One of the confusing questions you need to answer before you get into any job that requires dealing with data is, which career path should I choose? Which one will fit my aspiration most?
Answering these questions is difficult because some terms are not easy to distinguish from others, so how can you decide if you don’t know the difference? Hence, in my opinion, the most challenging role in differentiating is a data scientist and data engineer.
When you first enter the data science field, these terminologies may be confusing; you may think they are identical or similar but get told differently by others. It definitely doesn’t help that data science is a broad term that covers various job descriptions. However, after tons of reading and research, I finally understood the subtle difference between the two roles.
The truth is that data science and data engineering are related terms; there is a lot of overlap between the two terms. Nevertheless, each path requires a somewhat different learning path and will provide different results. So, let’s get into the differences between the two roles.
DATA SCIENTIST
Data science is not just one role; it is an umbrella term covering different terms and sub-branches, like natural language processing, computer vision, machine learning, deep learning, etc.
However, if we want to put what a data scientist does in words, it will be something close to; a data scientist is a person with a curious mind who loves to ask questions to solve a problem. They rely on data to design algorithms, develop code and build models to reach actionable insights from this raw data.
The main goal of any data science project is to explore data, find patterns and trends, and use this information to predict future patterns and trends using different tools and techniques that are often machine learning algorithms.
Skills needed
Since data science is an interdisciplinary field, for you to be a successful data scientist, you will need to master several technical and soft skills. But mastery requires a long time; you can kickstart your career if you are comfortable with the fundamental knowledge required to build any project.
These basic skills are:
- Maths and statistical expertise.
- Programming and software development.
- Data collection, cleaning, and exploration.
- Data visualisation and storytelling.
- Familiarity with the core algorithms of machine learning.
- A basic understanding of business models and how they are developed.
Job responsibilities
As a data scientist or an expert in any subfields, you will be expected to solve complex problems using collected data to analyze, clean, explore, model and test. Your job will mainly be to use different algorithms or design new ones to solve problems efficiently and quickly.
The insights collected from your model will be used to enhance or build new business models. So, your job will be critical for some companies’ success and how much profit they may obtain.
DATA ENGINEER
On the other hand, a data engineer’s primary role is to work on preparing the data for analysis or use by data scientists. Essentially, data engineers are responsible for building data pipelines to gather information from different sources. Then, they merge, filter, structure, and clean data for analysis.
Skills needed
Aside from the naming, both data engineering and data science require similar skill sets. Both need the same fundamentals and skills that set the two roles apart.
If you want to be a data engineer, the skills you need are:
- Programming knowledge is fundamental in languages such as Python, R, and SQL, which are the three languages data engineers use daily.
- Knowledge of ETL (Extract, Transform and Load) tools and REST-oriented APIs to be able to create and manage data integration tasks.
- An understanding of data warehouses and data lakes and how they work.
- The ability to utilise business intelligence (BI) platforms and the ability to configure them.
Job responsibilities
As a data engineer, you will be a part of the analysis team collecting the data and working closely with data scientists. Your primary job will be to provide structured and ready-to-use data to the data scientists so they can use it for different applications such as machine learning and data mining. Furthermore, as a data engineer, you will also deliver the data to the end user so they can use the insights reached by the data science to improve their business decisions making process.
Getting into data science is challenging, but it gets even more difficult when you don’t know what the different terms in the field mean, not just within the field but also in the job market. Perhaps data science and data engineering are at the top of the list of the most confusing terms.
Those two terms are so intercorrelated that they confuse any newcomer and people within the field. Because of the correlation between the two terms, reaching a concise, clear distinction between them is quite tricky.
But, despite some similarities, the two roles are quite different; they have different job responsibilities and skill sets that one needs to get the job.
