How to Supercharge Your Coding Speed. Don’t google everything. Build a repertoire. When I transitioned from coding mostly in C to coding mostly in C++, I had to look up everything. And I mean EVERYTHING.
When I transitioned from coding mostly in C to coding mostly in C++, I had to look up everything. And I mean EVERYTHING. How to output stuff. How to write things to a file. How to use boost. How to use signals and slots.
I learned C++ back in Uni, in a crash-course format that took 4 weeks that you could do on top of the mandatory C course. It was exciting and fun to learn and I enjoyed it a lot. I didn’t retain the knowledge at all, though.
So now, after a couple of months of coding in C++ professionally, I noticed a trend: I end up googling the same things a couple of weeks apart. I remember having googled them and having found the answer, but I have forgotten how exactly to do it.
If you are a Software Engineer, you know how it goes: Standard use cases are easy to find on StackOverflow or cppreference. But as soon as it gets a little less standard, changing the response that you find and making the whole thing compile takes time.
One of my colleagues seems to know everything that is possible in C++ by heart. He knows if it became relevant in C++11 or if it’s a C feature. He knows in which cases boost is too slow. He knows if one similar construct would improve performance.
All of this makes him an incredibly fast coder. He rarely has to look something up, and if he does, he makes sure to remember what it was.
Maybe that’s a bit much. I don’t like to slow myself down so much — if I were committed to memorizing everything I’ve ever googled, I wouldn’t get anything done right now. But if I never take the time to do so, I won’t improve.
Become a data analysis expert using the R programming language in this [data science](https://360digitmg.com/usa/data-science-using-python-and-r-programming-in-dallas "data science") certification training in Dallas, TX. You will master data...
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
This article will introduce the concepts and topics common to all programming languages, that beginners and experts must know!
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
Data Science Programming Languages: List of data science programming languages that aspirants need to learn to improve their career.