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Difference Between Data Science vs Software Engineering

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Head-to-Head Comparison Between Data Science vs Software Engineering (Infographics)

Below are the top 8 Comparisons between Data Science vs Software Engineering:

Key Differences Between Data Science vs Software Engineering

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

Data science comprises Data Architecture, Machine Learning, and Analytics, whereas software engineering is more of a framework to deliver a high-quality software product.

The data analyst is the one who analyses the data and turns the data into knowledge; software engineering has a Developer to build the software product.

The rapid growth of Big Data is an input source for data science. In software engineering, demanding new features and functionalities drives engineers to design and develop new software.

Data science helps make good business decisions by processing and analyzing the data, whereas software engineering structures the product development process.

Data science is similar to data mining, 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.

Data drive data science; the end-user needs to drive software engineering.

Data science uses several Big-Data Ecosystems and platforms to make patterns out of data; software engineers use different programming languages and tools, depending on the software requirement.

Data extraction is vital in data science; requirement gathering and designing are essential in software engineering.

A Data Scientist focuses more on data and its hidden patterns; a data scientist builds analysis on top of data. Data Scientist work includes Data modeling, Machine learning, Algorithms, and Business Intelligence dashboards.

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.

As more and more data is generated, 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.

Software engineering refers to the application of engineering principles to develop software. Software engineers participate in the software development lifecycle by connecting the client’s needs with appropriate technology solutions. Thus, they systematically create a process to provide a specific function. In the end, software engineering means using engineering concepts to develop software.

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.

Historical data will help find information and patterns about specific functions or products in data science.

Communication with the clients and end-users helps to create a good software development life cycle in software engineering, mainly because it is essential for the requirement gathering faced in SDLC.

One example result for Data science would be a suggestion about similar products on Amazon; the system processes our search and the products we browse and gives tips.

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; the requirement started, and so did design and development.

Data Science vs Software Engineering Comparison Table

Basis of Comparison Data Science Software Engineering

Why? I Importance

As data grows, so does the expertise needed to manage, analyze, and make good insights into it. The data science discipline has emerged as a solution.

Without following, specific disciplines creating any solution would be 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.

Ecosystem, platforms, and Environments Hadoop, Map R, spark, data warehouse, and Flink Business planning and modeling, Analysis and design, User-Interface development, Programming, Maintenance, 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, demand for particular functionalities, etc.

Conclusion

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