Full Stack Data Scientists Are Trending Right Now: Here’s How You Can Become One

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Full Stack Data Scientists Are Trending Right Now: Here’s How You Can Become One

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In 2019, everyone wanted to become a data scientist.

In 2020, everyone wanted to become a data engineer.

In 2021, everyone wanted to become a machine learning engineer.

In 2022, things have come full circle — almost.

Now, companies are wanting someone who can do it all — translate business problems, write production-ready code, develop machine learning models, engineer data pipelines, present to C-level executives, and more. The wants and needs of companies are beginning to drive the futures of the next generation of data-driven techies looking to get a job in the field. This time, however, companies are leaning towards an individual who appears more on the software side of the tech spectrum, just with a data science flair.

This time, in 2022, companies are looking for full-stack data scientists.

What is a full-stack data scientist?

Never before have we seen so many job ads for a full-stack data scientist. But what exactly is one?

A full-stack data scientist is a unicorn who is capable of fulfilling the role of a software engineer, data engineer, business analyst, machine learning engineer, and data scientist, all wrapped up in one package. These individuals have diverse skill sets beyond even that of a regular data scientist and could be a company’s one-stop shop for managing the entire lifecycle of a data science project.

This full lifecycle approach means that full-stack data scientists are capable of identifying the business need (or working with C-level executives to determine which problem needs to be solved), setting up the data architecture required for the project, analyzing data and building models, and finally deploying the model into the production environment.

In essence, this person is a one-man data science team who can fulfill all of the data requirements of a small company.

How is a full-stack data scientist any different than a data science generalist?

Full-stack data scientists may be a bit simpler than you think.

In essence, most up-and-coming and experienced data scientists have most of the skills required to become full-stack data scientists.

The one thing that sets full-stack data scientists apart is their software and data engineering skills. This is where data science generalists and full-stack data scientists differ. Data science generalists will have a variety of skills in a multitude of areas (a jack of all trades, if you will), but may not have the deep experience in carrying out the end-to-end work of an entire team.

Companies are no longer necessarily hiring data scientists for their original purpose and are instead expecting data scientists to bring with them a breadth of skills across a variety of tasks. This has resulted in data scientists looking to become even more impactful by expanding their data and software engineering skills to accommodate all of the job requirements now on the table.

How to become a full-stack data scientist

A full-stack data scientist has all of the basic skills of a regular data scientist with the added features of strong data and software engineering skills.

At a foundational level, full-stack data scientists will have the mathematical, analytical, design, and coding skills required to solve any general data science problem. These fundamentals are outside the scope of this article, but more information can be found here:

From there, data scientists can expand their data and software engineering skillsets to become the full package.

Software engineering

The easiest skill to improve upon is software engineering. All this entails is writing better code than you currently are.

The software engineering that full-stack data scientists need to know revolves around being able to carry a data project end-to-end, meaning that you can launch it to a production environment at the end. This will involve developing skills in modularity, documentation, and automated testing.

Modularity refers to writing your code such that its functionality is separated into independent, interchangeable modules. These modules should be separated into accessible classes and functions that allow you to write code once, improve the performance of your code, and that keep your code files small and easy to navigate.

The next step in improving software engineering skills is to learn how to write good code documentation. It’s surprisingly common to integrate new code into an existing production environment only for all hell to break loose with no code documentation to help you clean up the mess. Good code documentation is simple to create and revolves around highlighting any key points in your logic, troubleshooting in advance, and generally giving a good overview of what the code does and how it should work.

The final step in improving your software engineering skills is to develop a feel for automated testing. Compared to normal testing which involves running through your code by hand and seeing if it throws an error every time you enter a segment of logic, automated testing is carried out using tools that carry out these tasks for you. The types of automated testing you can carry out include unit testing, smoke tests, integration tests, regression tests, API testing, security tests, performance tests, acceptance tests, and more. Some of the tools that you’ll become familiar with include Selenium, LambaTest, and QMetry Automation Studio. Here is a great video to get you started with automated testing:

Data engineering

Data engineering is the other skill set that you’ll need to improve before you can call yourself a full-stack data scientist.

Data engineering involves “designing and building systems for collecting, storing, and analyzing data at scale”. More broadly, this can be expanded to include acquiring datasets, developing algorithms to clean data, creating data validation models, ensuring compliance with data security policies, and more.

Data engineering revolves around using a mix of programming, database, distributed systems, and cloud engineering skills to develop the data pipelines which are so essential to a good data science project.

Data engineering skills, like software engineering skills, can be picked up quite easily through free online courses. The best source for getting a well-rounded education in data engineering is a free online course by DataTalks.Club called Data Engineering Zoomcamp. This course takes you through the basics, from setting up your environment to learning about workflow orchestration, to creating data pipelines locally and in the cloud. Data warehouses, batch processing, and end-to-end projects are also covered.

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