The Data Science to Software Engineering Pipeline

Tech Career Hub
Written by:
App Academy
Published on:
January 9, 2023
computer screen with code on it

Discover practical steps and tips on transitioning from a career in data science to software engineering. This guide provides insights on the skills needed and the process involved in making a successful career switch.

The data science field has emerged as a major career path for students graduating from computer science programs, but some who enter the industry may start to consider how to transition from data science to software engineering.

Sometimes, certain jobs set you up for a more successful career change, and it’s definitely possible to transition from data analyst to software engineer. We’re going to dive into the data science to software engineering pipeline, so keep reading if you’re interested in making this switch to a career in software engineering.

What Does a Data Scientist Do?

Data science is a multidisciplinary field that focuses on examining structured and unstructured data using various scientific techniques and methods. Artificial intelligence (AI), machine learning (ML), data mining (DM), are just some of the tools used and explored in data science.

This field is primarily weighted toward estimation, the outcomes of data analysis, and the comprehension of these outcomes. Experts utilize algorithms and statistical analysis to gain insights from both organized and unorganized data.

A data scientist’s ultimate goal will be conditional on the nature of the problem being investigated. For instance, a data scientist in the healthcare industry may be trying to determine methods for detecting illness, predicting its progression, and tailoring treatment suggestions. A data scientist in the e-commerce industry, on the other hand, may be interested in automating the placement of digital ads by using information from previous ad campaigns.

What Does a Software Engineer Do?

Software engineering is the process of developing software by systematically applying engineering principles. It involves determining what software is needed, creating said software, and testing it to ensure it works as intended.

Dedicated software engineers support a strong software industry. They design, develop, and maintain software while making sure it performs as required. Engineers should be proficient in code quality, documentation, testing, and source control. They also need to know the ins and outs of algorithms and data structures as well as the inner workings of various programming languages and distributed systems.

To create new software, software engineers apply their programming and engineering expertise. They also work on improving and fixing existing software to make sure it meets necessary specifications and operates as expected.

A software engineer’s primary goal is the production of new software, including but not limited to applications, systems, and games. The type of work a software engineer might do can heavily depend on the company they work for.

Some software engineers may create new apps for mobile and desktop devices, while others might create the systems on which those programs run. In either case, the process entails determining what end users want before designing, developing, testing, and refining an application.

Where Do These Two Roles Overlap?

Data scientists and software engineers both have a firm grasp of computer science fundamentals, but they use these abilities for different purposes. Despite this, these two roles overlap in several ways, which is why it’s common for people in data science to transition to software engineering.

A career in data science requires many of the same abilities as those in software engineering, including the ability to code, think critically, and communicate effectively. Professions in data science often demand more specialized knowledge than software engineering does, such as advanced mathematical skills and methods of data manipulation.

Data analysts and software engineers still need some of the same technical skills including proficiency with coding and databases like R, Python, SQL, Java, and Scala. Data analysts use these to create algorithms and predictive models that can be applied to large datasets.

Both software development and data analysis have also embraced AI and automated portions of their processes as technology has advanced.

Software engineers and data scientists are both looking to improve algorithm performance by balancing the competing needs of speed and accuracy. They strive to strike a middle ground between assumptions and outcomes.

They also both deal with data in some form or another. Software engineers use it to develop, test, and maintain their systems. For data scientists, data sits at the foundation of what they do, and they use it to build insights that help drive businesses forward.

The technical skills for both roles overlap quite a bit, but so do the soft skills, including communication, curiosity, adaptability, critical thinking, and the ability to work well on a team. Both software engineers and data scientists are using their hard and soft skills to solve problems in business models.

How to Transition from Data Science to Software Engineer: Must-Have Skills

If you’re a data scientist wondering how to transition to software engineering, you’ll need to learn some of the things that wouldn’t apply to your previous career.

First, you should familiarize yourself with back-end web frameworks and the languages of those frameworks. The term “back-end” refers to the development of server-side software, which handles behind-the-scenes tasks. Some back-end coding languages include Java, Python, .NET, and Ruby.

Familiarity with front-end web technologies is also essential. HTML and CSS, for example, are used for styling, whereas JavaScript is often used for developing interactive features. Learning these languages will enable you to build a functional website or mobile application.

Data scientists may be familiar with databases, but they may need to brush up on their web development applications. You might think of them as “data vaults” for apps where all user and session data lives.

It’s also a good idea to start building a portfolio. This is essential to your transition from data analyst to software engineer since it will serve as a way to demonstrate your skills and ability to problem-solve to potential employers.

Finally, it’ll be important to maintain your soft skills. These are essential whether you’re working as a data scientist or a software engineer.

Start Your Transition with App Academy

App Academy’s curriculum is set up to give you the knowledge and skills you need to succeed in software engineering, whether you’re starting fresh or transitioning from data science. Our programs can help you develop the skills that will make you confident in your new career. Learn more about our bootcamp options or get in touch with an Admissions Specialist today.



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