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In this episode, we talk with Daniel, an astrophysicist turned machine learning engineer and AI ambassador. Daniel shares his journey bridging astronomy and data science, how he leveraged live courses and public knowledge sharing to grow his skills, and his experiences working on cutting-edge radio astronomy projects and AI deployments. He also discusses practical advice for beginners in data and astronomy, and insights on career growth through community and continuous learning.TIMECODES00:00 Lunar eclipse story and Daniel’s astronomy career04:12 Electromagnetic spectrum and MEERKAT data explained10:39 Data analysis and positional cross-correlation challenges15:25 Physics behind radio star detection and observation limits16:35 Radio astronomy’s advantage and machine learning potential20:37 Radio astronomy progress and Daniel’s ML journey26:00 Python tools and experience with ZoomCamps31:26 Intel internship and exploring LLMs41:04 Sharing progress and course projects with orchestration tools44:49 Setting up Airflow 3.0 and building data pipelines47:39 AI startups, training resources, and NVIDIA courses50:20 Student access to education, NVIDIA experience, and beginner astronomy programs57:59 Skills, projects, and career advice for beginners59:19 Starting with data science or engineering1:00:07 Course sponsorship, data tools, and learning resourcesConnect with Daniel Linkedin -   / egbodaniel   Connect 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/

​In this episode, we’re joined by Daniel Egbo, an astrophysicist turned ML engineer and AI ambassador (Arize, Tavily). Daniel will talk about the moment he decided to try data science and ML and what he transferred from astronomy.

​We’ll delve into how he selects resources, stays motivated during self-learning, and overcomes obstacles.

​We plan to cover:

• ​Why Daniel decided to try data science and ML and skills from astronomy that helped
• ​How he chooses learning resources in a noisy landscape
• ​What keeps him motivated and the first steps when he feels stuck
• ​Building a career from Africa/remotely
• ​Daniel’s experience with ML Zoomcamp

​ ​About the speaker

​Daniel Egbo is an astrophysicist turned machine learning engineer and AI ambassador (Arize, Tavily). A PhD candidate at the University of Cape Town, he builds end-to-end ML and LLM applications with a focus on reliability and learning in public. His work spans knowledge-retrieval assistants, practical evaluation, and applying data science to astronomy. ​

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