Data Science

Machine Learning Lifecycle for MLOps era brings model and software development together to build ML-assisted products — Building machine learning products or ML-assisted product features involve two distinct disciplines: Model Development: Data Scientists — highly skilled in statistics, linear algebra, and calculus — train, evaluate, and select the best-performing statistical or neural network model.

Handling dictionaries in a Pythonic way — All Python developers need to operate dictionaries, almost on daily basis. Beginners may dislike this part, cause they usually have to write a long function to do a simple job. I saw my girlfriend, who started learning Python last month, write nested for loops to merge two dictionaries. What I…

It breaks my heart when developers say they feel like an imposter. Don’t get me wrong. Imposter syndrome is very real. If anything, our industry exacerbates it. There’s a subtle expectation to be the hero of any team that you’re on. But the vast majority of developers are doing a…

Your portfolio plays a vital part in telling employers why they should hire you. — Every piece of advice you reference for tips on landing a job in data science will talk about the importance of having a professional portfolio that showcases your work and gives employers the chance to take a deep dive into your personal projects. Not only do portfolios allow employers to…

Powerful advice for learning how to code for real-world data science — Table of Contents R Or Python? Square One: Establishing The Environment The 7 Coding Basics Project-Based Learning Specifics For The Data Track Most Important Tips For New Coders I recently got an amazing opportunity where a friend offered to coach me in jiu-jitsu in exchange for me teaching him how to code. I…