Data Analytics

We will be meeting at Starbucks to learn together. Come with an online class you're already going through or an interest and we will try to connect you with a course where you can learn it. Already have a skill you want to contribute to a Kaggle Datascience competition? We will work on these too! Laptop required :)

If you’re trying to grow in marketing right now, guessing isn’t a great strategy. This event gives you direct access to how marketing leaders actually think. Join DC Marketing Tech Talks on May 19th in Tysons for a live **CMO Ask Me Anything** — a candid, open conversation with experienced marketing leaders who have built teams, driven revenue, and led at the highest levels. This is your chance to ask: * What skills actually matter right now? * What gets someone promoted vs. passed over? * What does it take to become a CMO today? * How are marketing teams and roles changing? * How do you connect brand to real business growth? Just real answers from people who have done the work. Whether you’re looking to move up, switch roles, or sharpen your edge, you’ll leave with a clearer sense of what to focus on next. As always, this event is free and open to the public thanks to our sponsors, SalesIntel. Food, drinks, networking, and a room full of marketers who care about getting better at what they do. Come with questions. Leave with direction.

**REGISTER AT THE LUMA EVENT PAGE!!!** https://luma.com/27ja5gwl Join us for an exciting talk by Mary Gibbs, Senior Applied Scientist at Relativity. ​ ​**​Agenda:** ​​6:00 - 6:30 PM - Welcome and mingle 6:30- 6:45 PM - Introductions 6:45 - 7:30 PM - Talk 7:30 - 8:00 PM - Wrap up **​​Description:** ​If you have ever shipped a model, watched your metrics improve, and later learned from your users that something was wrong, the metrics were always wrong. You just didn’t know it yet. An evaluation consists of three components, a benchmark, a scorer, and a claim about what a score represents. Each component has its own weaknesses. Benchmarks can suffer from narrow coverage, contamination, or saturation. Scorers are often chosen for ease of automation or computation rather than for their alignment with user outcomes. And the claim connecting a score to reality is rarely made explicit. These gaps compound across the model development lifecycle. When metrics improve, teams treat that as a signal and optimize directly against it, which is how a measurement problem becomes a model problem. This talk maps where evaluations can go wrong, considers counterarguments, and ends with practical advice for building better ones. **​Speaker Bio:** ​Mary is a Senior Applied Scientist at Relativity, tackling data science challenges in the e-discovery and legal tech space. She is also an organizer for Women and Gender eXpansive Coders DC (formerly Women Who Code DC), fostering a community dedicated to empowering women and nonbinary individuals to excel in their careers. Mary's experience spans various domains. She has developed data science solutions related to job search and career progression at Teal, cybersecurity challenges at LiveAction Software, and commercial and government consulting at Mosaic Data Science. Before venturing into the field of data science, Mary conducted and published research pertaining to the cellular and molecular mechanisms underlying neurodevelopment at the National Institutes of Health. In other words, she has dissected and imaged a lot of fruit fly brains. She holds a M.S. in Data Science from The George Washington University and a B.A. in Biological Sciences from Cornell University

Our first Technoverts meetup had a great turnout, so we're doing it again, this time with an optional conversation theme: **How people are actually using AI in everyday work and life**. This is not a panel or presentation, and I do not have a fixed agenda, it's just a relaxed coffee conversation for people in tech (or tech-adjacent fields) who are curious about practical AI use cases, useful tools, workflow hacks, weird experiments, and what's generally helping people right now. *Topics might include:* * AI tools people actually use at work * Productivity and automation * Prompting successes and failures * Writing, coding, research, analytics and creative use cases * What feels overhyped vs. genuinely useful. No expertise is required. It's completely fine to come mostly curious, skeptical, or just interested in meeting thoughtful people in tech. *What to expect:* * Low pressure conversation * A mix of tech talk and normal conversation Location: (Subject to change as I do more research) High Point Pub Tyson's Whole Foods, 1635 Boro Pl. (This is the cafe space on the second floor of the Whole Foods at the Boro, Tysons. Parking is free in the garage with validation). Time: 11 am (this is when the cafe opens). Everyone covers their own coffee/snacks. I'll arrive early and grab a table and will post details about location in the event discussion. Address: 1635 Boro Pl. Whole Foods 2nd Floor Cafe

PyData London 2026 brings together data scientists, engineers, and researchers from around the world for three days of insightful talks, tutorials, and community connection. Hosted in one of Europe’s leading tech hubs, the event highlights the latest developments in Python, machine learning, and data science. Attendees can expect a mix of cutting-edge research, real-world applications, and opportunities to network with industry leaders. Whether you're a beginner or an expert, PyData London offers something for every level of the data science community. **THIS IS NOT A TICKET- TICKETS MUST BE PURCHASED ON CONFERENCE WEBSITE.** https://pydata.org/london2026

**ProductTank NoVA is back!** After a brief hiatus, we are relaunching the chapter with a format that cuts out the fluff and gets straight to the reality of building. Every product feed is flooded with AI hype right now, but what does it actually look like on the ground? Instead of a polished keynote, we are hosting **0 to 1 with AI: Real Product Talks**—a "Show & Tell" featuring 3–4 lightning talks focused on the intersection of AI and product. The framing for the talks is simple: *"Here is what I'm building, and here is what I'm stuck on."* We want to look at the messy, 0-to-1 reality of integrating AI into your workflows and products. *(Want to share what you are working on? Reach out! The bar is low-pressure, just 10–12 minutes to talk about a real AI problem you are trying to solve.)* **Agenda:** * **5:30 PM - 6:00 PM:** Doors open, networking, & name tags * **6:00 PM - 6:10 PM:** Welcome + Chapter Relaunch Intro * **6:10 PM - 7:30 PM:** Lightning Talks (Three 12-min talks + 5 mins Q&A each, with a 5-min stretch break) * **7:30 PM - 8:00 PM:** Open networking * **8:00 PM:** Soft close **Location & Logistics:** Herndon Fortnightly Library (Extended Hours Meeting Room) 768 Center St, Herndon, VA 20170 * **Parking:** We'll meet at the library, located just behind the Herndon Municipal Center. There is plenty of free public parking available around the municipal center. * **Metro & Bus:** Take the Silver Line to Herndon Metro Station, then connect via Fairfax Connector Route 921 or 950 to Historic Downtown Herndon. * **Driving (via VA-267/Dulles Toll Rd):** Take Exit 10 North (Centreville Rd, which becomes Elden St). Turn left onto Station St or Lynn St. * **Driving (via VA-286/Fairfax Co Pkwy):** Take the Elden St West exit. Turn right onto Lynn St. Looking forward to seeing everyone again and getting back to building!

Join us for another session of our study group as we continue our coverage of the book Domain-Specific Small Language Models. In this session, we will attempt to cover Chapters 13 on "Creating end-to-end applications", which explores RAG and Agents and Chapter 14, on "Advanced Components for LLM Applications", which explores GraphRAG. This isn't just a lecture! Come ready to ask questions, share insights, and code along. Whether you're a beginner or have some experience, this is the perfect opportunity to continue to learn together. If you plan to work with the code on your own laptop during the session, try and download the code from here https://github.com/virtualramblas/Domain-Specific-Small-Language-Models ahead of time.