Mergulhamos no universo da Inteligência Artificial e da liderança de dados no Brasil. Para isso, convidamos os principais lideres do conselho do evento AI & Data Leaders — o maior encontro entre CDAOs, tomadores de decisão em AI e Data Analytics do Brasil-, para discutir os desafios, oportunidades dessa área em constante evolução e como as empresas estão utilizando a IA para impulsionar seus negócios.

Neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, conheçam Carina Ameijeiras — Executiva de Data & Analytics e atuante no conselho AI Data Leaders; Daniel Sérman — Diretor Executivo da TIM e membro do Conselho AI Data Leaders.

[Embedar_Episódio]

Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Caso queira, você também pode ouvir o episódio aqui no post mesmo!

Nossa Bancada Data Hackers:

Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart.

Monique Femme — Head of Community Management na Data Hackers

Gabriel Lages — Co-founder da Data Hackers e Data & Analytics Sr. Director na Hotmart.

In this episode you'll hear best practices for building and leading analytics teams, the impact AI is already making on the industry, and how data teams should be thinking about the future.   Director of Analytics & Data Science Joe Squire will share practical tips for data leaders looking to build effective teams and navigate through today's rapidly changing environment, and give his thoughts on where things are headed.   What You'll Learn: Best practices for building and leading strong analytics teams The ways AI is changing analytics and where the data industry is headed How leaders need to be thinking AI and the long-term impact on their teams   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guest: Joe Squire is a Director of Analytics & Data Science in the healthcare industry. Joe helps companies like UPMC manage and use their data in a meaningful way to improve their healthcare outcomes. Follow Joe on LinkedIn

Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Introductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration Statistics for Data Science and Analytics is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. A range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of “kitchen sink” formulas. Regression is taught both as a tool for explanation and for prediction. This book is informed by the authors’ experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. Statistics for Data Science and Analytics includes information on sample topics such as: Int, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and sets Experiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary data Specialized Python packages like numpy, scipy, pandas, scikit-learn and statsmodels—the workhorses of data science—and how to get the most value from them Statistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions Written by and for data science instructors, Statistics for Data Science and Analytics is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.

The rapid rise of generative AI is changing how businesses operate, but with this change comes new challenges. How do you navigate the balance between innovation and risk, especially in a regulated industry? As organizations race to adopt AI, it’s crucial to ensure that these technologies are not only transformative but also responsible. What steps can you take to harness AI’s potential while maintaining control and transparency? And how can you build excitement and trust around AI within your organization, ensuring that everyone is ready to embrace this new era? Steve Holden is the Senior Vice President and Head of Single-Family Analytics at Fannie Mae, leading a team of data science professionals, supporting loan underwriting, pricing and acquisition, securitization, loss mitigation, and loan liquidation for the company’s multi-trillion-dollar Single-Family mortgage portfolio. He is also responsible for all Generative AI initiatives across the enterprise. His team provides real-time analytic solutions that guide thousands of daily business decisions necessary to manage this extensive mortgage portfolio. The team comprises experts in econometric models, machine learning, data engineering, data visualization, software engineering, and analytic infrastructure design. Holden previously served as Vice President of Credit Portfolio Management Analytics at Fannie Mae. Before joining Fannie Mae in 1999, he held several analytic leadership roles and worked on economic issues at the Economic Strategy Institute and the U.S. Bureau of Labor Statistics. In the episode Adel and Steve explore opportunities in generative AI, building a GenAI program, use-case prioritization, driving excitement and engagement for an AI-first culture, skills transformation, governance as a competitive advantage, challenges of scaling AI, future trends in AI, and much more.  Links Mentioned in the Show: Fannie MaeSteve’s recent DataCamp Webinar: Bringing Generative AI to the EnterpriseVideo: Andrej Karpathy - [1hr Talk] Intro to Large Language ModelsSkill Track - AI Business FundamentalsRelated Episode: Generative AI at EY with John Thompson, Head of AI at EYRewatch sessions from RADAR: AI Edition Join the DataFramed team! Data Evangelist Data & AI Video Creator New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

Until recently, Nik Suresh wrote under a mysterious blog that had several viral posts, including the famous "I Will F*cking Piledrive You If You Mention AI Again." For the longest time, he was an underground sensation, with nobody (not even his friends) knowing his identity.

In this episode, we chat about his blog posts (I'm a huge fan), the realities of data science and data engineering, and much more. This is a very candid and fun chat where I'm actually the fanboy, so enjoy!

Blog: https://ludic.mataroa.blog/

Prepare-se para uma experiência inovadora e disruptiva ! No primeiro episódio do Podcast Data Hackers dublado por AI, vamos explorar os debates mais atuais e relevantes entre MLOps vs LLMOps. Este episódio marca uma nova era no nosso podcast, trazendo conteúdos em português com a precisão e naturalidade das vozes geradas por inteligência artificial.

Neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, conheçam Demetrios Brinkmann, Co-FounderCo-Founder MLOps Community , vai compartilhar suas experiências e insights sobre como o dia a dia nesse campo evoluiu ao longo do tempo. Discutimos as principais diferenças técnicas entre MLOps e LLMOps, e o que isso significa para os profissionais que atuam ou desejam atuar nessa área.

Se você é apaixonado pela área de dados, quer entender as últimas tendências e se atualizar sobre o futuro do MLOps, não pode perder este episódio!

Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Caso queira, você também pode ouvir o episódio aqui no post mesmo!

Nossa Bancada Data Hackers:

Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart. Monique Femme — Head of Community Management na Data Hackers Gabriel Lages — Co-founder da Data Hackers e Data & Analytics Sr. Director na Hotmart.

Referências no Medium.

0:00

hi everyone Welcome to our event this event is brought to you by data dos club which is a community of people who love

0:06

data and we have weekly events and today one is one of such events and I guess we

0:12

are also a community of people who like to wake up early if you're from the states right Christopher or maybe not so

0:19

much because this is the time we usually have uh uh our events uh for our guests

0:27

and presenters from the states we usually do it in the evening of Berlin time but yes unfortunately it kind of

0:34

slipped my mind but anyways we have a lot of events you can check them in the

0:41

description like there's a link um I don't think there are a lot of them right now on that link but we will be

0:48

adding more and more I think we have like five or six uh interviews scheduled so um keep an eye on that do not forget

0:56

to subscribe to our YouTube channel this way you will get notified about all our future streams that will be as awesome

1:02

as the one today and of course very important do not forget to join our community where you can hang out with

1:09

other data enthusiasts during today's interview you can ask any question there's a pin Link in live chat so click

1:18

on that link ask your question and we will be covering these questions during the interview now I will stop sharing my

1:27

screen and uh there is there's a a message in uh and Christopher is from

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you so we actually have this on YouTube but so they have not seen what you wrote

1:39

but there is a message from to anyone who's watching this right now from Christopher saying hello everyone can I

1:46

call you Chris or you okay I should go I should uh I should look on YouTube then okay yeah but anyways I'll you don't

1:53

need like you we'll need to focus on answering questions and I'll keep an eye

1:58

I'll be keeping an eye on all the question questions so um

2:04

yeah if you're ready we can start I'm ready yeah and you prefer Christopher

2:10

not Chris right Chris is fine Chris is fine it's a bit shorter um

2:18

okay so this week we'll talk about data Ops again maybe it's a tradition that we talk about data Ops every like once per

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year but we actually skipped one year so because we did not have we haven't had

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Chris for some time so today we have a very special guest Christopher Christopher is the co-founder CEO and

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head chef or hat cook at data kitchen with 25 years of experience maybe this

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is outdated uh cuz probably now you have more and maybe you stopped counting I

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don't know but like with tons of years of experience in analytics and software engineering Christopher is known as the

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co-author of the data Ops cookbook and data Ops Manifesto and it's not the

3:00

first time we have Christopher here on the podcast we interviewed him two years ago also about data Ops and this one

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will be about data hops so we'll catch up and see what actually changed in in

3:13

these two years and yeah so welcome to the interview well thank you for having

3:19

me I'm I'm happy to be here and talking all things related to data Ops and why

3:24

why why bother with data Ops and happy to talk about the company or or what's changed

3:30

excited yeah so let's dive in so the questions for today's interview are prepared by Johanna berer as always

3:37

thanks Johanna for your help so before we start with our main topic for today

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data Ops uh let's start with your ground can you tell us about your career Journey so far and also for those who

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have not heard have not listened to the previous podcast maybe you can um talk

3:55

about yourself and also for those who did listen to the previous you can also maybe give a summary of what has changed

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in the last two years so we'll do yeah so um my name is Chris so I guess I'm

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a sort of an engineer so I spent about the first 15 years of my career in

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software sort of working and building some AI systems some non- AI systems uh

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at uh Us's NASA and MIT linol lab and then some startups and then um

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Microsoft and then about 2005 I got I got the data bug uh I think you know my

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kids were small and I thought oh this data thing was easy and I'd be able to go home uh for dinner at 5 and life

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would be fine um because I was a big you started your own company right and uh it didn't work out that way

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and um and what was interesting is is for me it the problem wasn't doing the

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data like I we had smart people who did data science and data engineering the act of creating things it was like the

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systems around the data that were hard um things it was really hard to not have

5:11

errors in production and I would sort of driving to work and I had a Blackberry at the time and I would not look at my

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Blackberry all all morning I had this long drive to work and I'd sit in the parking lot and take a deep breath and

5:24

look at my Blackberry and go uh oh is there going to be any problems today and I'd be and if there wasn't I'd walk and

5:30

very happy um and if there was I'd have to like rce myself um and you know and

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then the second problem is the team I worked for we just couldn't go fast enough the customers were super

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demanding they didn't care they all they always thought things should be faster and we are always behind and so um how

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do you you know how do you live in that world where things are breaking left and right you're terrified of making errors

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um and then second you just can't go fast enough um and it's preh Hadoop era

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right it's like before all this big data Tech yeah before this was we were using

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uh SQL Server um and we actually you know we had smart people so we we we

6:14

built an engine in SQL Server that made SQL Server a column or

6:20

database so we built a column or database inside of SQL Server um so uh

6:26

in order to make certain things fast and and uh yeah it was it was really uh it's not

6:33

bad I mean the principles are the same right before Hadoop it's it's still a database there's still indexes there's

6:38

still queries um things like that we we uh at the time uh you would use olap

6:43

engines we didn't use those but you those reports you know are for models it's it's not that different um you know

6:50

we had a rack of servers instead of the cloud um so yeah and I think so what what I

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took from that was uh it's just hard to run a team of people to do do data and analytics and it's not

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really I I took it from a manager perspective I started to read Deming and

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think about the work that we do as a factory you know and in a factory that produces insight and not automobiles um

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and so how do you run that factory so it produces things that are good of good

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quality and then second since I had come from software I've been very influenced

7:29

by by the devops movement how you automate deployment how you run in an agile way how you

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produce um how you how you change things quickly and how you innovate and so

7:41

those two things of like running you know running a really good solid production line that has very low errors

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um and then second changing that production line at at very very often they're kind of opposite right um and so

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how do you how do you as a manager how do you technically approach that and

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then um 10 years ago when we started data kitchen um we've always been a profitable company and so we started off

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uh with some customers we started building some software and realized that we couldn't work any other way and that

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the way we work wasn't understood by a lot of people so we had to write a book and a Manifesto to kind of share our our

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methods and then so yeah we've been in so we've been in business now about a little over 10

8:28

years oh that's cool and uh like what

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uh so let's talk about dat offs and you mentioned devops and how you were inspired by that and by the way like do

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you remember roughly when devops as I think started to appear like when did people start calling these principles

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and like tools around them as de yeah so agile Manifesto well first of all the I

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mean I had a boss in 1990 at Nasa who had this idea build a

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little test a little learn a lot right that was his Mantra and then which made

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made a lot of sense um and so and then the sort of agile software Manifesto

9:14

came out which is very similar in 2001 and then um the sort of first real

9:22

devops was a guy at Twitter started to do automat automated deployment you know

9:27

push a button and that was like 200 Nish and so the first I think devops

9:33

Meetup was around then so it's it's it's been 15 years I guess 6 like I was

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trying to so I started my career in 2010 so I my first job was a Java

9:44

developer and like I remember for some things like we would just uh SFTP to the

9:52

machine and then put the jar archive there and then like keep our fingers crossed that it doesn't break uh uh like

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it was not really the I wouldn't call it this way right you were deploying you

10:06

had a Dey process I put it yeah

10:11

right was that so that was documented too it was like put the jar on production cross your

10:17

fingers I think there was uh like a page on uh some internal Viki uh yeah that

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describes like with passwords and don't like what you should do yeah that was and and I think what's interesting is

10:33

why that changed right and and we laugh at it now but that was why didn't you

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invest in automating deployment or a whole bunch of automated regression

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tests right that would run because I think in software now that would be rare

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that people wouldn't use C CD they wouldn't have some automated tests you know functional

10:56

regression tests that would be the

Vamos mergulhar no fascinante mundo da visão computacional com Carlos Melo, Computer Vision Engineer, que nos guiará desde os conceitos básicos até o funcionamento de modelos de visão computacional e onde eles estão presentes no nosso dia a dia.

Neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, conheçam Carlos Melo — Computer Vision Engineer, que também abordará temas polêmicos, como os preconceitos e vieses que podem ser propagados por essas tecnologias, e discutirá como a chegada dos Large Language Models (LLMs) pode impactar o futuro da visão computacional.

Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Caso queira, você também pode ouvir o episódio aqui no post mesmo!

Nossa Bancada Data Hackers:

Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart. Monique Femme — Head of Community Management na Data Hackers Gabriel Lages — Co-founder da Data Hackers e Data & Analytics Sr. Director na Hotmart.

Referências:

Acesse nosso Medium.

Henry Tornow, a DataScientest alumnus, will share his journey through the DataScientest program, key learnings, and the impact on his career in data science.

The Data Product Management In Action podcast, brought to you by Soda and executive producer Scott Hirleman, is a platform for data product management practitioners to share insights and experiences. We've released a special edition series of minisodes of our podcast. Recorded live at Data Connect 2024, our host Michael Toland engages in short, sweet, informative, and delightful conversations with five prevelant practitioners who are forging their way forward in data and technology.

About our host Michael Toland: Michael is a Product Management Coach and Consultant with Pathfinder Product, a Test Double Operation. Since 2016, Michael has worked on large-scale system modernizations and migration initiatives at Verizon. Outside his professional career, Michael serves as the Treasurer for the New Leaders Council, mentors with Venture for America, sings with the Columbus Symphony, and writes satire for his blog Dignified Product. He is excited to discuss data product management with the podcast audience. Connect with Michael on LinkedIn About our guest Jean-Georges Perrin: Jean-Georges “jgp” Perrin is the Chief Innovation Officer at AbeaData, where he focuses on developing cutting-edge data tooling. He chairs the Open Data Contract Standard (ODCS) at the Linux Foundation's Bitol project, co-founded the AIDA User Group, and has authored several influential books, including Implementing Data Mesh (O'Reilly) and Spark in Action, 2nd Edition (Manning). With over 25 years in IT, Jean-Georges is recognized as a Lifetime IBM Champion, a PayPal Champion, and a Data Mesh MVP. His expertise spans data engineering, governance, and the industrialization of data science. Outside of tech, he enjoys exploring Upstate New York and New England with his family. Connect with J-GP on LinkedIn.  All views and opinions expressed are those of the individuals and do not necessarily reflect their employers or anyone else. Join the conversation on LinkedIn. Apply to be a guest or nominate a practitioner.  Do you love what you're listening to? Please rate and review the podcast, and share it with fellow practitioners you know. Your support helps us reach more listeners and continue providing valuable insights!

Guess what? Data science and AI initiatives are still failing here in 2024—despite widespread awareness. Is that news? Candidly, you’ll hear me share with Evan Shellshear—author of the new book Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics—about how much I actually didn’t want to talk about this story originally on my podcast—because it’s not news! However, what is news is what the data says behind Evan’s findings—and guess what? It’s not the technology.

In our chat, Evan shares why he wanted to leverage a human approach to understand the root cause of multiple organizations’ failures and how this approach highlighted the disconnect between data scientists and decision-makers. He explains the human factors at play, such as poor problem surfacing and organizational culture challenges—and how these human-centered design skills are rarely taught or offered to data scientists. The conversation delves into why these failures are more prevalent in data science compared to other fields, attributing it to the complexity and scale of data-related problems. We also discuss how analytically mature companies can mitigate these issues through strategic approaches and stakeholder buy-in. Join us as we delve into these critical insights for improving data science project outcomes.

Highlights/ Skip to:

(4:45) Why are data science projects still failing? (9:17) Why is the disconnect between data scientists and decision-makers so pronounced relative to, say, engineering?  (13:08) Why are data scientists not getting enough training for real-world problems? (16:18) What the data says about failure rates for  mature data teams vs. immature data teams (19:39) How to change people’s opinions so they value data more (25:16) What happens at the stage where the beneficiaries of data don’t actually see the benefits? (31:09) What are the skills needed to prevent a repeating pattern of creating data products that customers ignore?? (37:10) Where do more mature organizations find non-technical help to complement their data science and AI teams?  (41:44) Are executives and directors aware of the skills needed to level up their data science and AI  teams?

Quotes from Today’s Episode “People know this stuff. It’s not news anymore. And so, the reason why we needed this was really to dig in. And exactly like you did, like, keeping that list of articles is brilliant, and knowing what’s causing the failures and what’s leading to these issues still arising is really important. But at some point, we need to approach this in a scientific fashion, and we need to unpack this, and we need to really delve into the details beyond just the headlines and the articles themselves. And start collating and analyzing this to properly figure out what’s going wrong, and what do we need to do about it to fix it once and for all so you can stop your endless collection, and the AI Incident Database that now has over 3500 entries. It can hang its hat and say, ‘I’ve done my job. It’s time to move on. We’re not failing as we used to.’” - Evan Shellshear (3:01) "What we did is we took a number of different studies, and we split companies into what we saw as being analytically mature—and this is a common, well-known thing; there are many maturity frameworks exist across data, across AI, across all different areas—and what we call analytically immature, so those companies that probably aren’t there yet. And what we wanted to draw a distinction is okay, we say 80% of projects fail, or whatever the exact number is, but for who? And for what stage and for what capability? And so, what we then went and did is we were able to take our data and look at which failures are common for analytically immature organizations, and which failures are common for analytically mature organizations, and then we’re able to understand, okay, in the market, how many organizations do we think are analytically mature versus analytically immature, and then we were able to take that 80% failure rate and establish it. For analytically mature companies, the failure rate is probably more like 40%. For analytically immature companies, it’s over 90%, right? And so, you’re exactly right: organizations can do something about it, and they can build capabilities in to mitigate this. So definitely, it can be reduced. Definitely, it can be brought down. You might say, 40% is still too high, but it proves that by bringing in these procedures, you’re completely correct, that it can be reduced.” - Evan Shellshear (14:28) "What happens with the data science person, however, is typically they’re seen as a cost center—typically, not always; nowadays, that dialog is changing—and what they need to do is find partners across the other parts of the business. So, they’re going to go into the supply chain team, they’ll go into the merchandising team, they’ll go into the banking team, they’ll go into the other teams, and they’re going to find their supporters and winners there, and they’re going to probably build out from there. So, the first step would likely be, if you’re a big enough organization that you’re not having that strategy the executive level is to find your friends—and there will be some of the organization who support this data strategy—and get some wins for them.” - Evan Shellshear (24:38) “It’s not like there’s this box you put one in the other in. Because, like success and failure, there’s a continuum. And companies as they move along that continuum, just like you said, this year, we failed on the lack of executive buy-in, so let’s fix that problem. Next year, we fail on not having the right resources, so we fix that problem. And you move along that continuum, and you build it up. And at some point as you’re going on, that failure rate is dropping, and you’re getting towards that end of the scale where you’ve got those really capable companies that live, eat, and breathe data science and analytics, and so have to have these to be able to survive, otherwise a simple company evolution would have wiped them out, and they wouldn’t exist if they didn’t have that capability, if that’s their core thing.” - Evan Shellshear (18:56)

“Nothing else could be correct, right? This subjective intuition and all this stuff, it’s never going to be as good as the data. And so, what happens is, is you, often as a data scientist—and I’ve been subjected to this myself—come in with this arrogance, this kind of data-driven arrogance, right? And it’s not a good thing. It puts up barriers, it creates issues, it separates you from the people.” - Evan Shellshear (27:38) "Knowing that you’re going to have to go on that journey from day one, you can’t jump from level zero to level five. That’s what all these data maturity models are about, right? You can’t jump from level zero data maturity to level five overnight. You really need to take those steps and build it up.” - Evan Shellshear (45:21) "What we’re talking about, it’s not new. It’s just old wine in a new skin, and we’re just presenting it for the data science age." - Evan Shellshear (48:15)

Links Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype: https://www.routledge.com/Why-Data-Science-Projects-Fail-the-Harsh-Realities-of-Implementing-AI-and-Analytics-without-the-Hype/Gray-Shellshear/p/book/9781032660301  LinkedIn: https://www.linkedin.com/in/eshellshear/  Get the Book: Get 20% off at Routledge.com w/ code dspf20   Get it at Amazon

Why do we still teach people to calculate? (People I Mostly Admire podcast)

There’s been a lot of pressure to add AI to almost every digital tool and service recently, and two years into the AI hype cycle, we’re seeing two types of problems. The first is organizations that haven’t done much yet with AI because they don’t know where to start. The second is organizations that rushed into AI and failed because they didn’t know what they were doing. Both are symptoms of the same problem: not having an AI strategy and not understanding how to tactically implement AI. There’s a lot to consider around choosing the right project and putting processes and skilled talent in place, not to mention worrying about costs and return on investment. Tathagat Varma is the Global TechOps Leader at Walmart Global Tech. Tathagat is responsible for leading strategic business initiatives, enterprise agile transformation, technical learning and enablement, strategic technical initiatives, startup ecosystem engagement, and internal events across Walmart Global Tech. He also provides support to horizontal technical and internal innovation programs in the company. Starting as a Computer Scientist with DRDO, and with an overall experience of 27 years, Tathagat has played significant technical and leadership roles in establishing and growing organizations like NerdWallet, ChinaSoft International, McAfee, Huawei, Network General, NetScout System, [24]7 Innovations Labs and Yahoo!, and played key engineering roles at Siemens and Philips. In the episode, Richie and Tathagat explore failures in AI adoption, the role of leadership in AI adoption, AI strategy and business objective alignment, investment and timeline for AI projects, identifying starter AI projects, skills for AI success, building a culture of AI adoption, the potential of AI and much more.  Links Mentioned in the Show: Walmart Global TechConnect with Tathagat[Course] Data Governance ConceptsRelated Episode: How Walmart Leverages Data & AI with Swati Kirti, Sr Director of Data Science at WalmartRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

Em parceria com o Grupo Boticário, abordamos os desafios e iniciativas para construir um setor de dados mais inclusivo e diverso.

Nos últimos anos, a pesquisa State of Data Brazil destacou as barreiras enfrentadas por profissionais da área de dados e dificuldades de progressão na carreira, para pessoas com deficiência. 

Discutimos como empresas e líderes podem criar ambientes de trabalho que valorizem habilidades, adaptando processos seletivos e práticas para acolher profissionais com deficiência. Também exploraremos o papel da IA Generativa na melhoria da acessibilidade e como essas ferramentas estão sendo adotadas. 

Neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, conheçam Gessica Pereira, deficiente visual total e Data Science Specialist; Maristela Salle — Gerente Sr. de Dados e AI; e Luíza Tocchetto — Especialista Diversidade Inclusão; ambas atuantes no Grupo Boticário.

Para encerrar, nossa convidada Géssica compartilhará uma mensagem inspiradora para outros profissionais com deficiência que aspiram a construir uma carreira na área de dados, além de oferecer conselhos para quem deseja ser parte dessa transformação inclusiva. 

Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Caso queira, você também pode ouvir o episódio aqui no post mesmo!

Falamos no episódio

Nossa Bancada Data Hackers:

Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart. Monique Femme — Head of Community Management na Data Hackers

Referências:

Pesquisa State of Data Brazil: https://stateofdata.datahackers.com.br/ Be My Eyes : https://www.bemyeyes.com/language/portuguese-brazil Senior software engineer at Google: https://x.com/lucasradaelli Livro como ser mulher (Kate White): https://www.wook.pt/livro/como-ser-uma-mulher-com-garra-kate-white/22267713 Dicas Apple.com: https://dicasapple.com/

In Analytics and Data Science departments, we've got a pretty good sense for why investing in data is important for any organization.   But how well could you pitch your company to spend its precious resources on improving data quality or better data management practices? Could you tell that data story to the right stakeholders when it matters?   In this episode, you'll hear from The Data Whisperer, Scott Taylor, sharing his best advice and practical tips for becoming a better storyteller and getting people to take action.   What You'll Learn: Why storytelling is a key skill for anyone who works in data The importance of data management, and what that really means Practical tips and frameworks for telling an effective data story   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guest: Scott Taylor The Data Whisperer, Scott Taylor, has helped countless companies by enlightening business executives to the strategic value of master data and proper data management. He focuses on business alignment and the "strategic WHY" rather than system implementation and the "technical HOW." At MetaMeta Consulting he works with Enterprise Data Leadership teams and Innovative Tech Brands to tell their data story. Get Scott's book: Telling Your Data Story: Data Storytelling for Data Management Follow Scott on LinkedIn

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Lexi Pasi and I chat about symbolic logic in AI, building and managing data science teams, math, and the shapes of ML/AI problems.

Lexi is one of my favorites to talk to because she's so left-field yet so effectively reasonable and logical (she does have a PhD in logic...).

LinkedIn: https://www.linkedin.com/in/alexandrapasi/

One of the best applications of data science is that it allows experimentation within any organization at scale. The ability to test a new checkout feature, the color of a button, and analyze whether that improves customer experiences can be truly magical when done correctly. However, doing this at scale means that the entire organization needs to be bought into the experimentation agenda. So how do you do this and how do you make sure this becomes part of your organization’s culture? Amit Mondal is the VP & Head of Digital Analytics & Experimentation at American Express. Throughout his career Amit has been a financial services leader in digital, analytics/data science and risk management, driving digital strategies and investments, while creating a data driven & experimentation first culture for Amex. Amit currently leads a global team of 200+ Data Scientists, Statisticians, Experimenters, Analysts, and Data experts. In the episode, Adel and Amit explore the importance of experimentation at American Express, key components of experimentation strategies, ownership and coordination in experimentation processes, the pillars that feed into a culture of experimentation, frameworks for building successful experiments, robust experiment design, challenges and trends across industries and much more.  Links Mentioned in the Show: American ExpressDecoding Marketing Mix Modeling[Course] A/B Testing in PythonRelated Episode: Data & AI at Tesco with Venkat Raghavan, Director of Analytics and Science at TescoRewatch sessions from RADAR: AI Edition New to DataCamp? Learn on the go using the DataCamp mobile app Empower your business with world-class data and AI skills with DataCamp for business

I've been told that I "say the quiet parts out loud." It might be calling out "data science" and AI as overhyped, or anything else I've ranted about online over the years.

In this episode, I unpack what that means and why I do it.

Discover how to seamlessly integrate data science into your leadership toolkit with 'Data Science for Decision Makers.' This practical guide emphasizes bridging business challenges with technical data insights, enabling you to make informed decisions leveraging modern data-driven methodologies. What this Book will help me do Gain foundational knowledge of statistics and machine learning to interpret data and drive insights. Learn to plan, execute, and evaluate data science projects effectively from start to finish. Understand the differences between machine learning, statistical methods, and traditional analysis and when to employ each. Acquire tools to manage and maximize the capabilities of high-performing data teams. Develop the skills to translate business challenges into data science problems for actionable solutions. Author(s) The author, None Howells, comes with an extensive background in data science leadership and AI technologies. With years of experience in guiding organizations through implementing data science solutions, they bring clarity and practicality to tackling complex problems. Their writing aims to be an accessible resource for both technical professionals taking on managerial roles and executives looking to understand the potential of data science. Who is it for? This book is tailored for executives, such as CDOs, data managers, or business leaders, who wish to understand data science concepts and their applications. It's also valuable for managers of technical teams aiming to bridge communication gaps and improve project outcomes. If you are at the intersection of leadership and data challenges, this book provides essential context and tools to thrive.

Ready for more ideas about UX for AI and LLM applications in enterprise environments? In part 2 of my topic on UX considerations for LLMs, I explore how an LLM might be used for a fictitious use case at an insurance company—specifically, to help internal tools teams to get rapid access to primary qualitative user research. (Yes, it’s a little “meta”, and I’m also trying to nudge you with this hypothetical example—no secret!) ;-) My goal with these episodes is to share questions you might want to ask yourself such that any use of an LLM is actually contributing to a positive UX outcome  Join me as I cover the implications for design, the importance of foundational data quality, the balance between creative inspiration and factual accuracy, and the never-ending discussion of how we might handle hallucinations and errors posing as “facts”—all with a UX angle. At the end, I also share a personal story where I used an LLM to help me do some shopping for my favorite product: TRIP INSURANCE! (NOT!) 

Highlights/ Skip to:

(1:05) I introduce a hypothetical  internal LLM tool and what the goal of the tool is for the team who would use it  (5:31) Improving access to primary research findings for better UX  (10:19) What “quality data” means in a UX context (12:18) When LLM accuracy maybe doesn’t matter as much (14:03) How AI and LLMs are opening the door for fresh visioning work (15:38) Brian’s overall take on LLMs inside enterprise software as of right now (18:56) Final thoughts on UX design for LLMs, particularly in the enterprise (20:25) My inspiration for these 2 episodes—and how I had to use ChatGPT to help me complete a purchase on a website that could have integrated this capability right into their website

Quotes from Today’s Episode “If we accept that the goal of most product and user experience research is to accelerate the production of quality services, products, and experiences, the question is whether or not using an LLM for these types of questions is moving the needle in that direction at all. And secondly, are the potential downsides like hallucinations and occasional fabricated findings, is that all worth it? So, this is a design for AI problem.” - Brian T. O’Neill (8:09) “What’s in our data? Can the right people change it when the LLM is wrong? The data product managers and AI leaders reading this or listening know that the not-so-secret path to the best AI is in the foundational data that the models are trained on. But what does the word quality mean from a product standpoint and a risk reduction one, as seen from an end-users’ perspective? Somebody who’s trying to get work done? This is a different type of quality measurement.” - Brian T. O’Neill (10:40)

“When we think about fact retrieval use cases in particular, how easily can product teams—internal or otherwise—and end-users understand the confidence of responses? When responses are wrong, how easily, if at all, can users and product teams update the model’s responses? Errors in large language models may be a significant design consideration when we design probabilistic solutions, and we no longer control what exactly our products and software are going to show to users. If bad UX can include leading people down the wrong path unknowingly, then AI is kind of like the team on the other side of the tug of war that we’re playing.” - Brian T. O’Neill (11:22) “As somebody who writes a lot for my consulting business, and composes music in another, one of the hardest parts for creators can be the zero-to-one problem of getting started—the blank page—and this is a place where I think LLMs have great potential. But it also means we need to do the proper research to understand our audience, and when or where they’re doing truly generative or creative work—such that we can take a generative UX to the next level that goes beyond delivering banal and obviously derivative content.” - Brian T. O’Neill (13:31) “One thing I actually like about the hype, investment, and excitement around GenAI and LLMs in the enterprise is that there is an opportunity for organizations here to do some fresh visioning work. And this is a place that designers and user experience professionals can help data teams as we bring design into the AI space.” - Brian T. O’Neill (14:04)

“If there was ever a time to do some new visioning work, I think now is one of those times. However, we need highly skilled design leaders to help facilitate this in order for this to be effective. Part of that skill is knowing who to include in exercises like this, and my perspective, one of those people, for sure, should be somebody who understands the data science side as well, not just the engineering perspective. And as I posited in my seminar that I teach, the AI and analytical data product teams probably need a fourth member. It’s a quartet and not a trio. And that quartet includes a data expert, as well as that engineering lead.” - Brian T. O’Neill (14:38)

Links Perplexity.ai: https://perplexity.ai  Ideaflow: https://www.amazon.com/Ideaflow-Only-Business-Metric-Matters/dp/0593420586  My article that inspired this episode