Data scientists and software engineers are a critical part of any company looking to innovate and find solutions to difficult problems. Both use their respective tools to perform their job duties and benefit from communicating and working together. Let’s take a moment to learn more about the role(s) of a data scientist and software engineer(with a caveat that it may vary from company to company).

The definition of what a data scientist is, is ever-evolving and lines tend to blur, but at a fundamental level, data scientists try to draw insights from structured and unstructured data using algorithms and their knowledge of statistics. They study the data and can communicate with team members in order to make major decisions, which is a key strategic practice for any business. The goal of the data scientist will also depend on the problem they are trying to solve. For example, a data scientist might be measuring the impact of changes in promotional material while working in a business context. In computer vision, a data scientist might be trying to convert handwritten math equations into digital format to be displayed on the screen.

Software engineers are more concerned with creating systems and software that can be easily used by customers and serves a specific purpose. There is a heavy analytical component to software engineering, and as such, overlaps with data science. To better understand the difference between the two, we can take a look at the Google browser on your phone. Software engineers were responsible for building the user interface that allows you to interact with the browser and perform searches or other functionality. It is very astonishing that among millions of possible sites, the Google browser is able to find results relevant to your search. This feat was performed by data scientists who developed the algorithms (code that performs a specific function) used on the Google browser to perform the search among millions of sites by filtering results based on the quality of content, the authority of the sites, and many other factors. This saves the user a ton of time because they don’t have to sift through so many sites to find a well-written, well-curated article.

Although there is something in common between the two, the differences can often be confusing for software engineers with no background in data science.

Firstly, the methodologies differ because a data scientist has a variable role within a company, depending on the problem they are trying to solve. A data scientist gathering data (referred to as a data engineer) has the responsibility of cleaning and processing it, and storing it into a database. This is commonly known as performing the Extract, Transform, Load (ETL) process. If they’re using the data to build models and perform analyses, they may be referred to as a “data analyst” or “machine learning engineer.”

The software engineering methodology is known as SDLC, or the Software Development Life Cycle. This workflow is used for developing and maintaining software and the steps include planning, implementation, testing, documentation, deployment, and maintenance. Because of these differences in methodology and approach, data scientists and software engineers use different tools to accomplish their job. Tools used by data scientists help them with data analytics, data visualization, working with databases, machine learning, and predictive modeling. The tools used by the data scientist will always depend on their role. Software engineers have tools that help them design and analyze software, test software, programming languages, web application tools, and many more. Similar to the data scientist, the tool used will depend on the task of the software engineer.

Data science also relies a lot on mathematical concepts such as derivatives, statistics, modeling, and linear algebra which are not as common in software engineering, although it depends on the role. Let us briefly discuss these classically considered “complex topics”:

**Derivatives**

Derivatives are ways of measuring changes in a variable with respect to another. Derivatives are heavily used to optimize cost functions which are used for training machine learning algorithms efficiently. Derivatives can oftentimes be seen as intimidating, but beginners should not let math notation scare them away. Behind the symbolic representation, is a clever method for thinking about rates of change among variables and finding optimal solutions.

**Statistics**

Statistics is also heavily relied upon by data scientists. It provides a robust method of analyzing problems involving data sets. Statistical computations like the average, mean, and standard deviation are very useful for data scientists studying probabilistic events. Probability plays a large role in real-world interactions, and this is where statistics is applied to derive insights.

**Modeling**

Modeling refers to the process of using a data set to find a function, or relationship, which can help predict future outcomes on a new set of data points. There are many different methods of modeling for predictive purposes, and machine learning is one way to build models that perform efficiently and effectively. Machine learning relies on statistics, so both are essential to a data scientist.

**Linear Algebra**

Linear algebra is a mathematical method used to perform operations on vectors and matrices. Vectors and matrices are mathematical objects that through linear algebra and computational power can be transformed and manipulated very efficiently and quickly. Matrix operations are essential and a necessary component in a data scientist’s toolbox. This way, data can be easily represented by vectors or matrices and subsequently transformed, visualized, and analyzed.

For many companies, data scientists and software engineers are part of a larger team. The role of each is dependent on the type of problem they are facing and thus both use different toolboxes. There is some overlap between the two fields, but beginners can often be intimidated by some of the differences. There are many benefits for software engineers to learn data science concepts that relate to their work. This benefits the entire team because, with the help of data science and software engineering, businesses are able to make data-informed decisions. This means being better able to identify an audience in a particular market, anticipate their needs, and make bigger profits.

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