Pytest: A Testing Framework for Python Code Photo by Ratanjot Singh on Unsplash How can you check that your code changes actually achieve what they’re meant to? Ensuring your code has integrity is actually quite difficult to ensure, especially at scale. Usually you’ll work in a large team with different people working on different parts of the system.... Continue Reading →
The Top 4 Virtual Environments in Python for Data Scientists
Which Environment Is Yours?Photo by Shahadat Rahman on UnsplashVirtual Environments are a relatively difficult thing for new programmers to understand. One problem I had in understanding Virtual Environments was that I could see my environment existed within an MacOS framework, I was using PyCharm and my code was running, what else did I need?However, as your career... Continue Reading →
You’re living in 1985 if you don’t use Docker for your Data Science Projects
What is it Docker and How to to use it with PythonPhoto by Iswanto Arif on UnsplashOne of the hardest problems that new programmers face is understanding the concept of an ‘environment’. An environment is what you could say, the system that you code within. In principal it sounds easy, but later on in your career you... Continue Reading →
The Future of AI is in Model Compression
New research can reduce the size of your neural net in a super easy wayPhoto by Markus Spiske on UnsplashThe future looks towards running deep learning algorithms on more compact devices as any improvements in this space make for big leaps in the usability of AI.If a Raspberry Pi could run large neural networks, then artificial intelligence... Continue Reading →
The Difference between “is” and “==” in Python
Equality and IdentityPhoto by Mr Xerty on UnsplashPython is full of neat tips and tricks and something worth noting are the different ways to indicate equality, and how these specific two ways are different.The == and is command both indicate some form of equality and are often used interchangeably. However, this isn’t exactly correct. To be clear,... Continue Reading →
The Future of GIT (2020)
Opinion5 Predictions of what Data Scientists can expectPhoto by Yancy Min on UnsplashVersion Control is a pretty boring topic for most people but for coders and researchers, it’s imperative to understand. The importance of version control is really understood when you work in a big team working on a big project. With multiple users working on the... Continue Reading →
How to Deploy Streamlit on Heroku
OpinionFor Endless Possibilities in Data SciencePhoto by Kevin Ku on UnsplashIn a previous post, I predicted that the popularity of Flask would really take a hit once Streamlit comes more into the mainstream. I also made the comment that I would never use Flask again.I still stand by both of these comments.In that time, I’ve made four... Continue Reading →
The difference between ‘git pull’ and ‘git fetch’?
The question we secretly askPhoto by Kristina Flour on UnsplashThis is a brief explanation for the difference between git pull and git fetch then merge. It’s a question that a lot of people want the answer to, being the 4th more most updated question on stackoverflow.The reason so many people get confused is that upon first glance,... Continue Reading →
4 Awesome COVID Machine Learning Projects
Forward thinking ways to apply Machine Learning in a PandemicPhoto by Neil Thomas on UnsplashNote from the editors: Towards Data Science is a Medium publication primarily based on the study of data science and machine learning. We are not health professionals or epidemiologists, and the opinions of this article should not be interpreted as professional advice. To... Continue Reading →
4 Sorting Algorithms in Python
Including Time Complexities and CodePhoto by Olav Ahrens Røtne on UnsplashHave you ever tried to sort a deck of cards by hand? You probably, intuitively, used the insertion sort algorithm. The following article will explain why this algorithm works and how long it takes. The good news is that you do end up with a sorted set,... Continue Reading →