Jenn Gamble on Data Science, Machine Learning, and Elixir

Elixir Wizards

The fields of data science and machine learning are moving ever faster. Jenn Gamble has her finger on the pulse and has become an industry expert with a wealth of experience to her name. As today’s guest, she dives into these rich and often complex topics, and she helps us boil them down into palatable nuggets of knowledge. We start off by asking Jenn about her current role at Very, and she tells us about the nature of her team and the things they’re able to achieve. She touches on what the language markups look like for a data science team, before moving onto her experiences in machine learning and data science. Delving deeper, Jenn tells us why it is not a necessity to have a master’s in data science, and why you can be well enough equipped in other senses to become proficient in the area. Later on, she reveals the differences between Elixir models and data science models. Following these detailed explanations, she furnishes listener’s minds with informative comments on relating the foundations of machine learning to IoT, using priori knowledge to add nuance to your machine learning, and how she envisions the future of data science. Join us today and be sure to get all this, and much more!

Key Points From This Episode:

• Introducing today’s guest, Jenn Gamble.
• Jenn tells us about Very, an IoT engineering firm.
• Hear about the data science team at Very.
• We learn more on what the language markup looks like for a data science team.
• Jenn’s experience in learning machine learning and data science.
• Hear her five-year plan while doing her masters.
• We ask if it’s necessary to have a master’s degree to be well-equipped in data science.
• The difference between an Elixir model and a data science model.
• Jenn elaborates on weights and intuitive algorithms.
• Dealing with N-dimensional matrices.
• Relating the foundations of machine learning to IoT.
• Ways to start building up an intuition around what the most fundamental abstractions are.
• Using priori knowledge to add nuance to your machine learning.
• How Jenn envisions the future of data science.
• Hear about tensors and vectors.
• Jenn tells us about her keynote experience at ElixirConf 2020.

Links Mentioned in Today’s Episode:

SmartLogic — https://smartlogic.io/
Elixir Wizards Discord — https://smr.tl/wizards-discord
Elixir Wizards Email — [email protected]
Jenn Gamble on Twitter – https://twitter.com/jennpgamble
Jenn Gamble on LinkedIn – https://www.linkedin.com/in/jenn-gamble/
ElixirConf 2020 - Keynote - Jenn Gamble – https://www.youtube.com/watch?v=btIvtN9ws_I&ab_channel=ElixirConf
IoT – https://www.verypossible.com/careers
Very – https://jobs.lever.co/verypossible
MathWorks – https://www.mathworks.com/products/matlab.html
Cassie Kozykrov – https://kozyrkov.medium.com/
Linear regression – http://www.stat.yale.edu/Courses/1997-98/101/linreg.htm
Pythagorean theorem – https://www.mathplanet.com/education/pre-algebra/right-triangles-and-algebra/the-pythagorean-theorem
Quadratic equation – https://www.mathsisfun.com/algebra/quadratic-equation.html
A priori and a posteriori – https://iep.utm.edu/apriori/
Tensor – https://mathworld.wolfram.com/Tensor.html
Vector (mathematics and physics) – https://mathinsight.org/vector_introduction
Coursera – https://www.coursera.org/learn/ai-for-everyone

Special Guest: Jenn Gamble.