talk-data.com

Join R-Ladies Ottawa for a casual evening of programming on Tuesday, January 27th. We'll be participating in TidyTuesday, a weekly data visualization challenge organized by the R for Data Science community.

What is TidyTuesday?

Every week, a new dataset is posted online on the TidyTuesday GitHub repo, and folks from around the world create data visualizations using the dataset. It's an opportunity to put your programming skills into practice using real-world data in a way that's fun! It's also a great way for everyone to learn from each other, by sharing their visualizations and code.

What will the dataset be?

Even we don't know that (yet)! We'll have to wait until the day before the event to know what data we'll be working with. If you're interested in seeing some past datasets, take a look at the examples below, or visit the TidyTuesday GitHub repo to see all of the datasets dating back to 2018.

Examples from past TidyTuesdays:

• Donuts, Data, and D'oh - A Deep Dive into The Simpsons
• Pokemon
• What have we been watching on Netflix?

Do I have to use R?

No! You can use any programming language or visualization software that you want. In fact, Python users from around the globe participate in "TyDyTuesday" on a weekly basis.

Who is this event for?

No previous programming experience is required to participate, and we'll have experienced programmers in the room who can help you get started (or unstuck), if needed.

...But if you want to get the most out of the event, a good way to prepare is to watch the recording of the introduction to data visualization workshop we hosted back in 2024. :)

What should I bring?

• Please bring a laptop so you can code along. We recommend that you have RStudio or another IDE (such as VS Code or Positron) installed ahead of time, but we can help you get one installed if needed!
• Come ready to learn, share, and contribute to a safe and welcoming community!

How will this event work?

• First few minutes of the event: Introductions, and taking a look at the dataset together as a group.
• Time to create a data visualization using the language or software of your choice, either on your own or with a (new) friend! Grab a free snack while you're at it :)
• Last \~30 minutes of the event: Show and tell session for anyone who would like to share their creation with the group.

What else do I need to know?

This event (like all R-Ladies events) is totally FREE to attend.

The event will take place at Bayview Yards, which is located just a few steps away from the Bayview O-Train station. There is also a free parking lot available for those who are driving. You can find us in the "Training Room", which is on the second floor of the Bayview Yards building.

This is an in-person event with limited space! Please only RSVP if you are able to attend in-person!

***Please note that the mission of R-Ladies is to increase gender diversity in the R community. This event is intended to provide a safe space for women and gender minorities. We ask for male allies to be invited by and accompanied by a woman or gender minority.***

We’re grateful to be part of the Bayview Meetups initiative and extend our thanks to Bayview Yards for generously providing the venue space.

In this episode, Conor and Ben chat about solving advent of code problems in C++ and BQN. Link to Episode 215 on WebsiteDiscuss this episode, leave a comment, or ask a question (on GitHub)Socials ADSP: The Podcast: TwitterConor Hoekstra: Twitter | BlueSky | MastodonBen Deane: Twitter | BlueSkyShow Notes Date Generated: 2024-12-16 Date Released: 2025-01-03 Advent of Code 2024AoC 2024 Day 5Ben's C++ SolutionConor's BQN SolutionConor's AoC Video PlaylistBQNC++ std::multimapC++20 std::ranges::is_sortedC++20 std::ranges::sortAlgorithms as a Tool of Thought // Conor Hoekstra // APL Seeds '21C++23 std::views::enumeratePython enumerateScala zipWithIndexComposition Intuition II - Conor Hoekstra - CppNorth 2024Intro Song Info Miss You by Sarah Jansen https://soundcloud.com/sarahjansenmusic Creative Commons — Attribution 3.0 Unported — CC BY 3.0 Free Download / Stream: http://bit.ly/l-miss-you Music promoted by Audio Library https://youtu.be/iYYxnasvfx8

Brought to you by: • Paragon: ​​Build native, customer-facing SaaS integrations 7x faster. • WorkOS: For B2B leaders building enterprise SaaS — On today’s episode of The Pragmatic Engineer, I’m joined by Quinn Slack, CEO and co-founder of Sourcegraph, a leading code search and intelligence platform. Quinn holds a degree in Computer Science from Stanford and is deeply passionate about coding: to the point that he still codes every day! He also serves on the board of Hack Club, a national nonprofit dedicated to bringing coding clubs to high schools nationwide. In this insightful conversation, we discuss:             • How Sourcegraph's operations have evolved since 2021 • Why more software engineers should focus on delivering business value • Why Quinn continues to code every day, even as a CEO • Practical AI and LLM use cases and a phased approach to their adoption • The story behind Job Fairs at Sourcegraph and why it’s no longer in use • Quinn’s leadership style and his focus on customers and product excellence • The shift from location-independent pay to zone-based pay at Sourcegraph • And much more! — Where to find Quinn Slack: • X: https://x.com/sqs • LinkedIn: https://www.linkedin.com/in/quinnslack/ • Website: https://slack.org/ Where to find Gergely: • Newsletter: https://www.pragmaticengineer.com/ • YouTube: https://www.youtube.com/c/mrgergelyorosz • LinkedIn: https://www.linkedin.com/in/gergelyorosz/ • X: https://x.com/GergelyOrosz — In this episode, we cover: (01:35) How Sourcegraph started and how it has evolved over the past 11 years (04:14) How scale-ups have changed  (08:27) Learnings from 2021 and how Sourcegraph’s operations have streamlined (15:22) Why Quinn is for gradual increases in automation and other thoughts on AI (18:10) The importance of changelogs (19:14) Keeping AI accountable and possible future use cases  (22:29) Current limitations of AI (25:08) Why early adopters of AI coding tools have an advantage  (27:38) Why AI is not yet capable of understanding existing codebases  (31:53) Changes at Sourcegraph since the deep dive on The Pragmatic Engineer blog (40:14) The importance of transparency and understanding the different forms of compensation (40:22) Why Sourcegraph shifted to zone-based pay (47:15) The journey from engineer to CEO (53:28) A comparison of a typical week 11 years ago vs. now (59:20) Rapid fire round The Pragmatic Engineer deepdives relevant for this episode: • Inside Sourcegraph’s engineering culture: Part 1 https://newsletter.pragmaticengineer.com/p/inside-sourcegraphs-engineering-culture• Inside Sourcegraph’s engineering culture: Part 2 https://newsletter.pragmaticengineer.com/p/inside-sourcegraphs-engineering-culture-part-2 — References and Transcript: See the transcript and other references from the episode at https://newsletter.pragmaticengineer.com/podcast — Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].

Get full access to The Pragmatic Engineer at newsletter.pragmaticengineer.com/subscribe

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)

VS Code Day is our annual event where you'll learn how to elevate your development workflow with the latest and greatest features of VS Code. This year, we’re excited to delve into AI and you’ll hear from the VS Code team and other industry experts on topics like AI-powered programming with GitHub Copilot, building and deploying generative AI apps to the cloud, enhancing the C# development experience, and more. Whether you’re just starting out or you’re an experienced developer, join us on April 24, 2024 for a day focused on the editor that lets you code anything, cross-platform and free!

SESSIONS

• Welcome to VS Code Day 2024 with Gwyneth Peña-Siguenza and Reynald Adolphe
• View Source: What gets into VS Code and why with Burke Holland
• JavaScript developers: Build fast and have fun with VS Code and Azure AI CLI with Natalia Venditto
• Generating Synthetic Datasets with GitHub Copilot with Alfredo Deza
• Real World Development with VS Code and C# with Scott Hanselman and Leslie Richardson
• Building a RAG-powered AI chat app with Python and VS Code with Pamela Fox
• Beyond the Editor: Tips to get the Most out of GitHub Copilot with Kedasha Kerr
• LangChain Examples with Azure OpenAI Service + VS Code with Rishab Kumar
• AI Made Clear: Practical AI Coding Sessions in VS Code with Bruno Capuano
• Asking Copilot about your workspace with Matt Bierner

Pre-requisites: Resources we think you may find useful related to VS Code Day 2024:

• LinkedIn Clone Repo - https://aka.ms/linkedin-clone
• VS Code Day Cloud Skills Challenge - https://aka.ms/VSCodeDayChallenge