I started on Mosaic in 1993… i was out of college by the time i truly entered programming… for me, Unix shell, HTML, Lynx programming —- then… just bust-ass trying to KEEP learning, KEEP using your intuition of the ‘good’ v ‘bad’ concepts behind design…. In Data… companies will NEVER understand the importance of having a truly unified Data Dictionary or Catalog… even with a centralized data department, the concept as simple as ‘miles’ or ‘kilometers’ as a measure can become 15 different ‘versions’…. Metric definition at the OUTSET, pervasive throughout the company, no ‘back of the envelope’ calculations accepted as an ‘alternative metric’, etc…. Just my 2 cents ✌🏼😊 accuracy and precision mean something.
This is the biggest problem I see right now with most data people. Once you go past the initial code and library functions, they fail to really understand the logic behind it all. I always say, the first two years of your career the best thing you can do is a be a sponge. Absorb everything you can learn and know how to focus on what matters
IMO, you absorb what's in your env; if you're in a swamp, that's what you absorb. To know what matters, so you can focus on that you'd need mentors who can point you in the right direction.
I think you capture this well in your title—embrace being a beginner. I think approaching each problem with an open mind, as in ‘I might need to solve this differently than I have solved problems in the past,’ is crucial. In my opinion, having the insight and curiosity to recognize that I might need to use something I don’t already know is also really important, coupled with a deep understanding of at least some grouping of technology.
It's funny, as I had the reverse experience route. Python, DBMS, SQL expert/educator, moving into ML, having to pick up Pandas. It made very little sense to me, and I wanted to solve everything using the power of DBMS. I did a lot of that, actually, why not use a system already optimized that I also knew how to use efficiently?!
When I got a chance to pick R, things cleared and made sense :)
I appreciate your article. For myself, I picked up a lot of skills outside of my (job description) by using the strong foundations, being old-school, knowing to do things in CLI. I think that this is one missing component in many (data) programs, they start with the assumption that everyone will use an out-of-the-box solution, and clickety-clack-boom everything works. When things don't work, it's close to impossible to find the root cause when the foundation knowledge is missing.
I'll stop, this comment is already the length of a post :) But I do have lots more to say.
Hi, I started learning software engineering a year ago, but I don't seem to understand a jack till now. Please, how do I build a strong technical foundation?
I started on Mosaic in 1993… i was out of college by the time i truly entered programming… for me, Unix shell, HTML, Lynx programming —- then… just bust-ass trying to KEEP learning, KEEP using your intuition of the ‘good’ v ‘bad’ concepts behind design…. In Data… companies will NEVER understand the importance of having a truly unified Data Dictionary or Catalog… even with a centralized data department, the concept as simple as ‘miles’ or ‘kilometers’ as a measure can become 15 different ‘versions’…. Metric definition at the OUTSET, pervasive throughout the company, no ‘back of the envelope’ calculations accepted as an ‘alternative metric’, etc…. Just my 2 cents ✌🏼😊 accuracy and precision mean something.
This is the biggest problem I see right now with most data people. Once you go past the initial code and library functions, they fail to really understand the logic behind it all. I always say, the first two years of your career the best thing you can do is a be a sponge. Absorb everything you can learn and know how to focus on what matters
absorb everything you can!...unless its a swamp
IMO, you absorb what's in your env; if you're in a swamp, that's what you absorb. To know what matters, so you can focus on that you'd need mentors who can point you in the right direction.
I think you capture this well in your title—embrace being a beginner. I think approaching each problem with an open mind, as in ‘I might need to solve this differently than I have solved problems in the past,’ is crucial. In my opinion, having the insight and curiosity to recognize that I might need to use something I don’t already know is also really important, coupled with a deep understanding of at least some grouping of technology.
i really like the point about coming to each problem with an open mind. Because it can be so tempting to use the method that works last time
It's funny, as I had the reverse experience route. Python, DBMS, SQL expert/educator, moving into ML, having to pick up Pandas. It made very little sense to me, and I wanted to solve everything using the power of DBMS. I did a lot of that, actually, why not use a system already optimized that I also knew how to use efficiently?!
When I got a chance to pick R, things cleared and made sense :)
I appreciate your article. For myself, I picked up a lot of skills outside of my (job description) by using the strong foundations, being old-school, knowing to do things in CLI. I think that this is one missing component in many (data) programs, they start with the assumption that everyone will use an out-of-the-box solution, and clickety-clack-boom everything works. When things don't work, it's close to impossible to find the root cause when the foundation knowledge is missing.
I'll stop, this comment is already the length of a post :) But I do have lots more to say.
100% if you know the basics, the rest are easy
Hi, I started learning software engineering a year ago, but I don't seem to understand a jack till now. Please, how do I build a strong technical foundation?