Data scientists are new in the world of computer science, while programmers have been here for a while. Now that the demand for data scientists is as high as programmers, it’s natural that we pay attention to who these professionals are, how does their role differ from a programmer, and what makes them so imperative to the technology sector. Software engineers have been winning the crown for years now, big data scientists seem to take that away.
Where’s the confusion?
Data scientists and programmers both emerge from computer science. Both roles require programming. The confusion exists because the word ‘data science’ is similar to ‘computer science’. So it seems the roles are similar. However, the roles are poles apart. Software engineers build products – web and mobile apps, develop operating systems, and design softwares that are used by organizations. Data scientists build predictive models, develop machine learning capabilities, and analyze data captured by these softwares.
Software engineers build products that generate data, data scientists analyze data to solve problems.
Software engineers are responsible for building products that make things easier for organizations. They are constantly involved in optimizing the performance of these tools and the experience of using these tools better for people. The use of these tools generates a lot of data. Data scientists take this data to solve a business problem, create actionable insights and recommendations in the form of risk mitigation and demand analysis.
Further, a software engineer may design a system for recurrent use, which use software for many years, a data scientist needs to consistently come up with new processes, deal with discrete data, and optimize processes using data.
For instance, software engineers build enterprise applications, which are used by thousands of users and generate billions of data points.
Programmers create deterministic algorithms while data scientists create probabilistic algorithms
One common thing between programmers and data scientists is both write algorithms. What’s different, however, is programmers deal with deterministic algorithms while data scientists work on probabilistic algorithms. This means the algorithms written by programmers are expected to produce the same results unless changed. Data scientists, on the other hand, work on statistics, so they are less certain about their algorithms’ output.
For instance, if you buy four items on Amazon, the total purchase would amount to a fixed value. Say each item costs $5, so the total would be $20. This is the work of a software engineer/ programmer. Data scientists’ algorithm, on the other hand, would determine if you would like to buy the fifth item. Say you buy a pack of eggs, would you also buy a loaf of bread? There’s no definite answer, but you likely will.
Programmers and data scientists have a different skillset
Programmers are focused on programming and logic. They are most likely to use SQL, Java, Python, and other programming languages to build software products. Data scientists need a gamut of skills. Programming is a small part of those skills. SQL, Java, Python, Hadoop, Spark, and more. Mathematics and statistics are most critical to excel in data science. Senior data scientists need much more and senior programmers likewise.
Data scientists work with large datasets using Excel, while programmers seldom use Excel in development. Big data scientist often deals with thousands of rows in Excel sheets. Software engineers use IDEs to write code and execute them to see results of the code. Similarly, data scientists write code in statistical software like SAS and Jupyter to perform analysis and computation. They also create documentation and visualization of their research and findings. To succeed in a data science role, a data scientist requires knowledge of several tools and technologies.
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Senior data scientists or seasoned data scientists other than knowledge of tools and technologies require strong business acumen and deep intellect. A seasoned software engineer is expected to have high proficiency in more than one programming language, architecture, microservices, product development, team management, and collaboration. A data scientist requires a keen sense of observation to ask the right questions to guide their analysis. They need a strong predilection for research and a data-driven approach to combine analysis with business goals.
In brief, software engineers are product builders for businesses, while data scientists build, optimize, improve, and solve business problems.
