20 Tips: How to Choose Between Software Engineering and Data Science
20 Tips: How to Choose Between Software Engineering and Data Science
When you are researching which career to pursue in the tech field, you will come across many different specialties and careers. Two of the most popular options are data science and software engineering as covered over at runrex.com. Both of these fields come with their own set of pros and cons as well as the fact that some similarities exist between the two. Although they are both segments of the tech industry, they are definitely two very different paths to go down. If you want to decide which is the better fit in terms of your skills and interests, this article, through the following 20 tips, will help you choose.
What is Data Science?
Let’s start by defining these two career paths one after the other to help you know what they are about, starting with data science. As outlined over at guttulus.com, the profession of data science pertains to the use of scientific skills of data extraction, mining, and analysis to solve business problems. It is a vast area covering various industries.
What is the goal of a data scientist?
The goal of a data scientist is going to depend quite a lot on the problem they are examining as discussed over at runrex.com. In the context of business, a data scientist might be measuring the impact of changes in promotional material; in finance, a data scientist is probably trying to discover what (if anything) accurately predicts returns in one of the major markets.
What is software engineering?
Software engineering, on the other hand, is the branch of study that deals with the development and creation of new software by applying the principles of computer science and mathematical analysis. As the gurus over at guttulus.com point out, it involves using programming and engineering skills to develop new software.
What is the goal of a software engineer?
In software engineering, the goal is to create new programs, applications, systems, and even video games. Since there is no such thing as bug-free software, a secondary goal for software engineers is to constantly patch and iterate on existing software to make it better and ensure that it performs as required.
The differences between Software Engineering and Data Science
To give you an idea of which career path between these two is right for you, let us look at the main differences as far as they are concerned.
Differences in methodologies
Data science methodology
There are various places at which a person could come into the data science pipeline. If they are gathering data, they are probably called a ‘data engineer’, and they are going to be pulling data from various sources, cleaning and processing it, and storing it in a database. This is usually referred to as the Extract, Transform, Load (ETL) process. If they are using these data to built models and do analysis, they are probably called ‘data analysts’ or ‘machine learning engineers’. The crucial aspect of this part of the data science pipeline is making sure that any models built aren’t violating their underlying assumptions and that they are actually driving worthwhile insights.
Software engineering methodology
On the other hand, software engineering uses a methodology called SDLC, or the software development life cycle as covered over at runrex.com. This workflow is used to develop and maintain software. The SDLC steps include planning, implementation, testing, documentation, deployment, and maintenance.
Differences in approaches
The data science approach
Another big difference between the two is the approach they tend to use as projects evolve. Data science is a very process-oriented field as the experts over at guttulus.com point out. Its practitioners ingest and analyze data sets to better understand a problem and arrive at a solution.
The software engineering approach
On the other hand, software engineering is more likely to approach tasks with existing frameworks and methodologies. The Waterfall model, for instance, as discussed over at runrex.com, is a popular technique that maintains that each phase of the software development life cycle must be completed and reviewed before moving on to the next.
Differences in tools used
Data science tools
A data scientist’s toolkit contains tools for data analytics, data visualization, working with databases, machine learning, and prejudice modeling. Which of these they end up using will depend on their role. If they are doing a lot of data ingestion and storage, they will probably be using Amazon S3, MongoDB, Hadoop, MySQL, PostgreSQL, or something similar. For model building, chances are they will be working with Statsmodels or Scikit-learn. Distributed processing of big data requires Apache Spark.
Software engineering tools
A software engineer, on the other hand, utilizes tools for software design and analysis, software testing, programming languages, web application tools, etc. As with data science, a lot depends on what they are trying to accomplish. For producing code, Atom, TextWrangler, Visual Code Studio, Emacs, and Vim are all popular. In the world of backend web development, Ruby on Rails, Python’s Django, and Flask are popular. Recent years have seen Vue.js emerge as one of the best ways of building lightweight web applications, and the same could be said for AJAX when building dynamic, asynchronously-updating website content.
Differences in skills
Data science skills
The most important things you will need to know to become a data scientist include programming, machine learning, statistics, data visualization, and a willingness to learn as pointed out by the gurus over at guttulus.com. While different positions may require more than these skills, they are the bare minimum when pursuing a career in data science.
Software engineering skills
If you are interested in a career in software engineering, the necessary skills will often be a little more intangible. Yes, the ability to program and code in multiple programming languages will be required, but you will also need to be able to work well in teams, problem-solve, adapt to different situations, and have a willingness to learn as covered over at runrex.com.
The difference in career paths
How to become a data scientist
According to guttulus.com, data science is one of those career paths where graduate levels of education are still highly valued. This is both because it is a relatively new field as well as the fact that most data science jobs require domain-specific knowledge. Therefore, if you want to become a data scientist, you will want to get the equivalent of undergraduate education; whether you do a traditional university course like computer science or a bootcamp.
The entry barrier for data science
The barrier to entry for data science is high. Even after you get your feet through the door, you will be learning for years to come. This is why hard work and dedication are key if you are to be successful on this career path.
How to become a software engineer
On the other hand, software engineers often don’t require quite the same level of education that data scientists do, with many even being self-taught. While completing a two or four-year degree is the more traditional path to becoming a software engineer, coding bootcamps are another viable option.
The entry barrier for software engineering
As is covered over at runrex.com, becoming a software engineer also requires hard work and dedication. However, if you are willing to put in the hard yards, you can enter this field and be successful. The barrier to entry for software engineering isn’t quite as high as data science, which is something to keep in mind when making your decision.
Considerations when choosing between software engineering and data science
Now that you know the differences between these two career paths, the following tips will cover the main things you need to consider when making your decision on which path to get on.
Do you have a building or discovering mindset?
As the gurus over at guttulus.com point out, software engineering is very much about building; building using engineering tools as well as computer science and programming skills. Data science on the other hand is much more focused on discovering; extracting insights from data and using those insights to drive impact in both business and products. Data science is extremely discovery-focused as you will be always looking to discover insights from the data you are working with. Therefore, if you are more passionate about building products, then software engineering is a better fit. But if you are more passionate about discovering insights, then data science is for you.
How well defined do you want your role to be?
As outlined over at runrex.com, software engineering is a very well-defined role compared to data science. This is because software engineering has been around for a lot longer than data science, which is relatively new. This means that, in most companies, you have a pretty good idea of what your role as a software engineer is going to be, which is something that is not always the case for data science fields, making this another consideration when making your decision.
If you want a more clearly defined job, then software engineering is for you. However, if you prefer something that is more interdisciplinary, then data science is the better fit for you.
Career progression and salary
The career track as a software engineer is pretty well defined; you work as a software engineer for a couple of years, and then you can choose if you want to continue down that track, getting better and better at your craft, or become a technical manager and manage other software engineers. For data science, there are also specializations. For example, you can become a machine learning expert or a domain expert, or even someone who helps business leaders drive insights. The software engineering salary is also pretty much set in stone. According to Glassdoor, software engineers earn an average annual salary of $92,000; ranging from $63,000 to $134,000. The salary for data scientists will vary a lot more depending on the work that you are doing. According to Glassdoor, the average annual salary is $113,000; ranging from $83,000 to 154,000.
Ask yourself if you would rather have a more clearly defined career progression, in which case software engineering is the right fit, or if you would prefer flexibility, in which case data science is the better fit. Don’t overthink the salary component too much as both of these careers are pretty similar in terms of salary and are both pretty lucrative.
How much do you like coding?
As a software engineer, you are going to be spending a lot of your time at work coding. While you may also spend a lot of time coding as a data scientist, you will also have a lot of other things to consider such as brainstorming and formulating questions to ask general research and communicating your insights to your manager and business leaders. Therefore, if you love coding a lot, just for the sake of it, then stick with software engineering. However, if you see coding as more of a skill to discover insights and to drive impact, then consider data science.
Hopefully, this article will help you choose between software engineering and data science, with more on this topic to be found over at runrex.com and guttulus.com.
