Entering the world of Data Science and Software Engineering

Hello everyone! Welcome, all of you guys to my second medium article after a long time. Hope you guys are staying safe. In this article, I am going to share my opinion on a frequently asked question by fellow undergraduates who are seeking a specialization path, “Software Engineering or Data Science?”.

We know, nowadays the technology is growing fastly as well as the industry is also moving towards the fast-growing. In this situation, undergraduates are looking for industry opportunities to have a good salary as well as to maintain a better reputation among others. After one or two years of starting higher education in IT, they get the opportunity to specialize in a specific technology discipline. There are plenty of pathways to follow up with. In that case, there is a popular question that most of the students are frequently asking. As mentioned in the topic, it is the popular question “Software Engineering or Data Science?”.

First of all, let’s look at those two disciplines.

Software Engineering

“the systematic application of scientific and technological knowledge, methods, and experience to the design, implementation, testing, and documentation of software”

The Bureau of Labor Statistics — IEEE Systems and software engineering — Vocabulary

Looks weird right? There are a bunch of definitions to explain that topic. By the way, let’s try to understand simply what is this. Just imagine the production of a vehicle. There should be a designer to design the blueprint and engineers to make the relevant parts separately. End of the day, all the parts will be assembled as a fully functional product. Just like that, In the software engineering field, there are engineers, experienced architects who are specialized in software designing to implement a solution for a given set of requirements in a well-defined architecture. This is just for understanding. When it comes to the industry there are a bunch of methodologies to follow as well as a lot of roles with various responsibilities to develop a fully functional software solution.

Data Science

“Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.” — Wikipedia —

Like that data science also has some mind-blowing definitions. At the moment, let’s try to understand the basic idea.

In that technology discipline what happens is, We know with the increase of devices that generate data, there should be a proper way to handle and analyze those data to get a meaningful full outcome. When it comes to the industry field, there are so many important data generating day by day. It is not enough to have a huge collection of data without getting any meaningful insights or gaining new knowledge from it. This is the place where data science comes into the picture. As well as in software engineering, this discipline also has some architectural patterns and specific areas to develop a data science solution.

Which one to select?

Now we have a basic understanding of why we need those two disciplines. If you are passionate about programming, problem-solving and also love to play with some programming languages you can think about the software engineering field. When you start on that specialization, programming is not enough, you should have the skills set and the passion to work on that discipline. According to your skillset and passion in the software engineering field, there are plenty of pathways to follow like UI/UX, front-end, back-end, full-stack, mobile applications, DevOps, TechOps, and many more.

Let’s see about the data science,

If you are a person who likes and curious to work with data you can look into that area. Data science is a vast area, so we cannot limit it to a particular frame. As I mentioned before, the primary objective of that discipline is to get meaningful insights from the data as well as gain some new knowledge from the data. As well as in software engineering, you should have the passion and the skills inside you to work with this discipline. In fact, most of the freshers think, data science is only about statistics or Machine Learning or AI likewise. Actually, knowing statistics can be an added advantage but is not compulsory. You can learn those stuff when you start to work on it. Those fields can be considered as sub-areas of the data science discipline. Here also, plenty of pathways to follow like Data engineering, cloud computing, AI, Machine Learning, Data analyzing, Data visualizing, Business Intelligence, Big Data, and many more. According to your passion and the skillset you can master in those fields.

Frequently asked questions:

How about the salary range? for both specializations, it depends on the organization, your skill set, and your specialized pathways. End of the day, if you have the required skillset and experience, the organization will pay you a lot. But it may take time.

How about industry demand? for both specializations, there is a competitive demand. what you need to have are the skillset, knowledge, and experience.

What are the best companies to work for? It depends on the projects there are working with and the achievements they have been achieved in history. You will be able to understand those things while you are working in the field.

Entrepreneur opportunities? limitless

I encourage you guys to explore those two disciplines and figure out what suits you. Another important thing is, to become a good data science professional it is necessary to have the knowledge and exposure of software engineering methodologies. Also to be a good software engineering professional, having knowledge in data science will be a plus point.

Conclusion

End of the day, what I am suggesting you is, don’t do data science or software engineering because of your friend also does this, don’t just only look at the salary rates, don’t just only look at the industry demand. Develop the skills, learn about what you need to do, develop the experience, and do whatever specialization you are passionate about and love. The demand will be created and you will be paid a lot.

Cheers!!!

NOTE: Here I have meant the keyword “experience” from the undergraduate’s point of view. It is about the projects that you have worked on, the knowledge and the familiarity with the relevant technologies and tools