Information session to discuss Magnimind Academy's Mentor-Led Data Science Internship Program, covering program structure, real industry projects, project-based training, mock interviews, resume polish, and post-interview feedback.

Information session to discuss Magnimind Academy's Mentor-Led Data Science Internship Program.

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

AI is playing an increasingly significant role in warfare – from combating disinformation and analyzing imagery to (semi-)autonomous systems. In this keynote, we explore how this technology is transforming the battlefield and how we can use AI responsibly.

DCMR transformeerde in vijf jaar van een klein datateam naar een professionele data-organisatie. Pieter Vreeburg deelt hoe zij dit bereikten met Agile, DevOps en tooling zoals ownR – inclusief lessen, successen en uitdagingen.

To fully unlock the potential of AI within KPN, scaling is key. Therefore KPN focuses on 4 pillars: AI Literacy, Governance, end-to-end implementation with business, IT, data and AI, and the expansion of our technical infrastructure. Together, these elements support the democratization of AI capabilities across the organization. With the emergence of Generative AI—especially Agentic AI—broad enablement has become even more critical. In this session, KPN will share organizational opportunities and challenges related to AI adoption at scale, and how it utilizes Dataiku as the central Data Science platform to drive this transformation.

Session en ligne présentant les différences entre Data Analytics et Data Science & AI, afin d’aider à choisir la formation adaptée. Le rôle clé de l’IA dans les formations et comment elle est intégrée, ainsi que des informations pratiques sur le programme, les formats, le financement et les admissions.

Você já parou para pensar quais viéses seu algoritmo pode carregar e como isso impacta suas análises? Neste episódio, conversamos com Andressa Freires, fundadora da diversiData e Data Science Specialist, sobre como as perspectivas dos desenvolvedores de AIs e modelos podem transpassar no conteúdo criado por essas tecnologias. Além disso, discutimos como a falta de diversidade pode impactar as ferramentas que são amplamente utilizadas pelo mundo e as consequências desse movimento. Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. 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: https://mitsloanreview.com.br/quebrando-correntes-e-liderando-com-proposito/ https://linktr.ee/diversidata https://www.amazon.com/Unmasking-AI-Mission-Protect-Machines/dp/0593241835 https://www.amazon.com/Weapons-Math-Destruction-Increases-Inequality/dp/0553418815

The line between human work and AI capabilities is blurring in today's business environment. AI agents are now handling autonomous tasks across customer support, data management, and sales prospecting with increasing sophistication. But how do you effectively integrate these agents into your existing workflows? What's the right approach to training and evaluating AI team members? With data quality being the foundation of successful AI implementation, how can you ensure your systems have the unified context they need while maintaining proper governance and privacy controls? Karen Ng is the Head of Product at HubSpot, where she leads product strategy, design, and partnerships with the mission of helping millions of organizations grow better. Since joining in 2022, she has driven innovation across Smart CRM, Operations Hub, Breeze Intelligence, and the developer ecosystem, with a focus on unifying structured and unstructured data to make AI truly useful for businesses. Known for leading with clarity and “AI speed,” she pushes HubSpot to stay ahead of disruption and empower customers to thrive. Previously, Karen held senior product leadership roles at Common Room, Google, and Microsoft. At Common Room, she built the product and data science teams from the ground up, while at Google she directed Android’s product frameworks like Jetpack and Jetpack Compose. During more than a decade at Microsoft, she helped shape the company’s .NET strategy and launched the Roslyn compiler platform. Recognized as a Product 50 Winner and recipient of the PM Award for Technical Strategist, she also advises and invests in high-growth technology companies. In the episode, Richie and Karen explore the evolving role of AI agents in sales, marketing, and support, the distinction between chatbots, co-pilots, and autonomous agents, the importance of data quality and context, the concept of hybrid teams, the future of AI-driven business processes, and much more. Links Mentioned in the Show: Hubspot Breeze AgentsConnect with KarenWebinar: Pricing & Monetizing Your AI Products with Sam Lee, VP of Pricing Strategy & Product Operations at HubSpotRelated Episode: Enterprise AI Agents with Jun Qian, VP of Generative AI Services at OracleRewatch RADAR AI  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

If we want to run data science workloads (e.g. using Tensorflow, PyTorch, and others) in containers (for local development or production on Kubernetes), we need to build container images. Doing that with a Dockerfile is fairly straightforward, but is it the best method? In this talk, we'll take a well-known speech-to-text model (Whisper) and show various ways to run it in containers, comparing the outcomes in terms of image size and build time.

Ever tried passing a Polars Dataframe to a data science library and found that it...just works? No errors, no panics, no noticeable overhead, just...results? This is becoming increasingly common in 2025, yet only 2 years ago, it was mostly unheard of. So, what changed? A large part of the answer is: Narwhals.

Narwhals is a lightweight compatibility layer between dataframe libraries which lets your code work seamlessly across Polars, pandas, PySpark, DuckDB, and more! And it's not just a theoretical possibility: with ~30 million monthly downloads and set as a required dependency of Altair, Bokeh, Marimo, Plotly, Shiny, and more, it's clear that it's reshaping the data science landscape. By the end of the talk, you'll understand why writing generic dataframe code was such a headache (and why it isn't anymore), how Narwhals works and how its community operates, and how you can use it in your projects today. The talk will be technical yet accessible and light-hearted.

From: Chrono-Regulatory Commission, Temporal Enforcement Division To: PyData Berlin Organising Committee Subject: Citation #TMP-2077-091 - Unauthorised Spacetime Disturbance

Dear Committee, Our temporal monitoring systems have detected an unauthorised chronological anomaly emanating from your facility (Berliner Congress Center, coordinates 52.52068°N, 13.416451°E) scheduled to manifest on September 1st at 9:20 a.m.

Discussion about the topic classification, including algorithms and experiences. Afterwards there is time for other topics or questions about data science.

This week’s focus is storytelling and presentation in data science, with emphasis on communicating your project effectively.

In this episode, We talked with Pastor, a medical doctor who built a career in machine learning while studying medicine. Pastor shares how he balanced both fields, leveraged live courses and public sharing to grow his skills, and found opportunities through freelancing and mentoring.TIMECODES00:00 Pastor’s background and early programming journey06:05 Learning new tools and skills on the job while studying medicine11:44 Balancing medical studies with data science work and motivation13:48 Applying medical knowledge to data science and vice versa18:44 Starting freelance work on Upwork and overcoming language challenges24:03 Joining the machine learning engineering course and benefits of live cohorts27:41 Engaging with the course community and sharing progress publicly35:16 Using LinkedIn and social media for career growth and interview opportunities41:03 Building reputation, structuring learning, and leveraging course projects50:53 Volunteering and mentoring with DeepLearning.AI and Stanford Coding Place57:00 Managing time and staying productive while studying medicine and machine learningConnect with Pastor Twitter - https://x.com/PastorSotoB1Linkedin -   / pastorsoto  Github - https://github.com/sotoblancoWebsite - https://substack.com/@pastorsotoConnect with DataTalks.Club: Join the community - https://datatalks.club/slack.htmlSubscribe to our Google calendar to have all our events in your calendar - https://calendar.google.com/calendar/...Check other upcoming events - https://lu.ma/dtc-eventsGitHub: https://github.com/DataTalksClubLinkedIn -   / datatalks-club   Twitter -   / datatalksclub   Website - https://datatalks.club/

Présentation détaillée des formations proposées par Le Wagon: Développement Web, Data Analyse, Data Science, Growth.

In this episode, we’re diving into a fascinating intersection of cutting-edge science and data innovation. As technology continues to evolve, researchers are increasingly turning to brain organoids, (miniature, lab-grown models of the human brain) to unravel some of the most complex mysteries of neuroscience. We’re joined by three brain organoid experts: Thomas Hartung, Professor of Environmental Health and Engineering at Johns Hopkins University; Jack Van Horn, Professor of Data Science and Psychology at the University of Virginia;  and Lulu Jiang, Assistant Professor of Neuroscience, also at the University of Virginia. Together, they’ll shed light on how brain organoid technology is reshaping our understanding of the brain, and how data science is playing a crucial role in unlocking its secrets.

Chapters (00:00:51) - Brain Organizations(00:05:54) - Brain Organoids for drug discovery and immunology(00:13:53) - Alzheimer's disease in the organoid system(00:15:49) - What are the standards in the field of brain organoids?(00:22:44) - Big Data and Intelligence in the Brain(00:26:50) - Alzheimer's disease, the human brain(00:30:39) - The computational twin of the brain(00:37:23) - The quest for precision medicine in the brain(00:42:17) - The human brain in an organoid(00:43:21) - Will Brain Derived Organoids Replace Animal Models in Neurodegener

Focusing on the game Mafia, this talk explores some common methods relied upon by players to solve the game. Is the first player to reach 3 votes really mafia? Can you really find mafia off voting patterns alone? And when you apply these methods in a game, how accurate are they in finding a wolf? Inspired by a desire to prove players on their homesite that people place too much faith in these methods, examples of how these methods have been applied in past games will be unpacked, and whether these principles should really be relied upon. After all, it is a social deduction game for a reason – statistics can only get you so far.

The relationship between AI and data professionals is evolving rapidly, creating both opportunities and challenges. As companies embrace AI-first strategies and experiment with AI agents, the skills needed to thrive in data roles are fundamentally changing. Is coding knowledge still essential when AI can generate code for you? How important is domain expertise when automated tools can handle technical tasks? With data engineering and analytics engineering gaining prominence, the focus is shifting toward ensuring data quality and building reliable pipelines. But where does the human fit in this increasingly automated landscape, and how can you position yourself to thrive amid these transformations? Megan Bowers is Senior Content Manager, Digital Customer Success at Alteryx, where she develops resources for the Maveryx Community. She writes technical blogs and hosts the Alter Everything podcast, spotlighting best practices from data professionals across the industry. Before joining Alteryx, Megan worked as a data analyst at Stanley Black & Decker, where she led ETL and dashboarding projects and trained teams on Alteryx and Power BI. Her transition into data began after earning a degree in Industrial Engineering and completing a data science bootcamp. Today, she focuses on creating accessible, high-impact content that helps data practitioners grow. Her favorite topics include switching career paths after college, building a professional brand on LinkedIn, writing technical blogs people actually want to read, and best practices in Alteryx, data visualization, and data storytelling. Presented by Alteryx, Alter Everything serves as a podcast dedicated to the culture of data science and analytics, showcasing insights from industry specialists. Covering a range of subjects from the use of machine learning to various analytics career trajectories, and all that lies between, Alter Everything stands as a celebration of the critical role of data literacy in a data-driven world. In the episode, Richie and Megan explore the impact of AI on job functions, the rise of AI agents in business, and the importance of domain knowledge and process analytics in data roles. They also discuss strategies for staying updated in the fast-paced world of AI and data science, and much more. Links Mentioned in the Show: Alter EverythingConnect with MeganSkill Track: Alteryx FundamentalsRelated Episode: Scaling Enterprise Analytics with Libby Duane Adams, Chief Advocacy Officer and Co-Founder of AlteryxRewatch RADAR AI  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

Data science continues to evolve in the age of AI, but is it still the 'sexiest job of the 21st century'? While generative AI has transformed the landscape, it hasn't replaced data scientists—instead, it's created more demand for their skills. Data professionals now incorporate AI into their workflows to boost efficiency, analyze data faster, and communicate insights more effectively. But with these technological advances come questions: How should you adapt your skills to stay relevant? What's the right balance between traditional data science techniques and new AI capabilities? And as roles like analytics engineer and machine learning engineer emerge, how do you position yourself for success in this rapidly changing field? Dawn Choo is the Co-Founder of Interview Master, a platform designed to streamline technical interview preparation. With a foundation in data science, financial analysis, and product strategy, she brings a cross-disciplinary lens to building data-driven tools that improve hiring outcomes. Her career spans roles at leading tech firms, including ClassDojo, Patreon, and Instagram, where she delivered insights to support product development and user engagement. Earlier, Dawn held analytical and engineering positions at Amazon and Bank of America, focusing on business intelligence, financial modeling, and risk analysis. She began her career at Facebook as a marketing analyst and continues to be a visible figure in the data science community—offering practical guidance to job seekers navigating technical interviews and career transitions. In the episode, Richie and Dawn explore the evolving role of data scientists in the age of AI, the impact of generative AI on workflows, the importance of foundational skills, and the nuances of the hiring process in data science. They also discuss the integration of AI in products and the future of personalized AI models, and much more. Links Mentioned in the Show: Interview MasterConnect with DawnDawn’s Newsletter: Ask Data DawnGet Certified: AI Engineer for Data Scientists Associate CertificationRelated Episode: How To Get Hired As A Data Or AI Engineer with Deepak Goyal, CEO & Founder at Azurelib AcademyRewatch RADAR AI  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