Summary: If you are mid-career and thinking about switching into data science here are some things to think about in planning your journey.
We get lots of inquiries from readers asking for career advice and many of these identify as mid-career looking to switch into data science. If you’re in this group you face some of the same challenges beginners do but also some that are unique to your circumstance. Here are some thoughts and observations that may be valuable.
When folks self-identify as mid-career they usually cite 10 or 20 years experience. By my way of thinking that makes you most likely 30 or 40 years old. The majority of these folks say they are involved with IT, perhaps as programmer, analyst, admin, or similar. Some are in an area of operations that is heavily data driven. Most have a bachelor’s degree but some do not. It’s you I’m thinking about in the following notes.
As you read this you may well say ‘this doesn’t apply to me, I’m different’. I am writing for the middle of the standard distribution. If you know you’re a two or three-sigma guy or gal, my hat’s off to you.
This article by Bill Vorhies has the following sections:
- Why Switch At All?
- The Cost of Switching
- Can I Do This OJT? How About MOOCs?
- How Much Education Do You Need?
- How Do You Differentiate Yourself to Get Hired?
- Opportunities and Markets
- Where to Look. Where to Go to School
- Data Science versus Data Engineers
- The Problem with Job Advertising
- AI, Deep Learning, Picking a Specialty
- End Notes
- Some other Data Science Career articles you may find valuable
Click here to read the article.
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