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Data science vs. software engineering: Key comparisons – VentureBeat

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Data science and software engineering are two important functions in managing the ever greater flows of data in an organization. As the terms connote, data science is more focused on applying scientific principles to the analysis of data, while software engineering focuses on applying the principles of engineering to the design and implementation of the related software systems.
The fields are similar in many ways and there are many overlapping roles. It’s not uncommon to find software engineers who do a bit of data science or data scientists who must engineer their software. 
But there are also key differences, and the roles are diverging. The data scientist is responsible for delivering answers, somehow, from the stream of bits. The software engineer’s job is to keep the machines running along the way. 
For example, a software engineer may construct the integrations by which real-time economic, weather, foreign currency, social media and other data is brought into an enterprise’s data operations. The data scientist may write the algorithms by which that data is used to inform product demand and supply forecasts within the organization.
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That’s a simple summary. Here’s a list of key ways that the jobs are similar and different. 
Also read: What is data science?
Data scientists and software engineers create instructions for computers and in many cases, the work is very similar.
A large part of a data scientist’s job is to gather information and prepare it for analysis. The filtering, cleaning and classification is often the largest part of the job and this work is not much different from some of the software engineering that’s done in many large systems. All software must gather input, filter it and make decisions about it. 
This part of data science is a subset of computer science and software engineering. A good software engineer will be able to do much of the gathering and filtering of data because that work requires many of the same skills as creating software for games, managing an assembly line or making a printed copy. 
Also read: Data analytics engineer: Defining the role and skill requirements
Enterprises increasingly rely on databases, data warehouses and data lakes to store and integrate massive flows of data gathered from internal and external sources. Data scientists and software engineers both rely on these and much of their work is focused on organizing these resources and putting them to use.
There are different levels of engagement. The data scientist’s main focus is the information. The software engineer’s main focus may be on other features, such as the response time or the system’s reliability; the organization of information is not their primary job.
Once the data is gathered and prepared, the work diverges. Data scientists are trained in a wide collection of mathematical and statistical techniques. They understand how scientists have developed these mechanisms to make sense from data gathered in labs and experiments over the years. Their job is to apply these techniques and mechanisms to some of the larger problems now appearing in the businesses today.
While some of the work of data scientists is to write software to prepare the data, much of this work uses tools and systems like databases or data pipelines that are already available. They can depend upon these systems to run smoothly and efficiently because they were built correctly by software engineers. 
Software engineers are trained not just to write code but to ensure that it runs correctly, quickly and efficiently. They create software that will tackle big problems because they understand how making the right decisions about the software architecture will pay off with a system that scales smoothly.  
The main goal of data science is to find useful information that can guide us to the right answers. Data scientists have the job of finding that information and analyzing it until an answer may appear. Often, machine learning (ML) is involved in extracting constantly refined results from very large datasets.
Along the way, data scientists need to do plenty of software engineering but that is not their main focus. Indeed, when the software layers function correctly — and sometimes that’s more of a dream than a reality — they can focus just on the data. 
The reason the computers exist in the first place is to organize the data. The software engineers are mostly devoted to keeping the machines and their various software layers running smoothly. Writing this code, debugging it and then tweaking it so it works effectively is their job. The data that flows through the machines is left to others. 
While their analysis can target any part of an enterprise, including obscure areas like the parameters for a manufacturing process, often a big part of data scientists’ job is helping the enterprise think strategically about the long term. Data science is one of the best tools to help managers understand how well a business is performing. The various metrics are often the only way to get good, unbiased insights into all of the sections of a company.
Data scientists play a big role in designing these metrics and ensuring that the information is accurate and available. It’s only natural that they work closely with any team that is making the strategic decisions.
Much of the work of software engineers is designing and maintaining a software stack. While the work is virtual and not as tactile as, say, overhauling an engine, it’s fair to use the phrase “hands-on” to describe many of the tasks that must be done to ensure the software is responsive to its users. From tweaking the user interface to watching for bottlenecks, the job is very interactive and dominated by finding the best practices to deliver functionality. 
This isn’t to say that it can’t be strategic. Software engineers will need to create long-term plans for the evolution of the code base. They’ll need to plan for changes in the workload and ensure the software is able to support them. All of this planning can be very strategic, especially for new companies where all of the value is contained in the stack. But when this architectural work is done, it’s time to implement the ideas, and that requires more tactics. 
Data scientists use many algorithms in their analysis, but lately some of the most exciting options have involved artificial intelligence (AI) and machine learning (ML). These algorithms can learn patterns from a training set of data and then apply them repeatedly to future examples. They are often used to classify and categorize data, which can often lead to automation and greater efficiency. For example, if some combination of details suggest a customer is close to purchasing, the AI model could automatically deploy a sales team. There are many opportunities for AI and ML algorithms to improve the workflows in an organization. 
While artificial intelligence and machine learning are important technologies that are in great demand, they aren’t as important to software engineering as they are to data science. Much of the work of software engineers involves careful programming and testing to eliminate bugs and solve problems with the most efficient combination of hardware and software possible. This generally requires attention to detail and a thorough test routine.

However, this may be changing. Some software engineers are finding that machine learning algorithms can spot opportunities for greater efficiency that humans sometimes miss. Algorithms can also identify anomalies or issues that require greater attention. Some developers are even using artificial intelligence routines to help them write software. In the future, software engineers may become some of the most devoted users of AI and ML. 
The work of writing and maintaining software stacks has grown to be such a large endeavor that school is often the last time a software developer creates something all their own. Software engineers often work in teams that may number in the thousands. They work on large, installed codebases that they could never read completely in their lifetimes. Indeed, some are working on code that was started long before they were born. Much of the work is not so much creating the code as testing it and reviewing it to make sure the code base is as consistent as possible. All of this means that software development is a process that requires teamwork and cooperation. 
Many projects in data science are new enough and small enough that they can be managed by a small team or even an independent data scientist. That isn’t to say that scientists work alone. The questions that drive the science come from the larger enterprise and the answers will be used by others in the organization to drive change. It’s just that the role of the data scientist is, as often as not, an extra one driven by management.
This is changing, though, as the work of collecting and analyzing the information becomes embedded in the workflow of the enterprise. In time, fewer and fewer data science projects will be greenfields development because the work will be revising and extending the tools that already exist. 
In recent years, many companies have built increasingly elaborate and automated data science tools. While much of the work was once writing original software to clean and filter collected data, the new, purpose-built tools are able to automate much of this work. These often-elaborate pipelines can sometimes be built completely with no-code tools with drag-and-drop interfaces, involving little hands-on work. These integrated tools are opening up the discipline to new people who lack traditional software skills. Now management teams themselves can often build data pipelines that answer most if not all of their questions. 
It’s not that better tools haven’t revolutionized the world of software engineering. The march of progress has created entire systems that automate many of the routine tasks that occupied the minds of software engineers just a few years ago. It’s just that the size and scope of the job is so large that there are often new challenges that require writing code. 
This is changing. There’s been a rise of tools that offer “low-code” or “no-code” development. While their capabilities are often overpromised by marketing teams, there’s some work that can be accomplished with little or no traditional programming. That means that software engineering teams can spend less time on traditional tasks. It’s also opening up the work to those with more business-side skills than computer-focused knowledge. 
Those who devote themselves to either data science or software engineering must pay careful attention to the workflow. The information must be gathered carefully in a timely manner to ensure that any conclusions are valid. The information should also be stored so it can be retrieved in order to complete unfinished work.
By the same token, the software engineer must be able to apply the same careful attention to the general flow of information throughout the system. While some information may need to be recorded in more detail than other information — a detailed record of mouse clicks may not be important, for example — all of these interactions must be juggled carefully so that the software is responsive, user-friendly and useful.
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