In this episode we’re looking at the past, present, and future of artificial intelligence in higher education.

To explore this topics we’re featuring a conversation between Phil Bourne, the dean of the UVA School of Data Science, and Jeffrey Blume, the Associate Dean for Academic and Faculty Affairs, also at UVA Data Science.

Jeffrey and Phil discuss the recent trends in artificial intelligence and they look at how this will impact the student experience, the faculty and staff experience, and the research landscape in higher education.

This volume provides a roadmap for the deployment of semantic technologies in the field of data science and enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame.

About 10 years ago, Thomas Davenport & DJ Patil published the article "Data Scientist: The Sexiest Job of the 21st Century" in the Harvard Business Review. In this piece, they described the bourgeoning role of the data scientist and what it will mean for organizations and individuals in the coming decade. As time has passed, data science has become increasingly institutionalized. Once seen as a luxury, it is now deemed a necessity in every modern boardroom. Moreover as technologies like AI and systems like ChatGPT keep astonishing us with their capabilities in handling data science tasks, it raises a pertinent question: Is Data Science Still the Sexiest Job of the 21st Century? In this episode, we invited Thomas Davenport on the show to share his perspective on where data science & AI are at today, and where they are headed. Thomas Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the co-founder of the International Institute for Analytics, a Fellow of the MIT Initiative for the Digital Economy, and a Senior Advisor to Deloitte Analytics. He has written or edited twenty books and over 250 print or digital articles for Harvard Business Review (HBR), Sloan Management Review, the Financial Times, and many other publications. One of HBR’s most frequently published authors, Thomas has been at the forefront of the Process Innovation, Knowledge Management, and Analytics and Big Data movements. He pioneered the concept of “competing on analytics” with his 2006 Harvard Business Review article and his 2007 book by the same name. Since then, he has continued to provide cutting-edge insights on how companies can use analytics and big data to their advantage, and then on artificial intelligence. Throughout the episode, we discuss how data science has changed since he first published his article, how it has become more institutionalized, how data leaders can drive value with data science, the importance of data culture, his views on AI and where he thinks its going, and a lot more. Links from the Show: Working with AI by Thomas Davenport The AI Advantage: How to Put the Artificial Intelligence Revolution to Work by Thomas Davenport Harvard Business Review New Vantage Partners CCC Intelligent Solutions Radar AI

Maddie is a Sr. ML / Research Engineer in industry, published author and seasoned open-source AI leader, with 6+ years of experience in ML R&D. Her areas of interest include generative models, NLP and Human <> AI interactions. She was also a 2x startup founder, a Blockchain educator/researcher, Founder of Women Who Code - Data Science, and technical advisor to various startups and Di…

Use R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverse—a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly. You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way. You'll understand how to: Visualize: Create plots for data exploration and communication of results Transform: Discover variable types and the tools to work with them Import: Get data into R and in a form convenient for analysis Program: Learn R tools for solving data problems with greater clarity and ease Communicate: Integrate prose, code, and results with Quarto

The conversation with my next guest was going so deep and so well…it became a two part episode! Today I’m chatting with Nadiem von Heydebrand, CEO of Mindfuel. Nadiem’s career journey led him from data science to data product management, and in this first, we will focus on the skills of data product management (DPM), including design. In part 2, we jump more into Nadiem’s take on the role of the DPM. Nadiem gives actionable insights into the realities of data product management, from the challenges of actually being able to talk to your end users, to focusing on the problems and unarticulated needs of your users rather than solutions. Nadiem and I also discuss how data product managers oversee a portfolio of initiatives, and why it’s important to view that portfolio as a series of investments. Nadiem also emphasizes the value of having designers on a data team, and why he hopes we see more designers in the industry. 

Highlights/ Skip to:

Brian introduces Nadiem and his background going from data science to data product management (00:36) Nadiem gives not only his definition of a data product, but also his related definitions of ‘data as product,’ ‘data as information,’ and ‘data as a model’ products (02:19) Nadiem outlines the skill set and activities he finds most valuable in a data product manager (05:15) How a data organization typically functions and the challenges a data team faces to prove their value (11:20) Brian and Nadiem discuss the challenges and realities of being able to do discovery with the end users of data products (17:42) Nadiem outlines how a portfolio of data initiatives has a certain investment attached to it and why it’s important to generate a good result from those investments (21:30) Why Nadiem wants to see more designers in the data product space and the problems designers solve for data teams (25:37) Nadiem shares a story about a time when he wished he had a designer to convert the expressed needs of the  business into the true need of the customer (30:10) The value of solving for the unarticulated needs of your product users, and Nadiem shares how focusing on problems rather than solutions helped him (32:32) Nadiem shares how you can connect with him and find out more about his company, Mindfuel (36:07)

Quotes from Today’s Episode “The product mindset already says it quite well. When you look into classical product management, you have something called the viability, the desirability, the feasibility—so these are three very classic dimensions of product management—and the fourth dimension, we at Mindfuel define for ourselves and for applications are, is the datability.” — Nadiem von Heydebrand (06:51)

“We can only prove our [data team’s] value if we unlock business opportunities in their [clients’] lines of businesses. So, our value contribution is indirect. And measuring indirect value contribution is very difficult in organizations.” — Nadiem von Heydebrand (11:57)

“Whenever we think about data and analytics, we put a lot of investment and efforts in the delivery piece. I saw a study once where it said 3% of investments go into discovery and 90% of investments go into delivery and the rest is operations and a little bit overhead and all around. So, we have to balance and we have to do proper discovery to understand what problem do we want to solve.” — Nadiem von Heydebrand (13:59)

“The best initiatives I delivered in my career, and also now within Mindfuel, are the ones where we try to build an end responsibility from the lines of businesses, among the product managers, to PO, the product owner, and then the delivery team.” – Nadiem von Heydebrand (17:00)

“As a consultant, I typically think in solutions. And when we founded Mindfuel, my co-founder forced me to avoid talking about the solution for an entire ten months. So, in whatever meeting we were sitting, I was not allowed to talk about the solution, but only about the problem space.”  – Nadiem von Heydebrand (34:12)

“In scaled organizations, data product managers, they typically run a portfolio of data products, and each single product can be seen a little bit like from an investment point of view, this is where we putting our money in, so that’s the reason why we also have to prioritize the right use cases or product initiatives because typically we have limited resources, either it is investment money, people, resources or our time.” – Nadiem von Heydebrand (24:02)

“Unfortunately, we don’t see enough designers in data organizations yet. So, I would love to have more design people around me in the data organizations, not only from a delivery perspective, having people building amazing dashboards, but also, like, truly helping me in this kind of discovery space.” – Nadiem von Heydebrand (26:28)

Links Mindfuel: https://mindfuel.ai/ Personal LinkedIn: https://www.linkedin.com/in/nadiemvh/ Mindfuel LinkedIn: https://www.linkedin.com/company/mindfuelai/

Historically in elite team sports, there has often been a dynamic between players and their inherent abilities, and the vision of the coach. In many sports, we’ve seen coaching strategies influence the future of how the game is played. As the era of professionalism swept across many elite sports in the 90s, we saw the highest-level sports teams achieve a competitive edge by looking at the data, with sports fans often noticing a difference in the ‘feel’ of the way their team plays. In Basketball specifically, we have recently seen the rise of the 3-pointer, a riskier and much more difficult shot to accurately hit, even for professional players. But what has driven the rise of the 3-pointer? Is it another trend among coaches, or does the answer lie with data-based insights and the analysts producing these insights? Seth Partnow is the Director of North American Sports at StatsBomb, where he previously served as their Director of Basketball Analytics. Prior to joining StatsBomb in 2021, Seth was the Director of Basketball Research for the Milwaukee Bucks basketball team. Seth is also an accomplished Analyst and Author, having worked as an NBA Analyst for The Athletic since 2019 and having published his own book on basketball analytics, The Midrange Theory. Seth’s knowledge and insight bridges the gap between data analytics and elite US sport.  In the episode, Seth and Richie look into the intricate dynamics of elite basketball. Seth explores the challenges of attributing individual contributions in a sport where the outcome is significantly influenced by the complex interplay between players. Drawing from his extensive experience in the field, Seth discusses the complexities of analyzing player performance, the nuances of determining why certain players get easier or harder shots, and the difficulty of attributing credit for defensive achievements to individual players. Seth provides a comprehensive overview of the various roles within sports analytics, from data engineers to analysts, and highlights the importance of finding one's niche within these roles, particularly in the context of elite basketball. Seth also shares his personal journey into basketball analytics, offering valuable insights and advice for those interested in pursuing a career in this field, stressing the importance of introspection and understanding the unique lifestyle associated with working for a sports team, while also offering industry-agnostic advice on how to approach analyzing and using data in any context.

Quanto mais varáveis você tem, maior é a complexibilidade para validar a confiança do teste A/B. Mas um dos desafios mais comuns é fazer com que um lado do seu experimento não interfira no outro.

Para abordar este assunto nós do Data Hackers — a maior comunidade de AI e Data Science do Brasil -, chamamos um dos maiores entusiasta de Produtos Data-Driven e Data Science, que nos últimos quinze anos, tem trabalhado para fechar o "Gap" entre Data Science, Ciência da Computação e o Management das empresas.

Conheçam o Caio Gomes  — CDO na Único idTech (ex Booking/Nubank/Amazon), que conta pontos da aplicabilidade da experimentação com teste A/B, mas que também aborda sobre o impacto da ética na aplicação de testes a usuário. 

Conheça nosso convidado:

Caio Gomes  — CDO na Único idTech (ex-Booking/Nubank/Amazon)

Falamos no episódioLinks de referências:

Livro Ron Kohavi : https://www.amazon.com/s?k=ronny+kohavi&crid=114E9909G4TEQ&sprefix=ronny+kohavi%2Caps%2C233&ref=nb_sb_noss_1 Lukas vermeer: https://www.lukasvermeer.nl/ Canal Youtube  — https://www.youtube.com/channel/UCV8ZgEjwdNnZC4_FJtdDFCg

Post do episódio: https://medium.com/data-hackers/experimenta%C3%A7%C3%A3o-e-teste-a-b-data-hackers-podcast-67-28b7319a92ff

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 Making Data Simple Podcast is hosted by Al Martin, VP, IBM Expert Services Delivery, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun. This week on Making Data Simple, we have Benn Stancil, Chief Analytics Officer + Founder @ Mode. Benn is an accomplished data analyst with deep expertise in collaborative Business Intelligence and Interactive Data Science. Benn is Co-founder, President, and Chief  Analytics Officer of Mode, an award-winning SaaS company that combines the best elements of Business Intelligence (ABI), Data Science (DS) and Machine Learning (ML) to empower data teams to answer impactful questions and collaborate on analysis across a range of business functions. Under Benn’s leadership, the Mode platform has evolved to enable data teams to explore, visualize, analyze and share data in a powerful end-to-end workflow. Prior to founding Mode, Benn served in senior Analytics positions at Microsoft and Yammer, and worked as a  researcher for the International Economics Program at the Carnegie Endowment for International Peace. Benn also served as an Undergraduate Research Fellow at Wake Forest University,  where he received his B.S. in Mathematics and Economics. Benn believes in fostering a shared sense of humility and gratitude.

Show Notes 1:22 – Benn’s history7:09 – Tell us how you got to where you are today9:14 – Tell us about Mode12:08 – What is your definition of the Chief Analytics Officer?21:53 – Why do we need another BI tool?24:09 – What’s your secret sauce?27:48 – Where did the name Mode come from?28:41 – How do we use Mode?31:08 – What is you goto market strategy? 32:38 – Any client references?34:58 – “The missing piece in the modern data stack” tell us about thisMode  Email: [email protected] [email protected] Twitter: benn stancil Connect with the Team Producer Kate Brown - 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.

Microsoft Power BI Machine Learning and OpenAI offers a comprehensive exploration into advanced data analytics and artificial intelligence using Microsoft Power BI. Through hands-on, workshop-style examples, readers will discover the integration of machine learning models and OpenAI features to enhance business intelligence. This book provides practical examples, real-world scenarios, and step-by-step guidance. What this Book will help me do Learn to apply machine learning capabilities within Power BI to create predictive analytics Understand how to integrate OpenAI services to build enhanced analytics workflows Gain hands-on experience in using R and Python for advanced data visualization in Power BI Master the skills needed to build and deploy SaaS auto ML models within Power BI Leverage Power BI's AI visuals and features to elevate data storytelling Author(s) Greg Beaumont, an expert in data science and business intelligence, brings years of experience in Power BI and analytics to this book. With a focus on practical applications, Greg empowers readers to harness the power of AI and machine learning to elevate their data solutions. As a consultant and trainer, he shares his deep knowledge to help readers unlock the full potential of their tools. Who is it for? This book is ideal for data analysts, BI professionals, and data scientists who aim to integrate machine learning and OpenAI into their workflows. If you're familiar with Power BI's fundamentals and are eager to explore its advanced capabilities, this guide is tailored for you. Perfect for professionals looking to elevate their analytics to a new level, combining data science concepts with Power BI's features.

A lot of the times when we walk into a supermarket, we don't necessarily think about the impact data science had in getting these products on shelves. However, as you’ll learn in today's episode, it's safe to say there's a myriad of applications for data science in the FMCG industry. Whether be that supply chain use-cases that leverage time-series forecasting techniques, to computer vision use-cases for on-shelf optimization—the use-cases are endless here. So how can data scientists and data leaders maximize value in this space? Enter Anastasia Zygmantovich. Anastasia is a Global Data Science Director at Reckitt, which is most known for products like Airwick, Lysol, Detol, and Durex. Throughout the episode, we discuss how data science can be used in the FMCG industry, how data leaders can hire impactful data teams in this space, why FMCG is a great place to work in for data scientists, some awesome use-cases she's worked on, how data scientists can best maximize their value in this space, what generative AI means for organizations, and a lot more.

We talked about:

Boyan's background What is data strategy? Due diligence and establishing a common goal Designing a data strategy Impact assessment, portfolio management, and DataOps Data products DataOps, Lean, and Agile Data Strategist vs Data Science Strategist The skills one needs to be a data strategist How does one become a data strategist? Data strategist as a translator Transitioning from a Data Strategist role to a CTO Using ChatGPT as a writing co-pilot Using ChatGPT as a starting point How ChatGPT can help in data strategy Pitching a data strategy to a stakeholder Setting baselines in a data strategy Boyan's book recommendations

Links:

LinkedIn: https://www.linkedin.com/in/angelovboyan/ Twitter: https://twitter.com/thinking_code Github: https://github.com/boyanangelov Website: https://boyanangelov.com/

Free MLOps course: https://github.com/DataTalksClub/mlops-zoomcamp Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html

Javier de la Torre, Founder and CSO of CARTO, kicks off the Spatial Data Science Conference 2023 highlighting the nuances in geospatial current era of artificial intelligence. He demonstrates several uses such as using GPT4 to generate OpenStreetMap SQL queries to grab data and perform analysis, creating GIS systems based on prompts and more.

For more information, check out our website: https://carto.com/

When we think about video games like Call of Duty, Fifa, or Fortnite, our minds often turn to creative artists, software developers, designers, and producers. These are the people who make our favorite games a reality. But behind the scenes, data & AI actively shape our experience with our favorite video games. From the quality of video games, the accessibility of maps and worlds, even the go to market, data & AI play an impactul role in making or breaking the success of a video game. Marie de Léséleuc is an accomplished game industry professional with over a decade of experience. Marie started her career as a data analyst, and has since risen through the ranks to a data leader in the gaming industry. She's worked at companies such as Ubisoft, Warner Brothers, and most recently at Eidos, the company most well known for games such as Guardians of the Galaxy and Tomb Raider. Throughout the episode, we discuss how data science can be used in gaming, the unique challenges data teams face in gaming from really low data volumes to massive changes to production schedules and game vision. We also spoke about the difference between "AI" as we know it in data science, and AI in gaming, which informs how NPCs behave in a video game world—and a lot more.

ABOUT THE TALK: Building data tools requires us to not only think about the data team, but also about the people that the data team is serving: business users, or "non-data team people".

This talk will go over how it's super important to consider these two personas when building data tools, but it can also be a bit complicated. We will talk through a few principles we can use to build data products that are great for everyone (not just the data team!)

ABOUT THE SPEAKER: As a product manager with a background in data science, Katie Hindson loves building data products. Currently, she's working at Lightdash, an open-source BI tool that instantly turns your dbt project into a full-stack BI platform. Katie is really interested in the interaction between data teams, their tools, and the rest of the company - because the best data teams are the ones that can help everyone at the company make better decisions, faster.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai/

A vast amount of time series datasets are organized into structures with different levels or hierarchies of aggregation.

In this talk, we introduce the open-source Hierarchical Forecast library, which contains different reconciliation algorithms, preprocessed datasets, evaluation metrics, and a compiled set of statistical baseline models. This Python-based framework aims to bridge the gap between statistical modeling and Machine Learning in the time series field.

ABOUT THE SPEAKER: Max Mergenthaler is the CEO and Co-Founder of Nixtla, a time-series research and deployment startup. He is also a seasoned entrepreneur with a proven track record as the founder of multiple technology startups. With a decade of experience in the ML industry, he has extensive expertise in building and leading international data teams. Max has also made notable contributions to the Data Science field through his co-authorship of papers on forecasting algorithms and decision theory.

👉 Sign up for our “No BS” Newsletter to get the latest technical data & AI content: https://datacouncil.ai/newsletter

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai/

ABOUT THE TALK: Data scientists build models of the real world using 1s and 0s. Model Railroaders build models of the real world using plastic and metal. In the end, they’re both models and Model Railroaders have been at it way longer than we DS have. Let’s look at parallel concepts like overfitting versus the 10 foot rule, synthetic data versus prototype freelancing, or assumptions versus modeler’s license and see what lessons from other realms of model building we can bring home to DS.

ABOUT THE SPEAKER Peter Lenz (he, him) is a Geographer and Data Scientist who combines a deep domain expertise in geoinformatics and economic geography with technical skills in programming, machine learning, NLP, among others. Peter is working to create 'Big Social Science'.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai/

ABOUT THE TALK: A thoughtful discussion between AI heavyweights on what to expect in this present age of AI. The moderator will draw on their own personal experience and insight to serve up some awesome queries (#wired).

ABOUT THE SPEAKERS: Gregory Larson is the VP of Engineering at Jasper. He joined the company to build out the organization and invest in making AI a part of every creative's workflow.

In past positions Greg was the head of engineering at Divvy Pay and ObservePoint, and he led development and AI projects at Adobe, Jive/LogMeIn, and Microsoft.

Idan Gazit is a Senior Director of Research at GitHub Next, leading the Developer Experiences team. He is a hybrid designer-developer, and can usually be found geeking out about the Web, data visualization, typography, and color

Barry McCardel is the CEO and co-founder of Hex. In past positions Barry has worked at TrialSpark and Palantir Technologies.

George Mathew is the Managing Director at Insight Partners focused on venture stage investments in AI, ML, Analytics, and Data companies as they are establishing product/market Fit.

He brings 20+ years of experience developing high-growth technology startups including most recently being CEO of Kespry.

Sean Owen is the Principal Specialist for Data Science and ML at Databricks.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai/

ABOUT THE TALK: The emergence of generative AI models such as GTP-3, DALL•E, and Stable Diffusion has the potential to fundamentally change knowledge and creative work. This talk highlight the ways generative AI can enhance products, accelerating workflows and unlocking creativity. It also discusses some of the technical challenges involved in building generative AI products, including prompt chaining, data privacy, learning from human and AI feedback, and AI-human interaction.

ABOUT THE SPEAKER: Tristan Zajonc is the co-founder and CEO of Continual, an ML delivery platform that provides lifecycle management for production machine learning. He was previously CTO for Machine Learning at Cloudera and co-founder of Sense, a data science platform acquired by Cloudera in 2016. He has spent over 10 years in the trenches of machine learning infrastructure and operations.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai/

ABOUT THE TALK: Shiny object syndrome draws us from technology to technology, technique to technique, and we forget that analytics is fundamentally not a technical profession. It is a domain in which excellence is predicated on one's ability to translate, consult, advise the business with data-driven intelligence. The engineering world has gone too far, and lost the spirit of business intelligence.

In this talk, we talk about how we can get it back.We define what a 10x analyst looks like, and spoiler alert: your query optimization ninjitsu doesn't make the cut.

ABOUT THE SPEAKER: Robert Yi is a Co-founder and Chief Product Officer at Hyperquery, a new kind of data notebook for teams. Previously, he spent time at Airbnb and Wayfair, working across a wide range of projects in data science, product analytics, and open-source.

ABOUT DATA COUNCIL: Data Council (https://www.datacouncil.ai/) is a community and conference series that provides data professionals with the learning and networking opportunities they need to grow their careers.

Make sure to subscribe to our channel for the most up-to-date talks from technical professionals on data related topics including data infrastructure, data engineering, ML systems, analytics and AI from top startups and tech companies.

FOLLOW DATA COUNCIL: Twitter: https://twitter.com/DataCouncilAI LinkedIn: https://www.linkedin.com/company/datacouncil-ai/