit’s largest and most complex interdisciplinary projects, ultimately earning the Israel Defense Award granted by Israel’s president, and the Creative Thinking Award, bestowed by the head of Israel’s military intelligence. Orr holds an M.Sc in Electrical and Electronics Engineering from Tel Aviv University and a B.Sc in Physics from the Hebrew University in Jerusalem. Show Notes 4:28 – Is Edge a hardware solution? 11:45 – Where do you think the fastest growth in the Edge is going to be? 14:01 – What makes your AI chip different? 17:10 – Anything else in your secret sauce that makes you different? 18:28 – If I am a customer what do I do for proof of technology? And do your chips work together at the Edge? 21:35 – What about security? 22:50 – Tell us about the data? 26:40 – Where will you be in 5 years? Hailo website    Connect with the Team Producer Kate Brown - LinkedIn. Producer Steve Templeton - LinkedIn. Host Al Martin - LinkedIn and Twitter.  Want to be featured as a guest on Making Data Simple? Reach out to us at [email protected] and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 1 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.

BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 2 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.

"Turn yourself into a Data Head. You'll become a more valuable employee and make your organization more successful."Thomas H. Davenport, Research Fellow, Author of Competing on Analytics, Big Data @ Work, and The AI Advantage You've heard the hype around data—now get the facts. In Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning, award-winning data scientists Alex Gutman and Jordan Goldmeier pull back the curtain on data science and give you the language and tools necessary to talk and think critically about it. You'll learn how to: Think statistically and understand the role variation plays in your life and decision making Speak intelligently and ask the right questions about the statistics and results you encounter in the workplace Understand what's really going on with machine learning, text analytics, deep learning, and artificial intelligence Avoid common pitfalls when working with and interpreting data Becoming a Data Head is a complete guide for data science in the workplace: covering everything from the personalities you’ll work with to the math behind the algorithms. The authors have spent years in data trenches and sought to create a fun, approachable, and eminently readable book. Anyone can become a Data Head—an active participant in data science, statistics, and machine learning. Whether you're a business professional, engineer, executive, or aspiring data scientist, this book is for you.