Breaking away from traditional manual video analysis, this talk introduces a data-driven approach to automatically identify football playstyles in key moments before a shot on goal , using tracking and event data. By applying network science , which studies relationships and interactions within complex systems, we objectively analyze attacking and defensive strategies. Key spatial network metrics are used to reveal diverse playstyles through clustering techniques. The session concludes with insights into the results and possible applications of these findings in football analysis.

Before StatQuest became the go-to learning companion for millions of AI and ML practitioners… Before the “BAM! Double BAM! Triple BAM!” became a teaching tool that many learners adore...

There was just one guy in a genetics lab, trying desperately to explain his data analysis to coworkers so they didn't think he was working magic.

In this deeply personal and inspiring episode, Joshua Starmer (CEO & Founder | StatQuest) shares the real story behind his rise — a journey shaped by strategy, struggle, blunt feedback, and a relentless desire to make complicated ideas simple.

What you’ll discover: 🔹How Josh went from helping colleagues in a genetics lab to becoming a renowned educator, treasuring his first 9 views and 2 subscribers as a big win. 🔹How early feedback Josh received as a kid became a quiet spark — motivating him to improve how he explained things and ultimately shaping the teaching style millions now rely on. 🔹How his method for breaking down complex topics with unique tools like his iconic BAM! help make learning lighter and less intimidating. 🔹His thoughts on AI tutors, avatars, and interactive learning and how ethics, bias, and hallucinations relate to next-gen learning.

This is more than a conversation about statistics, data science, AI, education, or YouTube. It’s the story of a researcher who never imagined starting a learning platform, yet became one of the most trusted teachers in statistics and machine learning—turning frustration into clarity, confusion into curiosity, and small beginnings into a massive global impact.

📌 If you’ve ever struggled with PCA, logistic regression, K-means clustering, neural networks, or any tricky stats and ML concepts… chances are StatQuest made it click. Now, hear from the creator himself about what goes on behind the scenes. Now you’ll finally understand how he made it click.

🔹A must-listen for: AI/ML learners, data scientists, educators, content creators, self-taught enthusiasts, and anyone who’s faced the fear of “I’m not good at explaining things.”Prepare to walk away inspired — and with a renewed belief that clarity is a superpower anyone can learn.

Here we explore the intersections of data, art, and storytelling. Our guest, Nathalie Miebach, is an internationally-recognized data artist and the School of Data Science’s inaugural Artist-in-Residence.

Using materials like reed and paper, she transforms complex datasets into woven sculptures and musical scores, inviting us to view and even hear data in new ways. Joining her is Alex Gates, assistant professor of data science at the University of Virginia research examines how patterns of connection shape creativity, innovation, and discovery.

Together, they discuss what happens when data meets art.

Chapters (00:00:01) - Data Points: When Art Meets Science(00:00:46) - Ian and Nicole: Introduction(00:06:18) - How Stories Get Made(00:09:59) - Basket Weaving Visualizing Data(00:20:33) - Wonders of the World(00:25:47) - Data and Artist Residency(00:27:50) - Breaking Habits in Creativity(00:30:06) - What is Data Science: Craftsmanship?(00:34:50) - How Art Affects Our Understanding of Data

Navigating the journey from Excel to Fabric (and back again!)

For decades, the entire business world has been built on the flexibility and power of spreadsheets. From rocket science to credit derivatives, a mind that could imagine it could implement it with nothing more than Excel formulas and cell references. But over time this has resulted in siloed, manual processes in “Shadow IT” where some of the most valuable data and logic in an organisation is locked away in these files. But it doesn’t need to be like that! The promise of Microsoft Fabric is to bring all your organisational data into a single place, in a single format where it can be extracted, shaped, enriched and secured once for use in any tool. So, from an organizational perspective, there are many benefits of such a unified data platform. However, it’s not obvious how you would take an Excel-based process (or financial model) and bring it into a Power BI and Fabric environment. Perhaps it’s overkill to even consider such a move, and it’s potentially a significant business risk if those analysts used to working in Excel haven’t yet developed the different skillsets required for Fabric. In this session we will take a financial credit risk model in Excel and go through the thought process and workflow for bringing this into Power BI/Fabric including:

• Considering how and why to use Power Query to combine and structure the data inputs into a star schema (which can be done in Excel!)
• Implementing the business logic with DAX, field parameters for increased flexibility and Translytical writeback to model scenarios directly from Power BI.
• Using visuals such as maps and dynamic scatter plots to tell a story with the data
• Breaking down the stages of data transformation/shaping into a medallion lakehouse structure in Fabric , with Row/Column level security at each layer
• Bringing the data at various layers back into Excel with this security model applies
• Querying the data model with Copilot and Data Agents to get AI powered insights

Attendees will come away with a view of how to drive “on the ground” business process transformation using Microsoft Fabric and ideas on how analysts used to Excel can think differently about the structure and re-usability of data, in order to take advantage of an enterprise level BI/Data platform.

Topic: Building an AI-Ready Data Stack: Integrating Lakehouse and Catalog for Unified Intelligence

Description: In todays AI-driven world, data fragmentation is the biggest barrier to building intelligent systems. Join VeloDB and Datastrato for an in-depth session on how modern data architectures are evolving to support unified, AI-ready analytics.

In this session, Rayner Chen (VP of Engineering, VeloDB) will explore how catalogs break down data silos and enable truly unified analytics across lakehouse environmentssharing best practices for integrating structured and streaming data into a single, high-performance stack. Then, Jerry Shao (Co-founder & CTO, Datastrato) will dive into the role of metadata catalogs as context, showing how rich metadata and governance frameworks can power and control the next generation of AI applications.

Whether youre building large-scale analytics platforms or preparing your organization for AI, this session will give you the architectural insights and practical frameworks to build a cohesive, AI-ready data stack.

• Breaking Data Silos with Catalogs: How to Build Unified Analytics (Speaker: Rayner Chen, VP of Engineering@VeloDB)
• Catalogs as Context: Using metadata to power and govern the next wave of AI development (Speaker: Jerry Shao, Co-founder and CTO@Datastrato)

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Discover More About WeCloudData

ABOUT US

WeCloudData is the leading accredited education institute in North America that focuses on Data Science, Data Engineering, DevOps, Artificial Intelligence, and Business Intelligence.

Developed by industry experts, and hiring managers, and highly recognized by our hiring partners,WeCloudDatas learning paths have helped many students make successful transitions into data and DevOps roles that fit their backgrounds and passions.WeCloudData provides a different and more practical teaching methodology, so that students not only learn the technical skills but also acquire the soft skills that will make them stand out in a work environment.

WeCloudData has also partnered with many big companies to help them adopt the latest tech in Data, AI, and DevOps. Visit our website for more information: https://weclouddata.com

In this episode, we're joined by Maven's own Chris Bruehl to unpack the 2025 data science landscape and explore what it really takes to break into the field today. If you're curious about what data scientists actually do — and how to become one — you won't want to miss this! What You'll Learn: How the data scientist role compares to other data careers The essential skills you need to land a data science job in 2025 Smart strategies to position yourself before applying   🤝 Follow Chris on LinkedIn!   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

Venue: Riverbank House, 2 Swan Ln, London EC4R 3AD

Please note:

1. 🚨🚨🚨A valid photo ID is required by building security. 🚨🚨🚨
2. This event follows the NumFOCUS Code of Conduct. Please familiarise yourself with it before attending.

If your RSVP status says "You're going" you will be able to get in. No need to show your RSVP confirmation when signing in. If you can no longer make it, please unRSVP as soon as possible. *** Code of Conduct: This event follows the NumFOCUS Code of Conduct. Please get in touch with the organisers with any questions or concerns. *** As always, there will be free food and drinks, generously provided by our host, Man Group. ***

Main Talks

1. Skrub: Machine Learning with DataFrames - Gaël Varoquaux

While data-science often talks about machine learning, much of the work lies in preparing and assembling DataFrames - a process that is highly manual. I'll introduce Skrub, a young package that eases machine learning with DataFrames. It provides a variety of tools to plug any scikit-learn-type model into complex and messy DataFrames with no manual effort. I will also discuss the exciting "DataOps" features coming in the new release, which wrap and record any data assembly or wrangling pipeline, and can apply full machine-learning workflows: applying the plan on new data, cross-validation, or tuning it to maximise prediction accuracy on a task.

2. Breaking the Black Box - How to Evaluate Your Agents... in Real Time Too! - Craig West

If you are building with LLMs, creating high quality evaluations is one of the most impactful things you can do. Without evals, it can be very difficult and time intensive to understand how different model versions might affect your use case. This talk aims to provide you a roadmap that may be simpler than you think to implement.

In this talk, we will look at the two aspects of Observability and Evaluation. Using the manual evaluating-ai-agents.com, along with its code repo, we will see that observability can be done without vendor solutions but with standard Python, either during Evaluation Driven Development or after development.

We will look at three core evaluation strategies - deterministic, human and LLM as Judge - with code examples.

⚡ Lightning Talks

1. From RNNs to Reliable Agents: Context Engineering, Roles, and What 1M-Token Windows Don’t Fix - Imamuddin Shaik
2. The Apprenticeship Pathway Into Data Science - Ruby Waller

Logistics Doors open at 6.30 pm (get there early as you'll need to sign in with building security). Talks start at 7:00 pm, with drinks afterwards from 9:00 pm at The Banker (EC4).

We have reduced capacity for this event, but there will be plenty of people to discuss data science questions with!

Please unRSVP in good time if you realise you can't make it. We're limited by building security on the number of attendees, so please free up your place for your fellow community members!

Everyone is welcome to this happy hour event - women and allies alike!

***Registration Required*** Please Register Here: https://www.supermomos.com/socials/women-ai-data-science?ref=maryamfarooq

Join Us for a Night of Connection at the Women + Allies in AI & Data Science NYC Networking Series!

We’re thrilled to invite you to the first installment of a brand-new monthly event series bringing together women across New York’s thriving AI and data science communities. Hosted in collaboration by NYAI, Supermomos, and WiMLDS, this event is designed to foster authentic connections, spark new ideas, and build lasting support systems for women in the field.

Whether you're just breaking into the industry, growing your career, or leading teams in tech, this gathering is a space for meaningful conversation, collaboration, and community. We’re expecting 50+ professionals, researchers, founders, and innovators—so come ready to meet fellow trailblazers in a relaxed and welcoming environment.

The kickoff event will take place at a cozy restaurant over drinks, food and music, with conversation prompts to help everyone connect more easily. You’ll also get to learn about and engage with the incredible communities behind this collaboration.

Spots are limited—RSVP now to secure your place, and let’s have a blast with good food, music and company! 🚀

Note: Food & drinks are on individual tabs.

Brought to you by:

New York Artificial Intelligence (NYAI): NYAI is a global community of AI builders, founders, and investors interested in learning about the latest in AI. We have been hosting events & partnering with startups & communities since 2016 to showcase the latest tools in AI, ML, data. Apply to join the NYAI community for free at NYAI.co

WiMLDS (Women in Machine Learning and Data Science): WiMLDS supports women and gender minorities who are practicing, studying or are interested in the fields of machine learning and data science, creating opportunities for members to engage in technical and professional conversations. Join our free organization for curated meetups, workshops, and presentations: https://www.meetup.com/nyc-wimlds/

DataScience and AI: in person in Darmstadt and live on PyData.TV on YouTube

Agenda 17:30 Doors open 18:00 Welcome 18:15 Boosting Time Series Accuracy: The Power of Ensemble Methods - Robert Haase (Paretos) 18:45 Break: Networking with snacks and beverages 19:45 Unleashing Confidence in SQL Development through Unit Testing - Tobias Lampert (Lotum) 20:15 Lightning Talks 20:30 Networking with snacks and beverages 21:00 End

🙋‍♂️ Questions to the presenters: Please go to here to submit questions to the speakers.

🍿 How to join remotely Use Zoom to join the meeting remotely. We use Zoom interact with our remote partiocpants. The presentations are streamed to YouTube (not Zoom). Talk #1 watch here Talk #2 watch here Please go to here to submit questions to the speakers.

⚡️ Lightning Talks Feel free to submit a proposal

How to sign up for on site It's important for us to make this meet up happen in a responsible way. We have limited seats available only. No limits to sign up remotely!

This event will be in English.

----

Talk #1 Boosting Time Series Accuracy: The Power of Ensemble Methods - Robert Haase (Paretos) This talk explores the practical application of ensemble methods in time series analysis, based on Robert’s extensive experience at Pareto. It covers various ensembling approaches, highlighting their effectiveness in different real-world scenarios. Attendees will gain insights into which methods perform best in practice, supported by behind-the-scenes examples of successful implementations. The session provides valuable strategies for improving predictive accuracy, making it ideal for anyone looking to leverage ensemble techniques in their time series projects. Robert earned both his Bachelor's and Master's degrees in Physics from the University of Heidelberg, specializing in Condensed Matter Physics and Computational Physics. During his Master's thesis in 2020, he advanced existing NLP Transformer architectures for timeseries applications. This involved Robert working extensively with uncertainty quantifications and normalizing flows. Since the beginning of 2021, he has been employed at Paretos, where the primary focus of his work lies in Timeseries Forecasting, specifically demand forecasting. Robert has a keen interest in combining traditional statistical methods with deep learning techniques.

Talk #2 Unleashing Confidence in SQL Development through Unit Testing - Tobias Lampert (Lotum) As data-driven applications grow, robust SQL development practices are crucial. This talk explores the challenges of maintaining complex SQL models in Data Warehouses and highlights the importance of unit testing in ensuring data quality. Attendees will learn how SQL unit testing validates modeling logic, prevents breaking changes, and supports faster deployment cycles. The session features Lotum’s Python-based SQL unit testing framework for BigQuery, which processes millions of daily events from mobile games. Discover how using small, static mock data simplifies testing and helps identify code errors efficiently.

Tobias Lampert is an experienced technical leader with expertise in Data Science and Data Engineering. With over 20 years of experience, he has designed and implemented data-intensive applications end-to-end, covering everything from data ingestion to deployment. He has developed solutions that generate insights from data using statistical analysis and machine learning. His passion lies in building user-friendly, high-performance, and cost-efficient data platforms. ---- Acknowledgements Also a big thank you to our partners:

• Hessian.AI, for hosting the meetup.
• PIONEERS HUB, for organising.

Contact If you have any questions or suggestions, please feel free to contact us via:

• Meetup
• Want to speak? Submit a talk here.
• Interested in hosting an event? Here's our Info-Deck & contact to the organisers!

PyTorch isn't just another framework; it's the de-facto standard for Deep Learning. Our community is dedicated to bringing together PyTorch users in London and those with a profound interest in ML and AI. This is your platform to share experiences, network, seek advice, and initiate collaborations.

This event is packed with opportunities to learn, connect, and exchange ideas and knowledge with experts from both Revolut and the data industry.

Join us for exclusive talks from Revolut speakers and our partner, PyTorch, and dive into the world of Data Science. Here’s what you can look forward to at the meet-up.

We'll have 3 amazing speakers at the event: Vadim Andronov, Deep Learning Engineer (Computer Vision) at Revolut Nikolay Falaleev, Head of AI at Sportlight Technology Vincent Moens, Applied Machine Learning Research Scientist at Meta Event Details: Date: Thursday, 21st November 2024 Time: 18:00 (GMT+4) Where: Revolut HQ Office Agenda:

• 17:30–18:00 \| Gathering
• 18:00–18:15 \| Welcome & Intro
• 18:15–18:55 \| Presentation: "End-to-end Visual Document Understanding" by Vadim Andronov + Q&A session
• 18:55–19:00 \| Refreshment Break
• 19:00–19:40 \| Presentation: "Optimising Video Pipelines for Neural Network Training with PyTorch" by Nikolay Falaleev + Q&A session
• 19:40–19:50 \| Refreshment Break
• 19:50–20:30 \| Presentation: "Breaking Free from CPU Bottlenecks: Unlocking PyTorch's Potential for Tiny Models" by Vincent Moens + Q&A session
• 20:30–21:30 \| Networking

Registration:

Fill out this form to receive your exclusive invitation!

Please note that this is a private event, and you'll need an invitation to attend. Since spots are limited. You’ll receive your invitation by email 3 days before the event. If the event is fully booked, you will be placed on a waiting list and notified if a spot becomes available.

Important: Only those who have submitted the registration form will be considered for an invitation. For security purposes, only invited guests on the list will be granted access to the premises; anyone not on the list will not be allowed entry.

Jeremy Forman joins us to open up about the hurdles– and successes that come with building data products for pharmaceutical companies. Although he’s new to Pfizer, Jeremy has years of experience leading data teams at organizations like Seagen and the Bill and Melinda Gates Foundation. He currently serves in a more specialized role in Pfizer’s R&D department, building AI and analytical data products for scientists and researchers. .

Jeremy gave us a good luck at his team makeup, and in particular, how his data product analysts and UX designers work with pharmaceutical scientists and domain experts to build data-driven solutions..  We talked a good deal about how and when UX design plays a role in Pfizer’s data products, including a GenAI-based application they recently launched internally.  

Highlights/ Skip to:

(1:26) Jeremy's background in analytics and transition into working for Pfizer (2:42) Building an effective AI analytics and data team for pharma R&D (5:20) How Pfizer finds data products managers (8:03) Jeremy's philosophy behind building data products and how he adapts it to Pfizer (12:32) The moment Jeremy heard a Pfizer end-user use product management research language and why it mattered (13:55) How Jeremy's technical team members work with UX designers (18:00) The challenges that come with producing data products in the medical field (23:02) How to justify spending the budget on UX design for data products (24:59) The results we've seen having UX design work on AI / GenAI products (25:53) What Jeremy learned at the  Bill & Melinda Gates Foundation with regards to UX and its impact on him now (28:22) Managing the "rough dance" between data science and UX (33:22) Breaking down Jeremy's GenAI application demo from CDIOQ (36:02) What would Jeremy prioritize right now if his team got additional funding (38:48) Advice Jeremy would have given himself 10 years ago (40:46) Where you can find more from Jeremy

Quotes from Today’s Episode

“We have stream-aligned squads focused on specific areas such as regulatory, safety and quality, or oncology research. That’s so we can create functional career pathing and limit context switching and fragmentation. They can become experts in their particular area and build a culture within that small team. It’s difficult to build good [pharma] data products. You need to understand the domain you’re supporting. You can’t take somebody with a financial background and put them in an Omics situation. It just doesn’t work. And we have a lot of the scars, and the failures to prove that.” - Jeremy Forman (4:12) “You have to have the product mindset to deliver the value and the promise of AI data analytics. I think small, independent, autonomous, empowered squads with a product leader is the only way that you can iterate fast enough with [pharma data products].” - Jeremy Forman (8:46) “The biggest challenge is when we say data products. It means a lot of different things to a lot of different people, and it’s difficult to articulate what a data product is. Is it a view in a database? Is it a table? Is it a query? We’re all talking about it in different terms, and nobody’s actually delivering data products.” - Jeremy Forman (10:53) “I think when we’re talking about [data products] there’s some type of data asset that has value to an end-user, versus a report or an algorithm. I think it’s even hard for UX people to really understand how to think about an actual data product. I think it’s hard for people to conceptualize, how do we do design around that? It’s one of the areas I think I’ve seen the biggest challenges, and I think some of the areas we’ve learned the most. If you build a data product, it’s not accurate, and people are getting results that are incomplete… people will abandon it quickly.” - Jeremy Forman (15:56) “ I think that UX design and AI development or data science work is a magical partnership, but they often don’t know how to work with each other. That’s been a challenge, but I think investing in that has been critical to us. Even though we’ve had struggles… I think we’ve also done a good job of understanding the [user] experience and impact that we want to have. The prototype we shared [at CDIOQ] is driven by user experience and trying to get information in the hands of the research organization to understand some portfolio types of decisions that have been made in the past. And it’s been really successful.” - Jeremy Forman (24:59) “If you’re having technology conversations with your business users, and you’re focused only the technology output, you’re just building reports. [After adopting If we’re having technology conversations with our business users and only focused on the technology output, we’re just building reports. [After we adopted  a human-centered design approach], it was talking [with end-users] about outcomes, value, and adoption. Having that resource transformed the conversation, and I felt like our quality went up. I felt like our output went down, but our impact went up. [End-users] loved the tools, and that wasn’t what was happening before… I credit a lot of that to the human-centered design team.” - Jeremy Forman (26:39) “When you’re thinking about automation through machine learning or building algorithms for [clinical trial analysis], it becomes a harder dance between data scientists and human-centered design. I think there’s a lack of appreciation and understanding of what UX can do. Human-centered design is an empathy-driven understanding of users’ experience, their work, their workflow, and the challenges they have. I don’t think there’s an appreciation of that skill set.” - Jeremy Forman (29:20) “Are people excited about it? Is there value? Are we hearing positive things? Do they want us to continue? That’s really how I’ve been judging success. Is it saving people time, and do they want to continue to use it? They want to continue to invest in it. They want to take their time as end-users, to help with testing, helping to refine it. Those are the indicators. We’re not generating revenue, so what does the adoption look like? Are people excited about it? Are they telling friends? Do they want more? When I hear that the ten people [who were initial users] are happy and that they think it should be rolled out to the whole broader audience, I think that’s a good sign.” - Jeremy Forman (35:19)

Links Referenced LinkedIn: https://www.linkedin.com/in/jeremy-forman-6b982710/

This month, Charlottesville Data Science is convening a panel discussion of data science managers and leaders to share their perspectives and advice on what it takes to get your first (or next!) job in data science or machine learning. We'll be gathering in person at Vault Virginia on the Downtown Mall.

Our panelists will include:

• Andrew Fast, VP of Technology at DataShapes, previously a chief data scientist
• Kimberly Scott, Senior Technical Director at Pixxel, previously a VP of data science
• Melissa Phillips, software engineering manager and data scientist at GA-CCRi
• Samantha Toet, data management specialist at the Virginia Equity Center
• Patrick Harrison, data science consultant, previously Director of AI engineering at S&P Global

We look forward to seeing you there!

How to find us 
Please enter the building using the side door on 3rd Street SE, right across 3rd Street from the Front Porch Music School, then take the stairs or elevator to the first floor. We'll be gathering in the Great Hall and Gallery area.

Join PyData NYC at 11 Times Square (Microsoft) on August 14th at 6:30 pm for a tutorial night with Yujian Tang (CEO of OSS4Al) and Zain Hasan (Senior ML Developer Relations Engineer at Weaviate). Please bring your 💻 to code and sign up with your government official name.

🍕 Pizza, drinks & venue sponsored by Microsoft Reactor - thank you!

Agenda: LLM Based Applications - Building Agentic RAG Workshop Speaker: Yujian Tang, CEO of OSS4Al

Yujian Tang started developing software professionally at the age of 16. In college, he studied computer science, neuroscience, and statistics and published machine learning papers to conferences like lEEE Big Data. After graduation, he worked on the AutoML system at Amazon before moving on to build his own companies including a data aggregation app, an NLP API, and his current company - OSS4Al, an organization aimed at providing all developers access to the resources to understand, use, and contribute to the direction and development of Al.

Scaling Vector Search in Production Without Breaking the Bank: Quantization and Adaptive Retrieval Speaker: Zain Hasan, Senior ML Developer Relations Engineer at Weaviate

Everybody loves vector search and enterprises now see its value thanks to the popularity of LLMs and RAG. The problem is that prod-level deployment of vector search requires boatloads of CPU, for search, and GPU, for inference, compute. The bottom line is that if deployed incorrectly vector search can be prohibitively expensive compared to classical alternatives.

The solution: quantizing vectors, leveraging hardware-accelerated optimizations and performing adaptive retrieval. These techniques allow you to scale applications into production by allowing you to balance and tune memory costs, latency performance, and retrieval accuracy very reliably.

I’ll talk about how you can perform real-time billion-scale vector searches on your laptop! This includes covering different quantization techniques, including product, binary, scalar and matryoshka quantization that can be used to compress vectors trading off memory requirements for accuracy. I’ll also introduce the concept of adaptive retrieval where you first perform cheap hardware-optimized low-accuracy search to identify retrieval candidates using compressed vectors followed by a slower, higher-accuracy search to rescore and correct. When used with well-thought-out adaptive retrieval, these quantization techniques can lead to a 32x reduction in memory cost requirements at the cost of \~ 5% loss in retrieval recall in your RAG stack.

Zain Hasan is a senior ML developer relations engineer at Weaviate. An engineer and data scientist by training, he pursued his undergraduate and graduate work at the University of Toronto building artificially intelligent assistive technologies, then founded his company, VinciLabs in the digital health-tech space. More recently he practiced as a consultant senior data scientist in Toronto. Zain is passionate about the fields of machine learning, education, and public speaking.

Networking Connect with fellow data enthusiasts, professionals, and community leaders. Build meaningful connections and forge collaborations.

---------------------------------------------------------------- RSVP is required; please note that walk-ins will not be accepted. Note: Per building policy, RSVPs will close at 12 pm on Aug 12th. Doors open @ 6 pm Doors close @ 7 pm Event @ 6:30 - 8:30 pm Venue provided by MSFT: 11 Times Square ----------------------------------------------------------------

The building requires a government-issued photo ID for entrance. This, and all PyData NYC events, is an all-level event. Newcomers and beginners are welcome.This and all NumFOCUS-affiliated events and spaces, both in-person and online, are governed by a Code of Conduct. ---------------------------------------------------------------- This event may be recorded.

This month, PyData Pittsburgh is convening a panel discussion of data science managers and leaders to share their perspectives and advice on what it takes to get your first (or next!) job in data science or machine learning. We'll be gathering in person at Benedum Hall at the University of Pittsburgh.

Our panelists will include:

• Marie Skoczylas, data scientist and analytics team leader at Highmark Health
• Ilya Goldin, head of data science at Phenom
• Summer Rae, previously director of data science and machine learning at Govini
• Patrick Harrison, data science consultant, previously director of AI engineering at S&P Global

We look forward to seeing you there!

Getting to Benedum Hall

We'll be gathering in room 157 in Benedum Hall at the University of Pittsburgh. Room 157 is located just off of the Benedum Hall courtyard through a set of double glass doors. You can access the courtyard via stairways from either O'Hara Street or Thackery Avenue. See this image for more information. This Google Maps link also shows the approximate location of the room.

If you're traveling by car, convenient parking is available at the Soldiers and Sailors Parking Garage for a flat rate of $5. Street parking is also free throughout the area after 6pm.

The University of Pittsburgh is served by many PRT bus routes. Any bus stop along Fifth Avenue between Bigelow Boulevard and Meyran Avenue will get you reasonably close.

Help us spread the word!

Share posts about the Breaking Into Data Science panel discussion on your favorite social media platforms:

• LinkedIn: https://www.linkedin.com/feed/update/urn:li:activity:7171216243662798849/
• X/Twitter: https://twitter.com/PyDataPGH/status/1765452331015880773
• Mastodon: https://fosstodon.org/@PyDataPGH/112050301290691328
• Meetup URL: https://www.meetup.com/pydata-pittsburgh/events/297050641/

Dear PyLadies 💚🐍

Our next on-site event is coming on 24th January featuring three excellent speakers:

🌟 Léa Longepierre (Kiro)

talk title: Leveraging machine learning for early detection of chronic kidney disease

🌟 Lea Bourel & Chiara Biscaro (Botify)

Talk title: Understanding SEO Split Testing: Optimizing for Search Engines

🌟Agenda (preliminary)

18h45 - 19h00 Come and take your seat

19h00 - 19h15 Welcome by PyLadies Paris and Kiro

19h15 - 19h45 Talk by Léa Longepierre

19h45 - 20h15 Talk by Lea Bourel & Chiara Biscaro

20h15 - 21h15 Cocktail, networking

🌟 Léa Longepierre (Kiro) Talk title: Leveraging machine learning for early detection of chronic kidney disease Abstract: Chronic kidney disease (CKD) is a condition characterized by a gradual degradation of kidney function over time. CKD is a multi-staged condition, which ultimately progresses to end-stage kidney failure, fatal without artificial filtering (dialysis) or a kidney transplant. Effective strategies are available to slow the progression of irreversible kidney damage, making early detection - and subsequent treatment of CKD - paramount to delay or prevent many associated complications. In this talk, we will show how the data science team at Kiro tackled this challenge, leveraging machine learning to predict CKD at least one year before onset. About Léa: Léa has been a Data Scientist at Kiro for the past 3 years. She graduated from ISUP in biostatistics and holds a PhD in statistics from Sorbonne Université, where she studied random graphs and more precisely, the maximum likelihood estimation in dynamic or spatial stochastic block models. At Kiro, she developed a unique expertise in medical biology data, predictive biomarkers, and recommender systems.

🌟 Lea Bourel & Chiara Biscaro (Botify)

Talk title: Understanding SEO Split Testing: Optimizing for Search Engines Abstract: This talk is your guide to SEO (search engine optimization) split testing, breaking down the basics without the jargon. Learn how we tweak websites not for people, but for search engine bots. We'll discuss the essentials, like how many pages and clicks you need for meaningful tests. Plus, we'll dive into how we divide our audience into two groups, exploring the simple yet crucial ways we optimize for both users and crawling bots. Get ready for a straightforward exploration of SEO split testing and how it shapes your website's performance in search results.

About Lea: She holds a bachelor's degree in economics and a master's degree in applied statistics for economics. She has worked as a Data Analyst at Botify for over two years About Chiara: she has worked as Senior Data Scientist at Botify since 2021. She was formerly an astrophysics researcher who studied Supernovae explosions, and she switched to Data Science in 2016. Her favourite area of Machine Learning is recommender systems

**Kiro** will be our host and sponsor of the food and the drinks during the networking session after the talks: thank you 💚

Important info

1:❗For safety reasons, the venue's staff will check everyone's identity on site. 📝Please remember to bring an ID with you and register for the event with your real name and family name. Thank you!

2: Please be on time. We can’t guarantee a seat once the meetup has started

# 🔍 FAQ

Q. I'm not female, is it ok for me to attend?

A. Yes, PyLadies Paris events are open to everyone at all levels.

Spend Tuesday evening discussing More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech by Meredith Broussard.

Description: When technology reinforces inequality, it's not just a glitch—it's a signal that we need to redesign our systems to create a more equitable world. The word “glitch” implies an incidental error, as easy to patch up as it is to identify. But what if racism, sexism, and ableism aren't just bugs in mostly functional machinery—what if they're coded into the system itself? In the vein of heavy hitters such as Safiya Umoja Noble, Cathy O'Neil, and Ruha Benjamin, Meredith Broussard demonstrates in More Than a Glitch how neutrality in tech is a myth and why algorithms need to be held accountable. Broussard, a data scientist and one of the few Black female researchers in artificial intelligence, masterfully synthesizes concepts from computer science and sociology. She explores a range of examples: from facial recognition technology trained only to recognize lighter skin tones, to mortgage-approval algorithms that encourage discriminatory lending, to the dangerous feedback loops that arise when medical diagnostic algorithms are trained on insufficiently diverse data. Even when such technologies are designed with good intentions, Broussard shows, fallible humans develop programs that can result in devastating consequences. Broussard argues that the solution isn't to make omnipresent tech more inclusive, but to root out the algorithms that target certain demographics as “other” to begin with. With sweeping implications for fields ranging from jurisprudence to medicine, the ground-breaking insights of More Than a Glitch are essential reading for anyone invested in building a more equitable future.

This is an event with R-Ladies DC! Feel free to RSVP to either meetup event.

Spend Tuesday evening discussing More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech by Meredith Broussard.

Description:

When technology reinforces inequality, it's not just a glitch—it's a signal that we need to redesign our systems to create a more equitable world.

The word “glitch” implies an incidental error, as easy to patch up as it is to identify. But what if racism, sexism, and ableism aren't just bugs in mostly functional machinery—what if they're coded into the system itself? In the vein of heavy hitters such as Safiya Umoja Noble, Cathy O'Neil, and Ruha Benjamin, Meredith Broussard demonstrates in More Than a Glitch how neutrality in tech is a myth and why algorithms need to be held accountable.

Broussard, a data scientist and one of the few Black female researchers in artificial intelligence, masterfully synthesizes concepts from computer science and sociology. She explores a range of examples: from facial recognition technology trained only to recognize lighter skin tones, to mortgage-approval algorithms that encourage discriminatory lending, to the dangerous feedback loops that arise when medical diagnostic algorithms are trained on insufficiently diverse data. Even when such technologies are designed with good intentions, Broussard shows, fallible humans develop programs that can result in devastating consequences.

Broussard argues that the solution isn't to make omnipresent tech more inclusive, but to root out the algorithms that target certain demographics as “other” to begin with. With sweeping implications for fields ranging from jurisprudence to medicine, the ground-breaking insights of More Than a Glitch are essential reading for anyone invested in building a more equitable future.

Join us for the ultimate experience with Data Fashion and Innovation! Hosted by Abby Harris Special Guest speakers, Casey Golden, CEO of Luxlock, Ko Yang Wang from Nplifier, and MetaBurnett (web 3fashion tech) Emily Burnett, will be joining us on a panel to share their insights and expertise.

During this event, we will test the platforms of Nplifier and Luxlock firsthand, witnessing how they empower brands and users in the digital economy while supporting high-touch luxury business models.

Moreover, MetaBurnett will showcase their groundbreaking all-in-one platform that merges Web3 and physical design, specifically designed to serve the fashion industry. This interactive event presents a unique opportunity to explore, experience, and even challenge these innovative technologies as we push the boundaries of innovation. Don't miss out on this exciting and interactive event!

Casey Golden, CEO, Luxlock A futurist that’s no stranger to the reality of retail. Casey is a 2x retail technology Founder, startup Advisor, Speaker, and advocate for Women in Technology. She’s as an experienced operator in both fashion and enterprise supply chain technology. A career she’s committed to digital enablement in the luxury sector as a personalization innovator and experience economy evangelist. Founder of https://www.luxlock.com/, a consumer-centric commerce experience platform that merges digital and physical shopping experiences into elevated brand experiences. Focused on digital transformation initiatives specifically designed to serve premium and luxury brands and business methodology to distinguish luxury from the masses. A disruptive industry thought leader advocating for a more humanized internet by https://www.modernretail.co/retailers/retailers-are-expanding-online-commissions-beyond-influencers/ to scale on-demand personalization without sacrificing consumer privacy or denying luxury brands their foundational DNA.

Ko-Yang Wang An entrepreneur, an author, and a frequent keynote speaker on FinTech, AI, Web3, Blockchain, and digital transformation. He is the founder of Nplifier.com - a platform for self-sovereign identity, credentials and privacy-respecting marketing. He was a founder of Fusion$360 and UFI.ai, the founding chairman of the Taiwan Fintech Association, an executive advisor of the Taiwan Blockchain Alliance, EVP of the Institute for Information Industry (a government think tank), and an advisor to six government ministries in Taiwan. He is an expert in fusing emerging technologies innovatively to drive digital innovations. Prior to his services in Taiwan, he was the Practice leader/partner for BPM Practice and CTO for four practices in IBM Global Business Services and the Research & Innovation Executive for the Business Transformation Executive of IBM Global Services in the USA. He was named 2016 CS Outstanding Alumni, Purdue University, and 2017 College of Science Outstanding Alumni, National Tsing Hua University. He was awarded an IBM Distinguished Engineer in 2000, an IBM Academy Member in 2009, a Fellow of the Chinese Society for the Management of Technology in 2017, and 2019 Smart City Outstanding Contribution Award for his contributions to Smart Commerce and FinTech in Taiwan.

Emily Burnett is a seasoned Creative Director and CEO in luxury fashion and technology, recognized across both industries for her creative talent and cutting-edge innovation. Emily launched MetaBurnett, an all-in-one platform merging Web3 and physical design, to serve the fashion industry. MetaBurnett creates new consumer experiences for brands to drive growth, increase revenue, and open new retail channels by providing digital solutions and strategies for fashion brands, retailers, and marketers of all sizes, across all categories within the $300BN global fashion industry. MetaBurnett’s mission is to serve the fashion community by guiding brands to become more profitable, by building transparent & traceable supply chains, and by navigating a path to success in generating new sales distribution networks in the digital-first era by merging both Web2 and Web3 Worlds, making it simple for the user to interact with a decentralized model. "When we think about the impact that fashion and technology can make on the world, we recognize its powerful role in breaking barriers, creating unity, and being a positive platform for change" -Emily Burnett, Founder, and Creative Director.