What Are the Most Common Mistakes Made by Aspiring Programmers?

I wasted A LOT of my time teaching myself the basics of coding, machine learning, and stats.

Below are a few mistakes to avoid while you are learning to code:

Learning through books, not projects.

Skip the books and go straight to learning by doing.

Avoid learning all the math until after you do your project. I spent many months reading books and taking online courses, and I promptly forgot 90% of it. Then I did one project and all the information came together.

Start doing projects, NOW.

Focusing on getting a job rather than innovation and clearing basic concepts.

Many of my students learning data science and programming just fantasize about how much salary they will get once they complete learning data science.

Data science and AI are changing the future and have a lot to offer yet.

Focusing on innovation opens up new areas to explore. Although many business Cases is Better Solved By Artificial Intelligence (AI) than Conventional Programming, this rule applies for each programmer.

Prefer complex solutions over simple ones.

Inexperienced programmers love complex algorithms because they sound cool.

You may feel good about yourself when you’re working on some highly complex and esoteric deep learning topology, but if you can accomplish the same result with linear regression, you’re just wasting time.

All else being equal, the simpler solution is the best. Remember Occam’s razor. I know you’ve heard of it.

Not spending enough time talking to domain experts.

Every successful programmer does not miss opportunities to network and ask senior developers. The domain expert is someone with a good understanding of the overall programming, while the database expert is ideally a data engineer who is intimately familiar with the data.

Many inexperienced developers try to be all three at once. Unless you’re working on a problem within your own organization and you’ve worked there for several years, you need help.

Depending too Much on Certifications and Degrees.

There are too many of these courses online being poured over and completed by thousands upon thousands of aspiring data scientists.

Even though cracking certificates is no easy job, practical skills are very important. unless you practice and develop practical skills it is very difficult to land a decent job.

Real learning is learning by doing. The more a programmer practice and build projects the more chances are he/she become a successful developer or a data scientist.