The Philosopher of Data Science | Giuseppe Bonaccorso


September 28th, 2020

1 hr 28 mins 31 secs

Season 5

Your Host
Special Guest

About this Episode

Giuseppe Bonaccorso is an experienced and goal-oriented leader with wide expertise in the management of Artificial Intelligence, Machine Learning, Deep Learning, and Data Science. His experience spans projects for a wide variety of industries including: healthcare, B2C and Military industries, and Fortune 500 firms.

His main interests include machine/deep learning, data science strategy, and digital innovation in the healthcare industry.

You may recognize him from the many best-selling machine learning books he’s published including: Python: Advanced Guide to Artificial Intelligence, Fundamentals of Machine Learning with scikit-learn, and Hands-On Unsupervised Learning with Python.


[00:13:01] The need for creating a culture of data science

[00:16:08] Why you need to be more than a nerd

[00:27:06] Heuristics for scaling data

[00:35:50] How to cross-validate

[00:43:53] Feature engineering techniques

[00:46:50] A lesson on tuning hyperparameters

[00:51:33] A lesson on using regularization

[00:58:01] What to do after model deployment


[00:10:29] "Data science is not something that can be learned in a week or even in a month. It's a real topic with a lot of theory behind. And it's very important for the practitioners to have clear ideas about what they do."

[00:22:45] "Another very important thing when defining a model is that our goal is not necessarily to describe what we already know, but to make predictions. So our model must become a sort of container of future possibilities. "

[01:06:14] "Data science is a science for sure. There is mathematics behind and we never we should never forget this. But I consider also mathematics and mix of science and art."

[01:09:48] "The only way you can really expand yourself is to be curious, to learn the new processes, to learn how other people work, to talk to other people, to understand how your business work."






[00:01:44] Introduction for our guest

[00:03:06] How Giuseppe got into data science

[00:04:37] The hype around data science

[00:06:10] Machine learning in the future

[00:07:33] The biggest positive impact data science will have in the near future

[00:10:13] How to minimize the negative impacts of data science

[00:13:39] Healthy vs unhealthy data science culture

[00:17:45] Good vs great data scientists

[00:21:50] What's artists I would love to hear from you.

[00:22:33] What is a model and why do we build them in the first place?

[00:27:06] Heuristics for scaling data

[00:35:50] With so many methods of cross-validation out there, how can we know which one to utilize for any given scenario?

[00:43:43] How we can be more thoughtful with our feature engineering feature?

[00:46:50] Tips on tuning hyperparameters

[00:51:33] A lesson on using regularization

[00:58:01] What to do after deployment

[01:01:24] The data generating process

[01:04:00] Keywords you need to search to learn more about different parts of the machine learning pipeline

[01:06:01] Do you consider Data science and machine learning to be an art or purely a hard science?

[01:07:21] Creativity and curiosity

[01:10:38] How could Data scientists develop their business acumen and cultivate a product sense?

[01:13:50] Advice for people breaking into the field

[01:17:19] What’s the one thing you want people to learn from your story?

[01:19:08] The lightning round

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