Table of Contents Introduction Startup Advantages and Disadvantages 3. Corporation Advantages and Disadvantages 4. Summary 5. References Introduction Being a data scientist at a startup is very different from being a data scientist at a corporation, or, in other words, a well-established, large company. The choice is ultimately up to you regarding which type…

Glassdoor vs ZipRecruiter vs PayScale — Table of Contents Introduction Average (with some popular companies) City Breakdown Seniority Breakdown Summary References Introduction This article is intended for those who are curious about salary breakdowns in data science for the year 2022. This information can be useful to make decisions in your career, whether it be for your current position or…

As a Data Scientist, collaboration is key — Table of Contents Introduction Planning and Grooming Stakeholder Updates Retrospectives Summary References Introduction A lot of the articles I write, as well as what is out there in general, focus on a singular data scientist, so I wanted to focus on something different itself. That is — the data science team. But first, let's…

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…