Although Python dominates the fields of Data Science and Machine Learning, and, to some extent, Scientific and Mathematical computing, it does have its share of disadvantages when compared to newer languages like Julia, Swift and Java.
One of the main driving points behind Python’s meteoric growth was how easy it was to learn and how powerful it was to use, making it extremely appealing to beginners and even those who shied away from programming because of the hard, unfamiliar syntax of languages like C/C++. …
A couple of weeks ago, I was just about ready to release Caer, a Computer Vision library in Python to be publically available on PyPi, when I decided to send it to a friend in Alberta to tink around with it.
A few days later, I find that he’s still figuring out how to get it to work on his machine.
After dozens of hours, I finally found out why — Caer implemented code from the previous versions of other Python packages that simply weren’t available in their newer releases.
Despite having those packages installed, my friend wasn’t able to run Caer for the reasons mentioned above. …
I’m a Data Science enthusiast and one of the main things I deal with is Data. A lot of it.
With more than 2.5 exabytes of data generated every day, it comes as no surprise that this data needs to be stored somewhere and accessed when required.
This article presents a hackable cheatsheet to get you up and running with SQL quickly!
SQL stands for Structured Query Language. It is a language for relational database management systems. SQL is used today to store, retrieve and manipulate data within relational databases.
Here’s what a basic relational database looks like.
Using SQL, we can interact with the database by writing queries. …