Full-Stack Data Science: A Complete OverviewAugust 6, 2021 2021-09-22 5:43
Full-Stack Data Science: A Complete Overview
Full-Stack Data Science: A Complete Overview
When most people first dive into the world of data science, they are reasonably naive about how big the field is. With time, they soon come to a realization that the word science in data science is not actually a name placeholder but a descriptive indicator of how a lifetime of learning will not be enough to comprehend the complexities of the field.
So, why do most people still persevere on and go into learning the ins and outs of the field? A simple yet strong reason is that the value of the knowledge to be gained outweighs the hours of effort needed to become a competent data scientist.
This holds true at an organizational level as well. Most enterprises, as well as startups, are constantly learning how to apply data science for their own use cases. They build teams of mathematicians, statisticians, and data scientists and spend millions of dollars on research and development with the sole objective of gaining materialistic/non-materialistic returns that is of a higher value than their financial and time investments.
However, it has been clear that the knowledge of data science in itself is not enough in an industrial setting – people also need the technical know-how of how to store/manage/govern their data, serve/monitor data models in a production environment, and much more.
This is where full-stack data science comes in.
Full-stack data science is the applied science of building end-to-end data science solutions covering four major verticals — data engineering, data analysis, data modeling, and model deployment/monitoring.
Introduction to Full-stack Data Science
Data science is an interdisciplinary field that deals with the study of data using various tools and methods to find unseen patterns, derive meaningful information, and solve problems in a wide range of domains. Full-stack data science is the end-to-end application of this study in real-world practice.
For an organization, full-stack data science unifies the concept of information mining with decision-making, big-data engineering with machine learning, and data storage with revenue generation. It does so by grouping together the following four major verticals of data science under one single roof: data engineering, data analysis, data modeling, and model deployment/monitoring.
These four verticals together form a standard pipeline of how data flows into an organization and how value is squeezed out in each step of the pipeline. This standard pipeline also forms a typical data science project lifecycle.
Full-Stack Data Scientists – Who are they?
A full-stack data scientist is a data professional who is competent in all of the four verticals of full-stack data science.
The competency of a full-stack data scientist differs in terms of his/her experience working on the different verticals of full-stack data science. However, in general, a full-stack data scientist covers the skillset needed to successfully scope, plan and execute an industrial data science initiative — starting from identifying problems that can be solved using data and ending with deploying and monitoring data models.
Here is a list of skills that full-stack data scientists typically possess:
- Ability to identify and understand business problems or opportunities.
- Ability to collaborate with stakeholders to identify existing problems or inefficiencies that can be solved with data science and have to ensure the result is acceptable and meets their needs.
- Ability to effectively communicate with the business team which allows for better collaboration and selling the model to the end-users.
- Ability to find, extract, transform and load the right data to the right model.
- Ability to write clean, efficient object-oriented code which works reliably in production.
- Ability to perform Exploratory Data Analysis.
- Ability to experiment with appropriate machine learning algorithms to solve machine learning problems.
- Ability to deploy model pipelines to production, which allows the end-user to query a model with data or access pre-generated model results in the desired way.
It is definitely difficult to master all these skills, especially as technology, algorithms, and tools advance. So, full-stack data scientists are jacks of all trades and masters of few.
In light of this, full-stack data scientists are mostly recruited by companies to fulfill the position of a data science team lead/supervisor rather than that of a developer. So, it’s not always that a full-stack data scientist is frantically building something across the organization but is instead acting from a managerial position.
Saying that, they can be seen working as regular developers in three particular cases:
- In VC-funded startups that require an all-star team to have an agile approach to Machine Learning (ML) based product development.
- In data science agencies where clients commonly seek an expert to design and develop a solution.
- In self-founded startups where either the founder or co-founder is expected to be a full-stack data scientist.
Differentiating between Data Scientists and Full-Stack Data Scientists
To understand more about full-stack data scientists, let us try to understand why all data scientists are not full-stack data scientists and what differentiates them.
It is true that almost all data scientists know how to clean, transform, analyze, and model the data. However, in an industrial setting, they commonly lack knowledge in a certain number of things and do not have first-hand experience in solving the following listed problems:
1. Unavailability of data – Most organizations do not have ready-made datasets when starting out and they need someone to fetch/curate necessary datasets from external sources. A data scientist is only experienced in working on ready-made datasets and hardly has any knowledge of extracting, transforming, and loading data from various sources. This gap in knowledge is filled by full-stack data scientists (as well as data engineers) since they are able to crawl, transform and store data in any format as required.
2. Proper data storage and management – A data scientist knows how to retrieve necessary data from an installed data storage and management system but lacks the technical knowledge of how to build such a system by himself/herself. On the other hand, a full-stack data scientist knows how data should be stored, managed, and governed and is able to either build an on-premise or a cloud-based solution for data storage and management.
3. Robust model deployment and monitoring – A data scientist knows how to build a data model and has experience competing in various data modeling competitions (such as in Kaggle) but lacks the knowledge of deploying and monitoring data models. A full-stack data scientist knows how to deploy a model in a client-facing use case as well as monitor the model’s performance over time.
4. Fault-tolerance and concurrency engineering – A data scientist doesn’t know how to make model predictions to thousands of user inputs at a single time. A full-stack data scientist knows how to build fault-tolerant and concurrent systems to handle a large number of user inputs.
5. Active learning – A data scientist knows how to train a model once but lacks the knowledge of how to actively train the model with new data points over time in an automated manner. A full-stack data scientist knows how to create an active learning model by automating the end-to-end process of data engineering to data modeling.
By highlighting all the above problems that a data scientist faces, it becomes extremely clear as to what differentiates a full-stack data scientist from just a data scientist.
Also, you may have noticed that the lack of domain expertise is not listed as a problem. Even if you don’t have the domain expertise you can learn it and can work on any problem that can be quantitatively described.
Want to take the next step? – Enroll in the Full-Stack Data Science course on Udemy
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