Meet Habeeb, from Electrical engineering in Nigeria to Machine learning engineer at Thomson Reuters Lab in London.

"In this episode of the Data Queries podcast, hosts William and Ama engage with MLOps expert, Prince Caduma. Prince takes us through his personal journey into the field of Machine Learning operations, sharing his early fascination with software's global impact and how a youthful determination to learn English allowed him to study in India and access world-class tech knowledge. He discusses the crucial role of ML Ops in deploying models into production from the typically isolated data science environment of Jupyter notebooks. Drawing on his extensive experience and vivid anecdotes, Prince breaks down the essence and significance of ML Ops in machine learning, illustrating his own mishap as a Freelance Data Scientist losing a critical JSON file, resulting in losing the client and discovering Neptune Ai. This episode promises insightful and engaging conversation around ML Ops, perfect for data scientists, MLOPs, and enthusiasts alike." Connect with Prince Twitter: https://twitter.com/CanumaGdt LinkedIn: https://www.linkedin.com/in/prince-canuma-05814b121/ Medium: https://prince-canuma.medium.com/ Articles, talks and podcast: What is MLOPs by Prince: https://neptune.ai/blog/mlops Lightening talk at MLOPs: https://t.co/LlMuFfLXXM Implementing MLOPs at a reasonable scale: https://t.co/lTtbebDt2h Recommended Resources: Community MLOPs community: https://mlops.community/meetups/ Courses Machine Learning Engineering for Production (MLOps) Specialization by Andrew Ng : https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops Books Deep Learning with Python by François Chollet: https://www.amazon.co.uk/Deep-Learning-Python-Francois-Chollet/dp/1617294438 Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples by Andrew P. McMahon: https://www.amazon.co.uk/Machine-Learning-Engineering-Python-production-ebook/dp/B09CHHK2RJ Designing Machine Learning Systems By Chip Huyen: https://www.amazon.com/Designing-Machine-Learning-Systems-Production-Ready/dp/1098107969 Machine Learning Design Patterns By Valliappa Lakshmanan, Sara Robinson, Michael Munn: https://learning.oreilly.com/library/view/machine-learning-design/9781098115777/

In today's episode, Ama and William have a beautiful conversation with Leonidas Efraim to uncover this beautiful accidental story of becoming a data engineer and how networks can be helpful in getting your dream data role in the tech industry. Leo shares his story from his education in Greece to his present role as a Data Engineer at a multinational Fintech like Mastercard and a Data scientist at Imperial College London. Leo shares his thoughts on projects and tools which can help you get a feel of the day-to-day of a Data engineer and the different processes involved in building data pipelines in a production environment. Connect with Leo on LinkedIn: https://www.linkedin.com/in/leonidas-efraim/

In this episode, we continue our conversation with the Talented Tambe Tabitha who is a Data Analyst at Social Finance in their London-based office. For the first Part of this episode you can refer to the previous episode. In this episode Tabitha shares her thoughts and insights on early careers tips and how to approach your career with humility and with a learners mindset. We also talk about technical tools and languages, the difficulties she faced trying to learn a new programming language for frontend development with deep experience as a backend developer building data tools, and how Javascript is a versatile tool. In was interesting to learn how Tabitha approaches new generative coding tools such as co-pilot and ChatGPT, and how to approach it as a learner.  As always we had our controversial or contrarian thought question and Tabitha played it well being diplomatic. Acknowledgements to her parents for her career and exposure.  Tabithat's recommended earning resources  DataCamp : https://www.datacamp.com/ Udacity : https://www.udacity.com/ ALX: https://nanodegree.alxafrica.com/ CS50: https://www.youtube.com/@cs50 Tabithat's read book recommendation. The Bible  The Law of Success by Napoleon Hill I'm ok your Ok by Thomas Harry  Automate the boring stuff with pthon by  Al Swegart Python Data Science Handbook by Jake VanderPlas      Bonus  Python Cookbook by David Beazley & Brain K. Jones      books to explore. Deep Work by Cal Newport Range by David Epstien It was great time having Tabitha with us and the invaluable lesson that Tabitha shared with us, we hope would help you in your next career move . As always, thank you for joining us. Keep Querying on...! Reach out to Tambe Tabitha Tambe’s talk at PyData London: https://rb.gy/eey0hk Twitter: https://twitter.com/TambeAchere LinkedIn: https://www.linkedin.com/in/tambe-tabitha-achere/ Vacancies at Social Finance: https://www.socialfinance.org.uk/careers Stream or Share on other platforms: Spotify: https://rb.gy/eey0hk Google Podcast: https://rb.gy/eey0hk Apple podcast: https://rb.gy/h0mz3x

In today’s episode, it was a pleasure to sit down with the Talented Tambe Tabitha who is a Data Analyst at Social Finance in their London-based office. Our conversation is divided into two parts.  First, we talk about her journey into the field of Data Science and we explore a highly impactful project that Tabitha worked on - building a validator tool for looked-after children. She shares her experience working in multifunctional teams and the rewards of working as a consultant with so many things to learn and explore, some of her favorite technologies and tech stacks.   In the second part of the episode, we shift gear and focus on the invaluable lesson that Tabitha has learned from her short but impressive career thus far. As always, thank you for joining us. Now let’s query on! Reach out to Tambe Tabitha Tambe’s talk at PyData London: https://rb.gy/eey0hk Twitter: https://twitter.com/TambeAchere LinkedIn: https://www.linkedin.com/in/tambe-tabitha-achere/ Vacancies at Social Finance: https://www.socialfinance.org.uk/careers Stream or Share on other platforms: Spotify: https://rb.gy/eey0hk Google Podcast: https://rb.gy/eey0hk Apple podcast: https://rb.gy/h0mz3x

In today's episode William and Ama have a deep insightful 33 minutes chat with Dr. Leila Ghanbar who is a lecturer at Brunel University London to explore research in academia and explore her path into the field of Data science. She deconstructs some of the myths people have about Machine learning and shares her fear and challenges she faced even as a lecturer and how she copes with them.  Connect with Leila on LinkedIn: https://uk.linkedin.com/in/leila-ghanbar-730192131 Recommended learning resources: https://www.datacamp.com/ Recommended book: Data Science from scratch https://www.oreilly.com/library/view/data-science-from/9781492041122/

On today's episode, Ama has a discussion with Habeeb Shopeju, a software/Machine learning engineer at Thomson Reuters Lab in London, United Kingdom. Habeeb shares his experience about his journey from an undergraduate in Electrical Engineering in Nigeria which he graduated in 2021, and how he was able to have over 8 different roles in different tech companies while in school to his current role at Thomson Reuters Lab. He shares some tips and resources which helped him on his journey. Connect with Habeeb on Twitter :https://twitter.com/HAKSOAT LinkedIn: https://www.linkedin.com/in/habeebshopeju/ Recommended book: Apprenticeship Patterns: Guidance for Aspiring Software Craftsman : https://www.amazon.co.uk/Apprenticeship-Patterns-Guidance-Aspiring-Craftsman/dp/0596518382

Ama and William the hosts of the Data Queries podcast share their experience of transitioning into data science from a finance background and moving to the United Kingdom. They talk about their challenges and some useful tips which others can use to ease their learning. Every week they would be bringing industry practitioners to share their stories, challenges and tips to succeed in your career as a data scientist.  Additional materials: https://pydata.org/london2022/ https://gh.pycon.org/ https://docs.openvino.ai/latest/index.html https://www.linkedin.com/in/william-danso/ https://www.linkedin.com/in/amaboh-achu-ngu-23333a9a/