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Hands-On Data Analysis with Pandas

Hands-On Data Analysis with Pandas provides an intensive dive into mastering the pandas library for data science and analysis using Python. Through a combination of conceptual explanations and practical demonstrations, readers will learn how to manipulate, visualize, and analyze data efficiently. What this Book will help me do Understand and apply the pandas library for efficient data manipulation. Learn to perform data wrangling tasks such as cleaning and reshaping datasets. Create effective visualizations using pandas and libraries like matplotlib and seaborn. Grasp the basics of machine learning and implement solutions with scikit-learn. Develop reusable data analysis scripts and modules in Python. Author(s) Stefanie Molin is a seasoned data scientist and software engineer with extensive experience in Python and data analytics. She specializes in leveraging the latest data science techniques to solve real-world problems. Her engaging and detailed writing draws from her practical expertise, aiming to make complex concepts accessible to all. Who is it for? This book is ideal for data analysts and aspiring data scientists who are at the beginning stages of their careers or looking to enhance their toolset with pandas and Python. It caters to Python developers eager to delve into data analysis workflows. Readers should have some programming knowledge to fully benefit from the examples and exercises.

Data Warehousing with Greenplum, 2nd Edition

Data professionals are confronting the most disruptive change since relational databases appeared in the 1980s. SQL is still a major tool for data analytics, but conventional relational database management systems can’t handle the increasing size and complexity of today’s datasets. This updated edition teaches you best practices for Greenplum Database, the open source massively parallel processing (MPP) database that accommodates large sets of nonrelational and relational data. Marshall Presser, field CTO at Pivotal, introduces Greenplum’s approach to data analytics and data-driven decisions, beginning with its shared-nothing architecture. IT managers, developers, data analysts, system architects, and data scientists will all gain from exploring data organization and storage, data loading, running queries, and learning to perform analytics in the database. Discover how MPP and Greenplum will help you go beyond the traditional data warehouse. This ebook covers: Greenplum features, use case examples, and techniques for optimizing use Four Greenplum deployment options to help you balance security, cost, and time to usability Why each networked node in Greenplum’s architecture includes an independent operating system, memory, and storage Additional tools for monitoring, managing, securing, and optimizing query responses in the Pivotal Greenplum commercial database

Operationalizing the Data Lake

Big data and advanced analytics have increasingly moved to the cloud as organizations pursue actionable insights and data-driven products using the growing amounts of information they collect. But few companies have truly operationalized data so it’s usable for the entire organization. With this pragmatic ebook, engineers, architects, and data managers will learn how to build and extract value from a data lake in the cloud and leverage the compute power and scalability of a cloud-native data platform to put your company’s vast data trove into action. Holden Ackerman and Jon King of Qubole take you through the basics of building a data lake operation, from people to technology, employing multiple technologies and frameworks in a cloud-native data platform. You'll dive into the tools and processes you need for the entire lifecycle of a data lake, from data preparation, storage, and management to distributed computing and analytics. You’ll also explore the unique role that each member of your data team needs to play as you migrate to your cloud-native data platform. Leverage your data effectively through a single source of truth Understand the importance of building a self-service culture for your data lake Define the structure you need to build a data lake in the cloud Implement financial governance and data security policies for your data lake through a cloud-native data platform Identify the tools you need to manage your data infrastructure Delineate the scope, usage rights, and best tools for each team working with a data lake—analysts, data scientists, data engineers, and security professionals, among others

Send us a text  According to the Economist, 70 percent of business executives rated analytics as “very” or “extremely important,” but just 2 percent say they have achieved “broad positive results.” In this episode of Stories from the Field, host Wennie Allen talks to Carlo Appugliese of the IBM Data Science and AI Elite Team about his insights from more than 100 client engagements to improve business outcomes quickly. They discuss top challenges and barriers to AI adoption, as well as expert advice on how to overcome them. You will hear how companies are succeeding in achieving ROI and business objectives. Finally, Carlo shares his perspective on how to get started with data science and AI. 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.

There's no one else better qualified than myself, Mico Yuk, to turn BI naysayers into superfans using the BI Dashboard Formula (BIDF) methodology than today's guest, Ron Reago. It's his superpower! I met Ron in June of 2012, and he's the pinnacle of what our BIDF methodology can do for one's career and company. He's BIDF's #1 global champion in the world, a low-key but well-respected oil/gas executive and a super-smart leader. In this epic episode of AoF, Ron candidly shares his successes and failures in BI, and how you too can turn doubtful users into raving fans by changing 'the story'. So much knowledge bombs.. enjoy! Sponsor

This exciting season of AOF is sponsored by our BI Data Storytelling Mastery Accelerator 3-Day Live workshop. Our first workshop coming up on Sept 17-19 almost full! Join us and consider upgrading to a VIP. Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of three days, you'll leave with a clear BI delivery action plan. Register today!

Enjoyed the Show? Please leave us a review on iTunes.   For all links and resources mentioned visit: https://bibrainz.com/podcast/30

Data Science with Python and Dask

Dask is a native parallel analytics tool designed to integrate seamlessly with the libraries you’re already using, including Pandas, NumPy, and Scikit-Learn. With Dask you can crunch and work with huge datasets, using the tools you already have. And Data Science with Python and Dask is your guide to using Dask for your data projects without changing the way you work! About the Technology An efficient data pipeline means everything for the success of a data science project. Dask is a flexible library for parallel computing in Python that makes it easy to build intuitive workflows for ingesting and analyzing large, distributed datasets. Dask provides dynamic task scheduling and parallel collections that extend the functionality of NumPy, Pandas, and Scikit-learn, enabling users to scale their code from a single laptop to a cluster of hundreds of machines with ease. About the Book Data Science with Python and Dask teaches you to build scalable projects that can handle massive datasets. After meeting the Dask framework, you’ll analyze data in the NYC Parking Ticket database and use DataFrames to streamline your process. Then, you’ll create machine learning models using Dask-ML, build interactive visualizations, and build clusters using AWS and Docker. What's Inside Working with large, structured and unstructured datasets Visualization with Seaborn and Datashader Implementing your own algorithms Building distributed apps with Dask Distributed Packaging and deploying Dask apps About the Reader For data scientists and developers with experience using Python and the PyData stack. About the Author Jesse Daniel is an experienced Python developer. He taught Python for Data Science at the University of Denver and leads a team of data scientists at a Denver-based media technology company. We interviewed Jesse as a part of our Six Questions series. Check it out here. Quotes The most comprehensive coverage of Dask to date, with real-world examples that made a difference in my daily work. - Al Krinker, United States Patent and Trademark Office An excellent alternative to PySpark for those who are not on a cloud platform. The author introduces Dask in a way that speaks directly to an analyst. - Jeremy Loscheider, Panera Bread A greatly paced introduction to Dask with real-world datasets. - George Thomas, R&D Architecture Manhattan Associates The ultimate resource to quickly get up and running with Dask and parallel processing in Python. - Gustavo Patino, Oakland University William Beaumont School of Medicine

IBM Spectrum Scale: Big Data and Analytics Solution Brief

This IBM® Redguide™ publication describes big data and analytics deployments that are built on IBM Spectrum Scale™. IBM Spectrum Scale is a proven enterprise-level distributed file system that is a high-performance and cost-effective alternative to Hadoop Distributed File System (HDFS) for Hadoop analytics services. IBM Spectrum Scale includes NFS, SMB, and Object services and meets the performance that is required by many industry workloads, such as technical computing, big data, analytics, and content management. IBM Spectrum Scale provides world-class, web-based storage management with extreme scalability, flash accelerated performance, and automatic policy-based storage tiering from flash through disk to the cloud, which reduces storage costs up to 90% while improving security and management efficiency in cloud, big data, and analytics environments. This Redguide publication is intended for technical professionals (analytics consultants, technical support staff, IT Architects, and IT Specialists) who are responsible for providing Hadoop analytics services and are interested in learning about the benefits of the use of IBM Spectrum Scale as an alternative to HDFS.

podcast_episode
by Val Kroll , Julie Hoyer , Tim Wilson (Analytics Power Hour - Columbus (OH) , Moe Kiss (Canva) , Michael Helbling (Search Discovery)

Have you ever thought it would be a great idea to have a drink or two, grab a microphone, and then air your grievances in a public forum? Well, we did! This episode of the show was recorded in front of a live audience (No laugh tracks! No canned applause!) at the Marketing Analytics Summit (MAS) in Las Vegas. Moe, Michael, and Tim used a "What Grinds Our Gears?" application to discuss a range of challenges and frustrations that analysts face. They (well, Moe and Tim, of course) disagreed on a few of them, but they occasionally even proposed some ways to address the challenges, too. To more effectively simulate the experience, we recommend pairing this episode with a nice Japanese whiskey, which is what the live audience did! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Summary Successful machine learning and artificial intelligence projects require large volumes of data that is properly labelled. The challenge is that most data is not clean and well annotated, requiring a scalable data labeling process. Ideally this process can be done using the tools and systems that already power your analytics, rather than sending data into a black box. In this episode Mark Sears, CEO of CloudFactory, explains how he and his team built a platform that provides valuable service to businesses and meaningful work to developing nations. He shares the lessons learned in the early years of growing the business, the strategies that have allowed them to scale and train their workforce, and the benefits of working within their customer’s existing platforms. He also shares some valuable insights into the current state of the art for machine learning in the real world.

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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Integrating data across the enterprise has been around for decades – so have the techniques to do it. But, a new way of integrating data and improving streams has evolved. By integrating each silo independently – data is able to integrate without any direct relation. At CluedIn they call it “eventual connectivity”. If you want to learn more on how to deliver fast access to your data across the enterprise leveraging this new method, and the technologies that make it possible, get a demo or presentation of the CluedIn Data Hub by visiting dataengineeringpodcast.com/cluedin. And don’t forget to thank them for supporting the show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Mark Sears about Cloud Factory, masters of the art and science of labeling data for Machine Learning and more

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what CloudFactory is and the story behind it? What are some of the common requirements

Data Science Strategy For Dummies

All the answers to your data science questions Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the “what” and the “why” of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you’ll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. Learn exactly what data science is and why it’s important Adopt a data-driven mindset as the foundation to success Understand the processes and common roadblocks behind data science Keep your data science program focused on generating business value Nurture a top-quality data science team In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.

While most BI teams are shrinking, Ericsson's Analytics team has grown to triple digits in the last five years, to bring the 5G evolution to life! If you're not familiar with 5G, it's the next generation of cellular network technology that is 100 times faster than 4G! Crazy I know! I met Bjorn six years ago when Ericsson hired our team to help them create analytics to support the LTE evolution. As the Head of Automation and Analytics at Ericsson Networks, Bjorn Johansson shares his lessons learned from scaling up so quickly, his unique philosophy around analytics, and key tips to scale any BI team!   Sponsor

This exciting season of AOF is sponsored by our BI Data Storytelling Mastery Accelerator 3-Day Live workshop. Our first workshop coming up from Sept 17-19 is 75% full! Join us and consider upgrading to a VIP. Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of three days, you'll leave with a clear BI delivery action plan. Register today!

Enjoyed the Show? Please leave us a review on iTunes.   For all links and resources mentioned visit: https://bibrainz.com/podcast/29

Data science has made immense progress, but companies are still stuck with the question: how do you use data science to deliver real value to the business? They hire dozens of data scientists and invest in state-of-the-art technology, but only a few have delivered ROI and business impact. In this episode, Wayne Eckerson and Alex Vayner discuss what organizations need to do for data science success.

Alex Vayner is a Partner and Americas Data & AI Practice Leader for PA Consulting Group, an innovation and transformation consultancy. Alex has spent his entire career in data & analytics, with his last five roles focused on building and running high-performance data science teams and capabilities in consulting and corporate environments. Before joining PA Consulting, Alex ran the NA Data Science & AI practice at Capgemini. He joined Capgemini from Equifax, where he served as VP, Global Data Innovation Leader, building a team responsible for pioneering disruptive data & analytics solutions for clients across all industries.

IBM FlashSystem 900 Model AE3 Product Guide

Today's global organizations depend on the ability to unlock business insights from massive volumes of data. Now, with IBM® FlashSystem 900 Model AE3, they can make faster decisions based on real-time insights. Thus, they unleash the power of demanding applications, including these: Online transaction processing (OLTP) and analytical databases Virtual desktop infrastructures (VDIs) Technical computing applications Cloud environments Easy to deploy and manage, IBM FlashSystem® 900 Model AE3 is designed to accelerate the applications that drive your business. Powered by IBM FlashCore® Technology, IBM FlashSystem Model AE3 provides the following characteristics: Accelerate business-critical workloads, real-time analytics, and cognitive applications with the consistent microsecond latency and extreme reliability of IBM FlashCore technology Improve performance and help lower cost with new inline data compression Help reduce capital and operational expenses with IBM enhanced 3D triple-level cell (3D TLC) flash Protect critical data assets with patented IBM Variable Stripe RAID™ Power faster insights with IBM FlashCore including hardware-accelerated nonvolatile memory (NVM) architecture, purpose-engineered IBM MicroLatency® modules and advanced flash management FlashSystem 900 Model AE3 can be configured in capacity points as low as 14.4 TB to 180 TB usable and up to 360 TB effective capacity after RAID 5 protection and compression. You can couple this product with either 16 Gbps, 8 Gbps Fibre Channel, 16 Gbps NVMe over Fibre Channel, or 40 Gbps InfiniBand connectivity. Thus, the IBM FlashSystem 900 Model AE3 provides extreme performance to existing and next generation infrastructure.

Summary The market for data warehouse platforms is large and varied, with options for every use case. ClickHouse is an open source, column-oriented database engine built for interactive analytics with linear scalability. In this episode Robert Hodges and Alexander Zaitsev explain how it is architected to provide these features, the various unique capabilities that it provides, and how to run it in production. It was interesting to learn about some of the custom data types and performance optimizations that are included.

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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show! Integrating data across the enterprise has been around for decades – so have the techniques to do it. But, a new way of integrating data and improving streams has evolved. By integrating each silo independently – data is able to integrate without any direct relation. At CluedIn they call it “eventual connectivity”. If you want to learn more on how to deliver fast access to your data across the enterprise leveraging this new method, and the technologies that make it possible, get a demo or presentation of the CluedIn Data Hub by visiting dataengineeringpodcast.com/cluedin. And don’t forget to thank them for supporting the show! You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management.For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Coming up this fall is the combined events of Graphorum and the Data Architecture Summit. The agendas have been announced and super early bird registration for up to $300 off is available until July 26th, with early bird pricing for up to $200 off through August 30th. Use the code BNLLC to get an additional 10% off any pass when you register. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register. Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat Your host is Tobias Macey and today I’m interviewing Robert Hodges and Alexander Zaitsev about Clickhouse, an open source, column-oriented database for fast and scalable OLAP queries

Interview

Introduction How did you get involved in the area of data management? Can you start by explaining what Clickhouse is and how you each got involved with it?

What are the primary use cases that Clickhouse is targeting? Where does it fit in the database market and how does it compare to other column stores, both open source and commercial?

Can you describe how Clickhouse is architected? Can you talk through the lifecycle of a given record or set of records from when they first get inserted into Clickhouse, through the engine an

Are you still struggling to create and build a good social media presence in the data space? This week, I talk with real-life data analytics and BI rockstar, Kate Strachnyi. Kate is a Program Manager and Consultant of Data Analytics, as well as an author, blogger, and LinkedIn "Top-Voice" in data analytics with well over 50,000+ followers. In this knowledge filled BI masterclass, Kate teaches you the importance of social media and the exact steps you can take to up your social media game and become a rockstar!  

Sponsor

This exciting season of AOF is sponsored by our BI Data Storytelling Mastery Accelerator 3-Day Live workshop. Our first workshop coming up from Sept 17-19 is 75% full! Join us and consider upgrading to a VIP. Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of three days, you'll leave with a clear BI delivery action plan. Register today!

Enjoyed the Show? Please leave us a review on iTunes.   For all links and resources mentioned visit: https://bibrainz.com/podcast/28

Highlights  Fake streams! Playlist manipulation! Fake artists! There’s a lot of buzz about it, but what does this look like in the data?Mission   Good morning, it’s Jason here at Chartmetric with your 3-minute Data Dump where we upload charts, artists and playlists into your brain so you can stay up on the latest in the music data world.DateThis is your Data Dump for Friday, June 28th, 2019.Enter the World of Streaming ManipulationLast week’s streaming code of conduct was signed by more than 20 major companies across the industry to combat streaming fraud, which is good for artist compensation and more forthcoming to the fans.How can we think about this prickly topic from a music data perspective? And when we say “this”, it’s not just fake streams. It’s also playlist manipulation and fake artist accounts.For sure, we are in very murky waters, and there is little actual data on the phenomenon.Recently American indie label Hopeless Records estimated 3-4 percent of global streams could be fraudulent.But a 2015 MBW article mentions how 60% or more Twitter followers on top artist accounts could also be fake.Granted, these are different types of fraudulent behavior, but it’s also a huge delta to try to account for.What we can do though is search for red flags in the music data available to us.For example: if we look at playlist manipulation, here’s one way to look at the data to try to identify potentially iffy behavior:We scanned the playlist charts looking for abnormally high 28-day follower increases, and found a non-editorial hip-hop genre playlist with a 262% increase in followers in the past month.While that could just be great marketing, currently having 110K followers-an impressive number-its max artist monthly listeners, however, is only ~470, which doesn’t seem to match up.This means that the only artist on the playlist that gets a lot of its unique listeners from here is getting less than 1% of its supposed followers actually listening to them.Again, possible, especially since the playlist has about 100 current tracks on it, but it’s ranked in the first third of the playlist, so it’s not likely.That artist, which only has a little over 200 followers, is playlisted among high-profile artists like Eminem, Kanye West and Cardi B, presumably to draw traffic, which would be smart marketing if done legitimately, but if so many followers are not streaming the actual tracks...it smells a little fishy.If that weren’t enough, there’s a three-piece pop band with only 16 followers, and two other rap artists who have 4 and 17 Spotify followers, respectively.All three have their listed label as a series of numbers, then “Records DK” or “DK2”, which is a default label for the distributor DistroKid, if left untouched.DistroKid is one of the most popular digital distributors available to independent artists and an official partner distributor with Spotify.If that still isn’t enough, all the playlist album artwork looks like carbon copies of official Spotify playlist album art. Again, good marketing tactic...or borderline deception?So while it’s admittedly an analytical leap, it is very possible that a playlist curator is buying illegitimate playlist followers to make themselves look good, they dupe unknowing artists into thinking they are getting amazing exposure, and the curator gets paid accordingly and in our opinion, unfairly.We could be completely 100% wrong on this, but the point is, there are certain ways you can look at the music data to try to suss out what’s likely real, and what at least should raise some red flags.We’ll try to unpack some other types of illegitimate activity from a data perspective next week.Outro That’s it for your Daily Data Dump for Friday, June 28th, 2019. This is Jason from Chartmetric.Do you know how NPR does their ask for donations every so often? That’s what we’re about to do now! But we’re just asking for an Apple Podcasts rating.Rutger and I put at least a few hours a day into each episode, researching, writing, editing, recording, editing again, publishing to multiple platforms, checking analytics...and it’d be really cool for us to get some feedback on how we’re doing: the good/bad/ugly. So it’d only takes a few thumb swipes out of your day, and you’d be sending us so much joy: we’d appreciate it.As always, free accounts are at chartmetric.comAnd article links and show notes are at: podcast.chartmetric.comHappy Friday, have a great weekend, and see you on Monday!

Associations and Correlations

"Associations and Correlations: Unearth the powerful insights buried in your data" is a comprehensive guide for understanding and utilizing associations and correlations in data analysis. This book walks you through methods of classifying data, selecting appropriate statistical tests, and interpreting results effectively. By the end, you'll have mastered how to reveal data insights clearly and reliably. What this Book will help me do Identify and prepare datasets suitable for analysis with confidence. Understand and apply the principles of associations and correlations in data analytics. Use statistical tests to uncover univariate and multivariate relationships. Classify and interpret data into qualitative and quantitative segments effectively. Develop visual representations of data relationships to communicate insights clearly. Author(s) Lee Baker is an experienced statistician and data scientist with a passion for education. With years of teaching and mentoring professionals in data analysis, Lee excels in breaking down complex statistical concepts into understandable insights. Lee's approachable style aims to empower learners to harness their data's full potential. Who is it for? This book is designed for budding data analysts and data scientists, targeting those starting their journey into data analytics. It serves well as an introduction to the fundamentals of associations and correlations, making it suitable for beginners. If you seek a foundational understanding or a recap of key concepts, this book is for you.

Send us a text In the latest episode of: "Stories from the Field", Host Wennie Allen, IBM Data and AI, sits down with Elenita Elinon, leader of the Quantitative Research Analytics at JP Morgan Chase, recognized and awarded as one of the top 40 Women Leaders in Data and AI for her work with risk and fraud. Elenita shares her experiences, challenges, and lessons learned. Explore more about the IBM Women Leaders in AI initiative. Learn more about JPMC and their work with the IBM Data and AI Elite Team.

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.

Are you data-informed or data driven? My guest this week, Ella Hilal, the Director of Data at Shopify explains exactly why being data-informed is more important than being data-driven. Ella works at Shopify, a unicorn company in Canada that handles over $100B per year in e-commerce transactions and is growing 60%+ year over year. Besides dealing with insane amounts of data, Ella is an award-winning professor with more accolades than we can list here. Listen as Ella clarifies what it means to be a leader, and explains why being data informed is to key to transforming your organizations' data culture! Golden nuggets galore here folks.. enjoy!

Sponsor

This exciting season of AOF is sponsored by our BI Data Storytelling Mastery Accelerator 3-Day Live workshop. Our first workshop coming up from Sept 17-19 is 70% full! Join us and consider upgrading to a VIP. Many BI teams are still struggling to deliver consistent, high-engaging analytics their users love. At the end of three days, you'll leave with a clear BI delivery action plan. Register today!

Enjoyed the Show? Please leave us a review on iTunes.   For all links and resources mentioned visit: https://bibrainz.com/podcast/27

Streaming Data

Managers and staff responsible for planning, hiring, and allocating resources need to understand how streaming data can fundamentally change their organizations. Companies everywhere are disrupting business, government, and society by using data and analytics to shape their business. Even if you don’t have deep knowledge of programming or digital technology, this high-level introduction brings data streaming into focus. You won’t find math or programming details here, or recommendations for particular tools in this rapidly evolving space. But you will explore the decision-making technologies and practices that organizations need to process streaming data and respond to fast-changing events. By describing the principles and activities behind this new phenomenon, author Andy Oram shows you how streaming data provides hidden gems of information that can transform the way your business works. Learn where streaming data comes from and how companies put it to work Follow a simple data processing project from ingesting and analyzing data to presenting results Explore how (and why) big data processing tools have evolved from MapReduce to Kubernetes Understand why streaming data is particularly useful for machine learning projects Learn how containers, microservices, and cloud computing led to continuous integration and DevOps