Chris Sanchez: Data Science-related Master's Degrees vs. Bootcamps | The Data Canteen #02

So, you’ve decided that you’re ready to take the plunge and seriously upskill into data science & machine learning…but should you do a master’s degree or a boot camp?

 

In this episode, I'm joined by Chris Sanchez, a retired Navy SEAL and fellow member of the Veterans in Data Science & Machine Learning community. The conversation starts with an overview of his journey through the military and into data science.

 

Chris' unique perspective extends beyond just his background with Special Operations Forces (SOF). He's also rather rare in the sense that he attended both a data science graduate program (UC Berkley’s Master of Information and Data Science) and a data science boot camp (Galvanize Data Science Immersive Bootcamp).

 

Consequently, Chris and I dive deep into a candid compare & contrast of his experiences with both learning approaches, where each excelled, and where they didn’t. Chris starts the comparison by telling us about the best qualities of each training approach. Then, we zoom in to continue the comparison, looking at seven key facets:

 

- Cost

- Duration

- Financing

- Prerequisites

- Pedagogy

- Networking

- Career Support Services

 

The episode wraps-up with Chris sharing some of his favorite data science & ML learning resources:

 

- DATAQUEST: https://www.dataquest.io/

- Learn Python 3 the Hard Way: https://www.amazon.com/Learn-Python-Hard-Way-Introduction-ebook/dp/B07378P8W6

- Andrew Ng’s Stanford CS229 – Machine Learning: https://see.stanford.edu/Course/CS229

- Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646

- Fast.ai: https://www.fast.ai/

- Practical Deep Learning for Coders (Fast.ai’s MOOC): https://course.fast.ai/

 

Go here to connect with Chris on LinkedIn (be sure to include a short contextual note with your connection request!): https://www.linkedin.com/in/excellenceisahabit/

 

Go here to see Chris’ GitHub portfolio: https://americanthinker.github.io/

 

There is so much packed into this episode that it's impossible to put it all in the show notes. 

One recommendation that I do have is that you listen to this podcast, then listen again while taking notes. 

 

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