ARTICLE
An Introduction to Full Stack Data Scientists
For the past few years, the ‘data scientist’ career path has been in the limelight due to its high demand in enterprise-level organizations as well as in AI-focused startups. It is a fairly new position yet the fundamentals involved in becoming a data scientist have been around for over multiple centuries.
However, a newer role that is slowly taking its place in the industry right now is the role of a full stack data scientist. As the name implies, a full stack data scientist is someone who is proficient in all of the aspects of building a full-fledged data science solution, including the identification of problems, gathering of data, training, and building of data models as well as working on their deployment.
In this article, we will be diving deep into how the role is now consolidating most data science roles and how you can take a step in becoming a full stack data scientist.
Why are organizations leaning towards hiring Full Stack Data Scientists?
The term full-stack has been extensively linked and used in the field of web development. Organizations typically hire full-stack web developers because it allows them to build an end-to-end web development solution with a minimal number of hires. Full-stack web devs also help in guiding such organizations to walk in the right path of digital transformation with the proper allocations of tools and resources.
And, this holds true for full stack data scientists as well.
Full stack data scientists are needed to build end-to-end data science solutions. They are responsible for working on each stage of the data science lifecycle, which generally consists of data engineering, data analysis, data modeling, and model deployment.
Previously, data engineers were needed to build data engineering pipelines, data analysts were responsible to perform data analysis, data scientists were tasked to build data models and DevOps engineers were needed to deploy the models. But, full stack data scientists are consolidating all of these data science roles by becoming an all-in-one hire.
So with the exponential growth in the use of data science and its huge business impact, businesses now want to hire people who are experts in multiple skills and this is why organizations are leaning towards hiring Full Stack Data Scientists.
The Full Stack Data Scientist Toolbelt
Now that we have a clear picture of how full stack data scientists are beneficial to organizations, let us understand what skills are necessary for the role. We will call this, ‘the full stack data scientist toolbelt’.
Here is a list of items a full stack data scientist should have in his/her toolbelt:
- Knowledge of Mathematics and Statistics: Mathematics and Statistics are the foundational pillars of data analysis and data modeling. The knowledge of these two fields is crucial to be able to understand the underlying mechanisms and processes that goes into building state-of-the-art data science solutions.
- Ability to code: Besides the theoretical understanding of Mathematics and Statistics, having concrete programming knowledge is also one of the essential skills for a full stack data scientist. Currently, Python is one of the most popular programming languages used for Data Science followed by R.
- Experience in working with SQL/NoSQL databases: When working with data, knowing how to store them is crucial to be able to efficiently access them when and where required. Having knowledge of databases and query languages such as SQL is very helpful when working with large amounts of data in real-world projects.
- Hands-on experience of data engineering: Engineering the data requires a lot of work and effort. It involves extracting, transforming, and loading the data prior to applying any analytical processes on it. A full stack data scientist should know how to perform data engineering as the first step in a data science lifecycle.
- Knowledge of data modeling algorithms: A full stack data scientist should have knowledge of how data can be modeled using various Statistical or Machine Learning algorithms.
- Knowledge of DevOps: In contrast to what general Machine Learning practice projects illustrate, real-world projects are very different. Data models should be portable, scalable, and secure when working with different systems and across multiple development teams as well as end-users. Having the knowledge of DevOps allows a full stack data scientist to securely deploy the data model into a production environment for implementation.
Phew! That’s a lot.
Conclusion
The skills required for a full stack data scientist are diverse. Each of these fields are vast on their own and hence, it might take some time to be able to master all the skills. So, it is important to remember that ‘consistency is key’ when learning anything new.
However, on the brighter side, a full stack data scientist is ideal for organizations that are new to data science and are looking to try and create solutions without having to hire a full-fledged team. So, if you do become one, you have a high chance of landing that six-figure paycheck you’ve always wanted.
