The 3 Fields in Data Science

If you are feeling overwhelmed, and don't what to focus on, then this is for you. We will be comparing a Data Scientist, Machine Learning Engineer, and a Data Engineer.

Table of Contents:

1. Who This Post Is For

2. Data Scientist (DS)

3. Data Engineer (DE)

4. Machine Learning Engineer (MLE)

5. Summary

1 - Who This Post Is For

If you are new to the field of data science, the odds are you are probably overwhelmed. One person is telling you all about matrices and why they are the most important thing on the planet. They are probably warning you of all the horrors that will happen if you don’t go all out and learn every little thing about them.

A different person is probably telling you why doing super simple things like basic statistical calculations is horrible, and instead why you should go learn the most cutting edge deep learning neural networks with the most cutting edge solver that exists. They are also probably telling you why you should immediately ditch your computer, and rent a server from AWS for your models, or you are not maximally using your company’s hardware to maximize profits the best.

Another person is probably telling you how important it is for your data pipelines to be done 100% efficiently, and why you shouldn’t use SQL tables as that is super super old, and instead why you should be using super cutting edge cloud computing technologies, and …. you get the idea.

Here is the thing, the Data Science industry started off small, and at this point the industry has grown large enough to the point where you can basically split the industry into 3 main different career paths. These 3 paths are: Data Scientists (analysts), Machine Learning Engineers (researchers), and Data Engineers (architects). If you are one of those who is confused, and feeling a bit overwhelmed, this post is for you. Now let’s do a bit of a deep dive into these 3 fields.

Note: If you are applying for jobs in the industry, remember most of the time it will be HR doing the postings… so if the responsibility matches what you are looking for, but they gave it a different title, just apply anyways.

2 - Data Scientist (DS)

A data scientist is an analyst who will be expected to have a bit of knowledge on everything, but he/she will specialize in actually working alongside the key decision makers and using their available data and statistical analysis to help the business run better. In other words, a data scientist should have the basic know how to take a bunch of old data, and construct some basic pipelines, and setup a simple working database, and also how to do some basic level of deep learning models.

But, it is understood by everyone that building DL models, and data pipelines is not their specialty, instead the specialty is on working alongside the key decision makers and helping them solve whatever problems they are after.

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