Some might argue that comparing a data scientist to a software engineer is pointless, as these are two entirely different disciplines. However, both of these professions have a lot in common, even to such an extent that one cannot exist without the other. That’s why we have decided to perform a small comparison–data science or software engineer. Time to find out what both these niches entail and what role they play in the enormous big data world.
Let’s talk about similarities first between a data scientist and a software engineer.
- Both of these professions are data-related.
- Are currently in demand.
- Both of these professions were devised to help organizations harness big data and turn it into useful insight, beneficial in the decision-making process.
Nowadays, our world is overfilled with data. We repeat that continuously in our blog posts, and for a valid reason. In the not too distant future, every company that wants to grow will have to harness big data, one way or another. The following numbers speak for themselves:
- In 2020, every person will generate on average 1.7 megabytes in just a second
- Internet users generate about 2.5 quintillion bytes of data each day
- By the end of 2020, there will be about 40 trillion gigabytes of data (40 zettabytes–that’s a multiplication by the seventh power of 1,000)
It’s hardly surprising that someone has to master it. Otherwise, it’s useless. And data science and software engineering alike try to do so.
The Difference Between A Data Scientist And A Software Engineer
As you already know, both of these disciplines are essential to harness big data. But what are they about? And what role do they play when it comes to big data? To make this comparison readable, let us begin with software engineering. This will make it easier to distinguish the roles of a data scientist or software engineer.
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Simply put, the software engineers create products (programs, applications) that later generate or gather data.
This means that they work on developing and building web and mobile apps, operating systems, and other variegated software used by companies and ordinary people. The software engineers deal with such issues as:
- The software design and development
- Software testing and maintenance
- Web development (either web applications and websites)
- Mobile app development
- System testing and improving
The perfect software engineer should have experience in working with technologies like Hadoop, Hive, Pig, Oozie, Map Reduce, Spark, Sqoop, Kafka, Flume, etc. They should understand the needs of a client and end-user alike. Software engineers are ought to know at least two core programming languages (Python, C, C++, Java, etc.), and other tools designed to build and test applications.
A software engineer has to combine the requirements coming from the programmers, project managers, and business analysts. Their work is 100% hard-headed; they focus on crucial facets of every software and app they develop. That’s why it takes a lot of knowledge and experience to become a successful software engineer. Depending on a system the given engineer uses, they can be called systems/software/database/web programmers, engineers or developers.
In summary, a software engineer builds software, maintains it, tests it, and improves it when needed. Software engineering, therefore, creates a base that every data scientist uses in their work. That’s why it’s entirely justified to say that there is no data science without software engineering.
Time to tackle data science. In general, the data scientists analyze, process, and model data they possess. Then, they interpret the results to create actionable plans and insights for companies and other organizations.
Data science is a multi-role field of expertise that entails an understanding of such disciplines as:
- Computer science
- Business knowledge
- Software development
- Machine learning
The list of desirable skills one should possess is even longer, and it comprises i.a.:
- Data visualization and reporting
- Statistical analysis
- Effective communication and soft skills
- Data mining, storing, analyzing
- Knowledge of the big data platforms
- Familiar with the cloud computing tools
- Knowledge of the data warehousing
To some extent, each one of the niches and skills mentioned above plays a significant role in a data scientist’s work. The data scientists work with and on data – they try to turn big data into useful business-wise knowledge. They work with other teams throughout the organization, for example, marketing, customer service, project management, and operations.
Their job is continuously combined with AI, machine learning, and business intelligence. That’s why a successful data scientist not only has full control over data but also understands its role in the company’s growth and development. It is important for every data scientist to have fully-developed soft skills and harness the ability to explain the big data issues in plain, understandable language.
One of the major challenges every data scientist faces is to turn messy, unstructured, disorganized data, originating from various sources into organized and neat datasets that can be used in business intelligence and easily stored, typically in a data warehouse.
According to mastersindatascience.org a good data scientist also possesses software engineering skills! That’s why we decided to start with software engineers because many data scientists started their careers as software engineers.
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From A Software Engineer To A Data Scientist
Naturally, it doesn’t happen overnight, but many software engineers see the great potential in pursuing a data scientist career. It requires a lot of effort and sacrifice, but the good news is–it can be done!
As we mentioned, a good data scientist should also have knowledge of software engineering, so consider your previous experience as a base to build on. What will make your career change easier and faster?
It would be immensely beneficial if you already knew the data analysis tools and languages such as SQL, R, Python, SPSS or SAS. Moreover, you need a solid background in statistics. That’s why many experts recommend earning a master’s degree in data or related fields. Common degrees that help you learn data science include computer science, statistics, applied math, or economics.
Compare different programs of study and curriculums from various universities in your country. Choose studies that concentrate on the data science issue. There are also many worthwhile online studies and courses. For instance, one of the universities which offer online data science courses is MIT (Massachusetts Institute of Technology). They offer an online course called Data Science And Big Data Analytics: Making Data-Driven Decisions.
Experience in the field you intend to work in is also important, as every industry or sector has its own requirements and specificity. Try to start your new career with lower positions, perhaps you’ll have to begin as an intern, but it will give you a crucial opportunity to get thorough, practical knowledge of your new expertise.
The Demand For The Data Scientists Grows
Indeed, it’s a career worth considering, as a data scientist earns an average income of slightly over 105,000 USD annually. Furthermore, the demand for this position is high. The January 2019 report from Indeed.com, showed a 29% increase in the demand for data scientists year over year and a whopping 344% increase since 2013. Additionally, as LinkedIn indicated in their report from August 2018, at the time, there was a shortage of over 150,000 people with data science skills!
The situation is likely to look similar today. Currently, there are over 10,000 openings on Indeed.com for data scientists, and this includes only opportunities in the US! Although companies look for full-time employees (94% of offers), there are also some internship positions available (over 300), which is encouraging for someone who’s at the very beginning of their “data adventure”.
As an aspiring data scientist, you should:
- Obtain a master’s degree in statistics or another data-related field
- Try to improve your skills and knowledge through various online courses
- Begin your new career with lower positions, perhaps even internships
Granted, the career of a data scientist is demanding, but it’s also rewarding. And you have a multitude of possibilities for many years to come, as this profession grows rapidly every year and it’s expected to continue doing so.
Data Scientist or Software Engineer – Which One Do You Need?
There can be only one answer–both of them! If you want to achieve big data goals, you can’t do without decent software engineers and data scientists. Are you a business owner? Do you need a data scientist? Or maybe a software engineer?
Thankfully, you don’t have to hire them! At Addepto, we have everything you need to start implementing business intelligence or machine learning in your company. Give us a call, and let’s talk about your needs. Our experienced team is always more than happy to advise you on big data and AI. We will gladly show you what big data analytics can do for your company. And then, we will implement it on your behalf!
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 Datasciencesociety.net. How to take on the Data Science career path right after college?. Dec 2, 2018. URL: https://www.datasciencesociety.net/data-science-career-path-after-college/. Accessed Feb 24, 2020.
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 Linkedin.com. August LinkedIn Workforce Report: Data Science Skills are in High Demand Across Industries. Aug 10, 2018. URL: https://news.linkedin.com/2018/8/linkedin-workforce-report-august-2018. Accessed Feb 24, 2020.