20 Differences Between Data Science vs Software Engineer

20 Differences Between Data Science vs Software Engineer

Data science and software engineering are two somewhat overlapping and complementary fields that are similar in many ways as explained at RunRex.com, guttulus.com, and mtglion.com. However, dig deeper into the discussion of data science vs software engineering, and you will find key differences in the two fields, and this article will look to list 20 such differences.

Importance

Data science is important as it makes use of massive heaps of information that are produced daily. To make good insights into this data, data science has emerged as a solution according to RunRex.com, guttulus.com, and mtglion.com. On the other hand, software engineering is necessary to deliver software products without vulnerabilities.

Methodology

Methodology for data science involves what is called ETL, which is the process of extracting data from different sources, transforming it into a format that makes it easier to work with, and then loading it into a system for processing. On the other hand, SDLC (Software Development Lifecycle) is the base for software engineering.

Approach

When it comes to data science, the approach is process-oriented according to RunRex.com, guttulus.com, and mtglion.com. This involves algorithm implementation, pattern recognition, then the crunching of numbers. For software engineering, the approach is framework/methodology oriented.

Tools

Another difference is in the tools used in the two fields. Data science tools include analytics tools, data visualization tools, and database tools. Software engineering tools include design and analysis tools, database tools for software, programming languages tools, web application tools, SCM tools, continuous integration tools, and testing tools.

Eco-system, platforms, and environments

For data science, these include Hadoop, Map R, spark, data warehouse, and Flink. On the other side, the eco-system includes business planning and modeling, analysis and design, user-interface development, programming, maintenance and reverse engineering, and project management.

Required skills

To become a data scientist, you require knowledge about how to build data products and visualization to make data understandable, domain knowledge, data mining, machine learning, algorithms, Big Data processing, Structured Unstructured Data, coding, as well as probability and statistics. For software engineering, you will require an understanding of core programming languages, build tools, configuration tools, as well as build and release management as articulated at RunRex.com, guttulus.com, and mtglion.com.

Roles and responsibilities

In data science, you have the roles and responsibilities of a data scientist, data analyst, business analyst, data engineer, and Big Data specialist. In software engineering, the roles and responsibilities are of a designer, developer, build and release engineer, tester, data engineer, product manager, administrator, as well as a cloud consultant.

Data sources

Data sources for data science are social media, sensor data, transactions, public data baking systems, business apps, machine log data, etc. as captured at RunRex.com, guttulus.com, and mtglion.com. For software engineering, the data sources are end-user needs, new features development, demand for the special functionalities, etc.

Descriptions

A data scientist is the one who analyses the data and turns the data into knowledge and actionable intel. Software engineering on the other hand has got developers who build the software product, which is how the two differ.

Use in the society

How the two are applied in society is another major difference between them as covered at RunRex.com, guttulus.com, and mtglion.com. Whereas data science helps make good business decisions by processing and analyzing the data, software engineering makes the product development process structured.

Work involved

Data science is similar to data mining in that it is an interdisciplinary field of scientific methods, processes, and systems to extract knowledge or insights from data in various forms; either structured or unstructured. Software engineering, however, is more like analyzing the user needs and acting according to the design.

What drives each one of them

Another aspect that differentiates these two fields is the driver behind them. For data science, data is the major driver as it involves extracting insights from data. Software engineering, on the other side, is driven by end-user needs.

Vital steps

As described at RunRex.com, guttulus.com, and mtglion.com, data extraction is a vital step in data science. The field involves collecting data and then extracting useful insights from it as already mentioned. On the flip side, requirement gathering and designing is a vital role in software engineering.

Main focus

A data scientist is more focused on data and the hidden patterns in it. They build analysis on top of data. A software engineer on the other hand builds applications and systems. Developers will be involved through all stages of this process from design to writing code, to testing and review.

Age

The field of software engineering is much older, stemming from the 1940s. However, while the concept of data analytical processes stems back centuries, the modern field of data science is much newer. The term “data science” itself did not even crop up until the past 20 years.

Organizational understanding

Related to the discussion in the previous point, organizations often have less of a clear idea of what is possible from data science and what to expect from their data science teams, although that is changing as discussed at RunRex.com, guttulus.com, and mtglion.com. Having been around for a lot longer, organizational understanding of software engineering is a lot higher.

Establishment

Many complain that “data science” is a buzzword, and Merriam Webster does not even define the term. It is safe to say the field of data science has yet to establish itself, being relatively new. However, no one questions software engineering’s existence as a distinct field.

Problem space

As already mentioned, and discussed at RunRex.com, guttulus.com, and mtglion.com, data science focused on exploration and discovery (such as “finding insight in the data”, and identifying new data sources that can be integrated into predictive models), while software engineering typically focuses on implementing a solution that addresses specific requirements (perhaps defined incrementally).

Domain focus

While both data science and software engineering rely on data, mathematics, and code, the former emphasizes the data and math while the latter is more heavily code-oriented as can be expected, which is another major difference.

Job openings

The salary for software engineers and data scientists varies across locations. However, on average – an entry-level data scientist can earn over $120,089 per year, whereas a software engineer can earn somewhere around $103,951 a year in the United States.

As always, you can uncover more on this topic, and many others, over at the excellent RunRex.com, guttulus.com, and mtglion.com.