Learning and acquiring the skills required to be a data scientist can be hard and sometimes cost-prohibitive. Some courses and some tools are definitely expensive. But you shouldn't let this deter you from achieving your goal of becoming a data scientist or improving your existing skill set. There are some great FREE resources out there for data science.
In today’s episode of the Lights and Data Show, we have T. Scott Clendaniel, Lead Artificial Intelligence Solutions Architect at Booz Allen Hamilton. Scott is also an AI pioneer with over 35 years proven track record of dramatic ROI improvements. He is a guest lecturer at John Hopkins University and the University of Maryland, and the author of Harvard Innovation Labs’ Experfy course. He joins us to share with us this trove of FREE resources for data science that can help you become a data scientist or improve your existing skill set.
You will want to hear this episode if you are interested in:
- [00:30] Introducing Scott
- [03:23] Getting back in time when data science tools were priceless
- [05:10] What components do you need for a data science project?
- [05:35] #1: Data
- [07:20] #2: Software
- [09:35] #3: Training
- [10:33] #4: References
- [13:22] Are free resources any good?
- [14:33] What’s the best way to leverage the free resources?
- [16:57] Should you start with R or Python?
- [19:46] Scott’s favorite dataset
- [22:59] Getting started with data science without prior experience
- [25:23] Create a portfolio on Kaggle Team
- [25:54] Volunteer on Data for Good organization & other pieces of advice
- Google has a whole separate search engine, which is called Google dataset search, whose sole purpose in life is for you to be able to access different types of datasets, download them for free, and play around with them.
- If you're already a developer, software engineer, computer science expert, or any of those things, Python is fantastic. Go do that, go forth, and you’ll be successful.
- Anaconda is free, standardized Python code. But it also has many additional applications buried in that menu system. One of them is called Orange data mining.
- I recommend if you don't have a computer science background, learn to use a GUI tool first.
- There is a concept most people aren't familiar with when it comes to books on technology, engineering, and data science. And that's the fact that they have a very short shelf life.
- Part of the challenge is there are too many free resources. Just because it's free doesn't mean it's good.
About T. Scott Clendaniel
T. Scott Clendaniel is an Artificial Intelligence Pioneer with 35 years' proven track record of dramatic ROI improvements. Lead Artificial Intelligence Solutions Architect at Booz Allen Hamilton. He is a guest lecturer at John Hopkins University and the University of Maryland, and the author of Harvard Innovation Labs’ Experfy course. He is also a Chief Data Officer on the Board of Directors, Gartner/ Evanta (DC region). Currently he is the Lead Artificial Intelligence Solutions Architect at Booz Allen Hamilton and a 3rd time guest of the Lights On Data Show :).
“Part of the challenge is there are too many free resources. Just because it's free doesn't mean it's good.”
- T. Scott Clendaniel
- Making Friends with Machine Learning course by Cassie K
- Best Datasets for Machine Learning & Data Science
- The Insane App
- KDNuggets Cheat Sheets
- Open source code for economic modeling
- AcademicTorrents: Data sets for researchers by researchers
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