The 21st century has expanded employment opportunities in the Science, Technology, Engineering, and Mathematics (STEM) fields. These employment opportunities include careers in fields as far ranging as artificial intelligence and robotics to cybersecurity and social media. In particular, the data science industry has seen exponential growth with multiple sources predicting that the industry will only continue to expand in the coming years. Many students and professionals with a STEM background have an interest in transferring their skills into a data science position or working on data science projects.
Whether it is knowledge of statistical analysis or machine learning, there are several industries and career paths which offer training that is relevant to the field of data science.
Especially for those with software engineering knowledge, there are many opportunities to transfer skills in programming, machine learning, and product development to a data science career. Additionally, there are several ways for data scientists and software engineers to work together on projects and product development, and developing data science skills offers multiple possibilities for software engineers and developers.
Software Engineering vs. Data Science
It is first important to define software engineering and data science, as well as working through the similarities and differences between the two. Software engineering is the maintenance and development of computer software. Industry professionals within the field are not only known as software engineers, but also designers, developers, and product managers. Data science is the collection, organization, and analysis of data in order to develop insights or make predictions. Those that work within the field of data science, can take on the role(s) of data scientist, analyst, and even data engineers.
In comparing the work of software engineers to data scientists there are many similarities between the two, as well as some key differences, which reflect the type of training that is required in the two fields. Both software engineers and data scientists tend to have extensive training in both mathematics and computer science, with both careers having a heavy emphasis on learning statistical analysis and various programming languages. Each of these fields relies on using multiple tools at the same time in order to produce a particular deliverable.
At the same time, data science tends to be focused more on research and using the analysis of data in order to solve problems for an institution or individual. Software engineers tend to focus on the development of a specific product or prototype for a client or company. While both careers require the creation of a deliverable, what that deliverable is can be different depending on the industry and expectations of the project. Software engineers also tend to require a more diverse set of skills as well as input from users that are not always required of data scientists. In this sense, there is just as much that data scientists can learn from software engineers as software engineers can learn from them.
Data Science Skills for Software Engineers
Due to the similarities between the fields of data science and software engineering, there are many transferable skills that software engineers can bring to data science, in addition to essential data science skills that can help expand the career of a software engineer. One of the primary similarities between the two is the cycle of developing a project or product. The following list includes some of the primary data science skills that are useful within the field of software engineering through a breakdown of how the software development lifecycle (SDLC) relates to the data science life cycle.
Identifying and Planning
Whether working on a data science project or developing a particular product or software, it is first important to identify the problem and plan out how to solve that problem. Software engineers tend to use this stage of the SDLC in order to consult with stakeholders and create different solutions for responding to the problem. Some data science skills that are especially useful at this beginning stage in software development are running risk assessments as well as collecting data on user experience and other products. Within data science, risk assessments can be created through machine learning models and running exploratory analyses of a dataset.
Especially for software engineers that are working within a field that has a high number of competitors, collecting and analyzing data on products that are similar to the product that is in development is an important aspect of the software development process. This exploratory analysis can be accomplished by collecting data about how users engage with a particular software or platform, as well as through collecting reviews on a particular software or brand through web scraping. Although research and data collection is not always an extensive part of the software engineering process, learning how to collect data can greatly improve the software development process by learning more about past products, similar products, as well as how users respond to a particular product.
Designing and Building
The next stages in the software development life cycle include creating the models or specifications for a product and then actually creating it. Within data science, there are multiple ways that you can use programming languages to create prototypes and plans for a product based on data or image processing. In addition to building prototypes, there are several machine learning and deep learning data science tools which can be used to test software and applications. However, the majority of the similarities between data science and software engineering can be seen at the stage of coding and software deployment.
Coding and Software Deployment
The coding stage is focused on the development of a product and many data scientists are trained in multiple programming languages. Both software engineers and data scientists should become well-versed in languages such as Python, Ruby, and Java. The software deployment process is then the moment in which the product is produced and/or tested. Python libraries such as Selenium are an excellent resource for both data scientists and software engineers that are interested in testing applications and software.
The software deployment stage is similar to the final stage of the data science life cycle, and software engineers can benefit from learning data visualization tools like Tableau to share information about their product, such as statistics and graphics on how a product work, or software specifications. Data visualization is not only useful for presenting the specifications of a product to stakeholders but can also be useful for communicating and advertising the benefits of a product to users.
Want to expand your knowledge of Data Science?
For software engineers that want to learn more about data science, Noble Desktop offers multiple data science classes and certificate programs that teach skills in programming and data analysis. There are also software engineering classes offered by Noble Desktop that deliver training in the cross-section between data science and web development and design. If you are interested in transitioning from software engineering to the data science industry, then taking part in the Data Science Certificate program can also help develop the skills that you need to begin a career as a Data Scientist or Data Analyst!