We discuss everything about a career in data science, with individuals from unorthodox backgrounds about their journey into becoming a data science. The gap between academia and data science and how to prepare yourself to make that career transition from your current role into a Data science, must know frameworks and tools, debunking complex statically and machine learning concepts into everyday life examples to make data science less of a theoretical concept but more like your favorite movie.
Data Queries
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7 episodes
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Podcast Overview
We discuss everything about a career in data science, with individuals from unorthodox backgrounds about their journey into becoming a data science. The gap between academia and data science and how to prepare yourself to make that career transition from your current role into a Data science, must know frameworks and tools, debunking complex statically and machine learning concepts into everyday life examples to make data science less of a theoretical concept but more like your favorite movie.
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Publishing Since
11/9/2022
1 verified contact email on file for Data Queries
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Recent Episodes
May 25, 2023
Meet Prince Canuma an MLOPs and DevRel with Neptune AI, whose curiosity into computer science took him to India and presently in Poland working with a leading Ai StartUp.
<p>"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."</p> <p><br></p> <p><strong>Connect with Prince</strong></p> <p>Twitter: https://twitter.com/CanumaGdt</p> <p>LinkedIn: https://www.linkedin.com/in/prince-canuma-05814b121/</p> <p>Medium: https://prince-canuma.medium.com/</p> <p><br></p> <p><strong>Articles, talks and podcast:</strong></p> <p>What is MLOPs by Prince: https://neptune.ai/blog/mlops</p> <p>Lightening talk at MLOPs: https://t.co/LlMuFfLXXM</p> <p>Implementing MLOPs at a reasonable scale: https://t.co/lTtbebDt2h</p> <p><br></p> <p><br></p> <p><strong>Recommended Resources: </strong></p> <p><br></p> <p><strong>Community</strong></p> <p>MLOPs community: https://mlops.community/meetups/</p> <p><strong>Courses </strong></p> <p>Machine Learning Engineering for Production (MLOps) Specialization by Andrew Ng : https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops</p> <p><br></p> <p><strong>Books</strong></p> <ol> <li>Deep Learning with Python by François Chollet: https://www.amazon.co.uk/Deep-Learning-Python-Francois-Chollet/dp/1617294438</li> <li>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</li> <li>Designing Machine Learning Systems By Chip Huyen: https://www.amazon.com/Designing-Machine-Learning-Systems-Production-Ready/dp/1098107969</li> <li>Machine Learning Design Patterns By <a href="https://learning.oreilly.com/search/?query=author%3A%22Valliappa%20Lakshmanan%22&sort=relevance&highlight=true">Valliappa Lakshmanan</a>, <a href="https://learning.oreilly.com/search/?query=author%3A%22Sara%20Robinson%22&sort=relevance&highlight=true">Sara Robinson</a>, <a href="https://learning.oreilly.com/search/?query=author%3A%22Michael%20Munn%22&sort=relevance&highlight=true">Michael Munn: </a>https://learning.oreilly.com/library/view/machine-learning-design/9781098115777/</li> </ol> <p><br></p>
April 2, 2023
Meet Leonidas a Data Engineer at Master Card and Data Scientist at Imperial College London, who became a Data Engineer by the luck of his personal attitude.
<p>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. </p> <p><br></p> <p><br></p> <p>Connect with Leo on LinkedIn: https://www.linkedin.com/in/leonidas-efraim/</p>
March 4, 2023
Meet Tambe a Data Analyst at Social Finance. Part 2
<p>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.</p> <p>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. </p> <p>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. </p> <p><br></p> <p>Tabithat's recommended earning resources </p> <ul> <li>DataCamp : https://www.datacamp.com/</li> <li>Udacity : https://www.udacity.com/</li> <li>ALX: https://nanodegree.alxafrica.com/</li> <li>CS50: https://www.youtube.com/@cs50</li> </ul> <p>Tabithat's read book recommendation.</p> <ul> <li>The Bible </li> <li><a href="https://www.amazon.co.uk/Success-Sixteen-Lessons-Napoleon-Hill/dp/1617201782/ref=asc_df_1617201782/?tag=googshopuk-21&linkCode=df0&hvadid=310831412334&hvpos=&hvnetw=g&hvrand=29281600487622694&hvpone=&hvptwo=&hvqmt=&hvdev=c&hvdvcmdl=&hvlocint=&hvlocphy=9045954&hvtargid=pla-453448116206&psc=1&th=1&psc=1">The Law of Success</a> by Napoleon Hill</li> <li><a href="https://www.amazon.co.uk/Im-Youre-Thomas-Harris-M-D/dp/0099552418">I'm ok your Ok</a> by Thomas Harry </li> <li><a href="https://www.amazon.co.uk/Automate-Boring-Stuff-Python-Programming/dp/1593275994">Automate the boring stuff with pthon</a> by Al Swegart</li> <li><a href="https://jakevdp.github.io/PythonDataScienceHandbook">Python Data Science Handbook</a> by Jake VanderPlas </li> </ul> <p> Bonus </p> <ul> <li>Python Cookbook by David Beazley & Brain K. Jones</li> </ul> <p> books to explore.</p> <ul> <li><a href="https://www.amazon.co.uk/Deep-Work-Focused-Success-Distracted/dp/B01D0JE7KQ/ref=sr_1_1?adgrpid=60154452904&gclid=Cj0KCQiA9YugBhCZARIsAACXxeJPLyt4vTAJBFYfELIvO6VVYN3ygUhHPWN5fNerJts7oYobUi0dqykaAsq3EALw_wcB&hvadid=606070243375&hvdev=c&hvlocphy=9045954&hvnetw=g&hvqmt=e&hvrand=15698646239558310078&hvtargid=kwd-445787283326&hydadcr=18490_2288070&keywords=deep+work+-+cal+newport&qid=1677932249&sr=8-1" target="_blank">Deep Work </a>by Cal Newport</li> <li><a href="https://www.amazon.co.uk/Range-Generalists-Triumph-Specialized-World/dp/1509843493" target="_blank">Range</a> by David Epstien</li> </ul> <p>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...!</p> <p><strong>Reach out to Tambe Tabitha</strong></p> <p>Tambe’s talk at PyData London: https://rb.gy/eey0hk</p> <p>Twitter: https://twitter.com/TambeAchere</p> <p>LinkedIn: https://www.linkedin.com/in/tambe-tabitha-achere/</p> <p>Vacancies at Social Finance: https://www.socialfinance.org.uk/careers</p> <p><strong>Stream or Share on other platforms:</strong></p> <p><strong>Spotify:</strong> https://rb.gy/eey0hk</p> <p><strong>Google Podcast:</strong> https://rb.gy/eey0hk</p> <p><strong>Apple podcast:</strong> https://rb.gy/h0mz3x</p>
7 total episodes available
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