If data science is the “sexiest job of the 21st”, Python is undoubtedly the “sexiest programming language of the 21st.”
Python has brilliantly lowered the entry bar to computer science. When you hear someone say “if you know English you can code in Python”, expect people from all walks of life to opt for data science and programming careers. Mostly, because of the lucrative job prospect; partly, due to the data science buzzwords circulating in the community — the jargon makes one feel they’re building rocket ships.
Python is nothing more than a tool. A tool to craft code. Yet…
Python is amazing for its ability to promote programming. It delicately endorses the proverbial “if you know English, you should know how to code” idea. With its English-like syntax, indentation paradigm, and extensive libraries, Python makes other programming languages look useless.
Python is the default programming language of “the sexiest job of the 21st century.” Yes, data buzzwords still make data scientists “sexy,” although the current circumstances wouldn’t fully support that claim. Impressively, Python happens to answer most if not all data science problems.
Python is also popular in blockchain, DevOps, and cybersecurity.
The hype around Python is growing exponentially…
Hardly a day passes on without a bitcoin notification buzzing my phone, which sickens me.
“Bitcoin is nothing more than just another “get rich quick scheme”. And it would’ve been completely forgotten if it wasn’t for its current monetary value.” That’s what my little mind picked from the bitcoin mayhem a few years back.
But, I was wrong!
Bitcoin is more than just another money machine. It’s the dawn of a new era.
In January 2009, bitcoin and the concept of “digital currency” saw daylight. This date however raises questions as bitcoin arrived in the 2008 collapse and economic upheaval…
I have been recently working on a digital image processing project. Hyperparameter tuning took quite some time before I got the desired accuracy. All because of the overfitting parasite and my useless low-end hardware.
For each execution, my machine took approximately 15–20 min. 20 min to process 20 000 entries. I imagine if I had been working on a 1 million record dataset, I would have had to wait for the earth to do a complete rotation before the end of the training.
I was satisfied with the accuracy of the model. Yet, I wanted to try many other Convolutional…
I’ve always been fascinated by how Amazon, Apple, Microsoft do business. Much as they’ve consistently been competitors, no one could defeat the other. And so, the three of them beautifully monopolize the market.
Knowing the secret behind their monopoly has always lingered in my mind. What’s intriguing, though, is that none of the trio has the element of novelty. They all operate in a similar fashion. Yet, their stats are making innovative and futuristic businesses blush.
It turns out that these tech giants share the same mindset. A mindset that was injected into Amazon, Apple, and Microsoft from their inception…
I had always been a fidgety boy, running and jumping around nonstop. Day and night. And my parents were always running after me.
Despite the distress and worry my childhood brought on my parents, it somehow made them more agile and boosted their stamina. Perhaps the reason behind my hyperactivity was my premature birth — at least that’s what they told me.
Cognitive science suggests that fidgeting is a sign of inattention and lack of intelligence. My heart was crushed when I read about that for the first time. But I kept hope.
With that said, my father was a…
I can still recall the day when my math professor said, “Data science is the answer to all your questions.”
It was the first time I had heard the term “data science.” I must admit, it sounded scary — especially since the term is similar to “rocket science.”
I went straight home, fired up my computer, and Googled “data science.” I expected the engine to produce a list of articles that would provide insights into data science. Instead, I was overwhelmed by a multitude of data science online courses spearheaded by Coursera.
As a newbie to data science, I started…
In my previous article, about the slowest modern programming languages, I discussed how dynamically typed languages like Python are user-friendly and less error-prone.
It turns out not everyone agrees.
Many senior developers stated that they find working with dynamically typed languages a pain in the neck. The response below collected the bulk of the claps.
“Excuse me, what? Dynamically typed languages are less error-prone than statically-typed? Sorry, but, not in my 21 years of software development.” — Rasmus Schultz
Following this answer, I decided to compile the main reasons that would make senior developers dodge dynamically typed languages and lay…
Programming languages have been out there for decades. Each language is built to satisfy a certain need.
As of today, the world counts about 700 notable programming languages. While about 250 managed to stay afloat, the rest was dumped by the programming community. (Latest data from Wikipedia)
Performance has been always an alarming matter for programmers. While before, execution time could be measured in calendars, nowadays, it is measured in fractions of seconds. Credits go to advancements in electronics.
The speed rate by which computers evolve follows Moore’s law:
Take a piece of writing. Remove the cohesive devices (However, Therefore…). And read it. Does it make sense anymore? NO! The same thing applies to code without
Conditional branching is a basic and mandatory programming concept. It flags a reasoning shift. The wise utilization of conditionals results in having a short, maintainable, and readable code.
That said, ever since I started coding, I took it upon myself to dedicate as much time as possible reading both senior and entry levels programmers’ codes. Here I lay the track I follow to demystify advanced code.
Surprisingly, the more I read…