The term “IT (Information Technology)” is comprehensive. If you explore the world of IT, you’ll feel lost when you try to determine the career path right for you. There are ample of specializations, such as web development, AI, software engineering, networking, data science(1) and so on. However, software engineering and data science are two of the most preferred and popular fields. So, this post is all about in-depth data science vs software engineering from various aspects.
Currently, data science is a hot IT field paying well. On the other hand, software engineering has been around for a while now. Considering that, both pay well and own their special place.
If you’re struggling to determine whether to choose data science or software engineering as your career path, you’ll know about it after giving this post a read.
What is Data Science?
Dealing with structured and unstructured data, Data Science compromises of everything that relates to data cleansing, preparation & analysis. It is the combination of mathematics, statistics, problem-solving, programming, capturing data in resourceful tactics, the ability to glance at things differently and the cleansing, preparing and sorting the data.
To put in simple words, Data Science is the umbrella of tactics used when trying to draw information and insights from data. It is a growing and valuable field that offers ample of opportunities to people with the right experience and skills.
(Also Read: What is Data Science? Everything You Need to Know)
What is Software Engineering?
Software Engineering involves the usage of engineering and programming skills to build new software or application. In software development, the purpose is to create new applications, systems, programs, and video games as well.
As we all know that there’s no such thing as bug free software, a secondary purpose for software engineers is to continuously monitor the existing software to enhance it and ensure that it performs as it needed. Like Data Science, Software Engineering is a highly valued field and the perks of a good software engineering skill set are popular. Indeed, if you possess software development chops, then you’re certainly going to find someone who would like to use them.
Data Science vs Software Engineering
So, what is the difference between software engineering and data science? Data scientists use their skills to examine data, understand it in meaningful ways, determine patterns and utilize what they’ve discovered to help businesses to become more efficient. On the other hand, software engineers focus on developing software that’s user-friendly and serves a particular purpose.
Let’s now compare software engineering vs data science in more detail from different aspects.
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Data Science vs Software Engineering – Methodologies
There are so many areas at which one could come into the world of data science. If they’re congregating data, then they’re likely known a “data engineer” and they’re going to extract data from numerous sources, cleaning & processing it and organizing it in a database. This is often known as ETL (Extract, Transform and Load) process.
If they’re utilizing these data to develop models and perform analysis, then they’re probably known as a “machine learning engineer” or “data analyst”.
On the other side, software engineering utilized a methodology known as SDLC (Software Development Life Cycle). This workflow helps to build and maintain software.
The steps of SDLC are as follows:
- Planning
- Implementing
- Testing
- Documentation
- Deployment
- Maintenance
Theoretically, following one of the numerous SDLC models will result in the software running at high efficiency and will enhance any developments in the coming times.
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Data Science vs Software Engineering – Approaches
Data Science is an extremely process-oriented practice. Its practitioners tend to ingest and examine data sets to better comprehend a problem and drive the best solution.
On the other side, software engineering is more probably to approach tasks with already existing methodologies and frameworks. For example, The Waterfall model is a well-known strategy that ensures each stage of the SDLC must be finished and reviewed before proceeding further. There are other frameworks in software engineering such as Spiral, Agile and V-Shaped model.
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Data Science vs Software Engineering – Skills
It’s no doubt in the fact that both data scientists and software engineers get paid well. Indeed, they have to master very technical skills to excel and they have to constantly learn as both fields have evolving technology.
To become a data scientist, you need skills – programming, statistics, machine learning, data visualization and an enthusiasm to learn. It could be more, but these are the minimum.
On the other hand, the necessary skills in software engineering are programming and coding in multiple programming languages. In addition, the ability to work in teams, problem-solving skills, and able to deal with different situations are skills that are also required if you’re want to become a software engineer.
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Data Science vs Software Engineering – Tools
Both software engineers and data scientists leverage a wide array of precision machinery to perform their jobs efficiently and effectively.
A data scientist use tools for data visualization, data analytics, machine learning, predictive modeling and a lot more. If they’re performing lots of data ingestion & storage, they’ll likely be using MongoDB, MySQL, Amazon S3 or something similar.
On the other hand, a software engineer uses tools for software analysis and design, programming languages, software testing, and a lot more.
Whatever your position, it’s imperative to use the best tools for the task you’re doing to accomplish the best results.
Infograph: Data Science vs Software Engineering
Final Thoughts
Which career path is right for you, whether data science or software engineering? It entirely depends on your personal interest and preference. If you like developing things and algorithms, then software engineering is ideal for you. But, if you would love the unpredictable, and like to deal with trends and statistics, then you should think about choosing a data scientist as your career path.
The bottom line is, even though the data science is evolving day by day, its significance never surpasses that of a software engineer, as we will always require them to develop the programs that a data scientist will work on. In addition, with more data at our end, we will always require a data scientist to examine the data and do enhancements in business.
