Data Science vs Software Engineering

Difference Between Data Science and Software Engineering

Data science, in simpler terms converting or extracting the data in various forms, to knowledge. So that the business can use this knowledge to make wise decisions to improve the business. Using data science, companies have become intelligent enough to push and sell products.

Software engineering is a structured approach to design, develop and maintenance of software, to avoid the low quality of the software product. Software Engineering makes the requirements clear so that the development will be easier to proceed. so let us understand both Data Science and Software Engineering in detail in this post.

Head to Head Comparison Between Data Science and Software Engineering (Infographics)

Below is the top 8 Comparisons between Data Science vs Software Engineering

Key Differences Between Data Science and Software Engineering

Let’s look at the top differences between Data Science vs Software Engineering

1. Data science comprises of Data Architecture, Machine Learning, and Analytics, whereas software engineering is more of a framework to deliver a high-quality software product.
2. The data analyst is the one who analyses the data and turns the data into knowledge, software engineering has Developer to build the software product.
3. The rapid growth of Big Data is acting as an input source for data science, whereas in software engineering, demanding of new features and functionalities, are driving the engineers to design and develop new software.
4. Data science helps to make good business decisions by processing and analyzing the data; whereas software engineering makes the product development process structured.
5. Data science is similar to data mining, it’s 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 is more like analyzing the user needs and acting according to the design.
6. Data science is driven by data; software engineering is driven by end-user needs.
7. Data science uses several Big-Data Ecosystems, platforms to make patterns out of data; software engineers use different programming languages and tools, depending on the software requirement.
8. Data extraction is a vital step in data science; requirement gathering and designing is a vital role in software engineering.
9. A Data Scientist is more focused on data and the hidden patterns in it, data scientist builds analysis on top of data. Data Scientist work includes Data modeling, Machine learning, Algorithms, and Business Intelligence dashboards.
10. A software engineer builds applications and systems. Developers will be involved through all stages of this process from design to writing code, to testing and review.
11. As more and more data is generating, there is an observation that data engineers emerge as a subnet within the software engineering discipline. A data engineer builds systems that consolidate, store and retrieve data from the various applications and systems created by software engineers.
12. Software engineering refers to the application of engineering principles to develop software. Software engineers participate in the software development lifecycle by connecting the clients’ needs with applicable technology solutions. Thus, they systematically develop a process to provide a specific function in the end, software engineering means using engineering concepts to develop software.
13. There is an important observation is that the software design made by a software engineer is based on the requirements identified by Data Engineer or Data Scientist. So Data Science and software engineering in a way go hand-in-hand.
14. Historical data will be useful for finding the information and patterns about specific functions or products in data science.
15. Communication with the clients and end-users helps to create a good software development life cycle in software engineering, especially it is very important for the requirement gathering face in SDLC.
16. One example result for the Data science would be, a suggestion about similar products on Amazon; the system is processing our search, the products we browse and give the suggestions according to that.
17. In the case of software engineering, let’s take the example of designing a mobile app for bank transactions. The bank must have thought or collected, the user feedback to make the transaction process easy for the customers; there the requirement started so does design and development.

Data Science vs Software Engineering Comparison Table

Below is the topmost comparison between Data Science vs Software Engineering

The Basis Of Comparison	Data science	Software Engineering
Why? I Importance	The impact of ‘Information Technology’ is changing everything about science. Loads of data coming from everywhere. As data grows, so does the expertise needed to manage it, to analyze this data, to make good insights for this data, data science discipline has emerged as a solution.	Popular Course in this category All in One Data Science Bundle (360+ Courses, 50+ projects)360+ Online Courses | 1500+ Hours | Verifiable Certificates | Lifetime Access 4.7 (3,220 ratings) Course Price View Course Related Courses Data Scientist Training (76 Courses, 60+ Projects)Tableau Training (4 Courses, 6+ Projects)Azure Training (5 Courses, 4 Projects, 4 Quizzes)Hadoop Training Program (20 Courses, 14+ Projects)Data Visualization Training (15 Courses, 5+ Projects) Without following, certain disciplines creating any solution, would prone to break.  Software Engineering is necessary to deliver software products without vulnerabilities.
Methodology	ETL is a good example to start with. ETL 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.	SDLC (Software Development Lifecycle) is the base for software engineering.
Approach	Process Oriented	Framework/methodology     Oriented
	Algorithms implementation	Waterfall
	Pattern recognition	Spiral
	Crunch numbers	Agile
Tools	Analytics tools, Data visualization tools, and database tools.	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	Hadoop, Map R, spark, data warehouse, and Flink	Business planning and modeling, Analysis and design, User-Interface development, Programming, Maintenance, and reverse engineering and Project management
Required Skills	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(SQL and NoSQL DBs), Coding, Probability and Statistics	Understanding and analyzing User needs, Core programming languages(C, C++, Java, etc), Testing, Build tools(Maven, ant, Gradle, etc), configuration tools(Chef, Puppet, etc), Build and release management (Jenkins, Artifactory, etc)
Roles and Responsibilities	Data scientist, Data Analyst, Business Analyst, Data Engineer, and Big Data specialist	Designer, Developer, Build and Release Engineer, Testers, Data Engineer, Product managers, Administrators, and cloud consultants.
Data Sources	Social  Media(facebook, twitter, etc), Sensor Data, Transactions, Public Data Baking systems, Business Apps, Machine Log Data, etc	End-user needs, New features development, and demand for the special functionalities, etc.

Conclusion

The conclusion would be, ‘Data Science’ is “Data-Driven Decision” making, to help the business to make good choices, whereas software engineering is the methodology for software product development without any confusion about the requirements.

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