How you can use DevOp tools for your Data Science and Machine Learning Operations — Table of Contents Introduction Why? Postman Rancher Jenkins Summary References Introduction The position of a data scientist can vary considerably, whether it be from only focusing on algorithms in a Jupyter Notebook, or being a full-stack software engineer utilizing data science models. In school or in your early career, it can be expected that…

Aside from the obvious, what are the specifics? — Table of Contents Introduction Product Manager Collaboration Project Lifecycle Management Summary References Introduction There are a lot of obvious differences between junior and senior data scientists, as the titles imply, but what are some lesser-known differences? …

Better categorical encoding methods for your Machine Learning algorithms (not one-hot encoding) in 2022 — Table of Contents Introduction Target Encoder CatBoost Encoder CatBoost Library Automatic Encoder Summary References Introduction The once-ruler of encoding, one-hot-encoding, is no more. Newer, better, and frankly, easier approaches to encoding have emerged and are starting to replace conventional encoding types. In a world of quicker algorithm implementation, better interpretability of features, etc., there…

Most data science projects are more similar than you would think — Table of Contents Introduction Query/Data Preprocess Train Deploy Summary References Introduction The purpose of this article is to inform the main steps that are practically applicable to every data science project. This discussion can be helpful to not only beginner data scientists, but to product managers, software engineers, and even advanced data scientists who…

Software Engineering and other non-data-science techniques that are helpful in streamlining your Data Science work — Table of Contents Introduction DevOps Deployment Scripts SQL Querying Within Python Preprocessing Automation Between Models Summary References Introduction Data science requires a wide array of skills and most people following a traditional route in academia, like a graduate degree, might not see much emphasis on software engineering as a result. This article is intended…

Updated US salary deep dive for your 2022 career decisions and interests — Table of Contents Introduction Machine Learning Engineer (ML Engineer) Software Engineer Summary References Introduction The goal of this article is to show some differences and similarities between two roles that are sometimes a little related, and at other times, very related. You can use this information to decide which role to pursue, for entertainment…

Updated US salary deep dive for your 2022 career decisions and interests — Table of Contents Introduction Software Engineer Data Scientist Summary References Introduction The goal of this article is to show the differences and similarities between two popular tech roles. This information can be helpful for determining which role you would like to pursue. It can also be interesting to clear up some assumptions between these…

…that can help you for 2022 — Table of Contents Introduction AB Testing is Only About Significance Only Looking at Model Error & Accuracy You Won’t Use SQL Summary References Introduction Data scientists have a lot of expectations from companies as well as expectations for the role itself. There are things we expect that turn into misconceptions of the position itself…

…and their main benefits for Data Scientists — Table of Contents Introduction CatBoost DeepAR Forecasting PyCaret Summary References Introduction New algorithms are difficult to come by and 2022 is likely going to be no exception. However, there are still a few machine learning algorithms and python libraries that will be more popular moving forward. The reason these stand out from the rest…