DATA SCIENCE VS SOFTWARE ENGINEERING
The terms like Software Engineer and Data Science are now parts of daily life for people. Some people are aware of Software Engineer terms and many people have their career in that field or related to the field based on computer or computer science. But Data Science is relatively very new rather than Software Engineering. So many people are not that aware of this type of career option. In this article, there will be a discussion about Data Science vs Software Engineering in the following sections. In the end, there will be a conclusion based on this article.
Definition and background
What is Data Science?
Data Science is a study field that extracts meaningful insights from data and is based on data-driven decisions. This is more of a multidisciplinary approach that combines the practices and principles from various fields such as Statistics Mathematics, Artificial Intelligence (AI), and computer knowledge. The term comes from the ’60s and basically, it came from Statistics. The term was formalized in the 90s by professionals in computer science.
Data Scientists draw insights by using these fields and computer languages, and software applications. They derive from past data and analyze based on the requirements and predict the output.
What is Software Engineering?
Software Engineering is about developing and designing software. This is a product of two components; one is engineering and another is software. There are many steps which are involved in this field that help to evaluate the software and the various products. To excel in this field one has to be aware of various software programming, adaptability, system analysis, designing the system, maintenance, updating, and many more. The term or the field existed since the 1960s.
A Software Engineer is required for managing software for a business or for a company, to manage cost, gain higher scalability, and quality management.
Responsibilities and Skillsets
Data Science skills
● Analytics skills
● Programming proficiency
● Machine Learning knowledge
● Data Visualization
● Strong domain knowledge
The responsibilities of Data Science include many conditions and it is essential for many fields.
● The development of ML models is one of the crucial tasks for Data Scientists.
● Exploration of data and making data-driven decisions for business is very pivotal responsibly for a Data Scientist.
● Using proper recommendations from the data and the insights, a Data Scientist should be capable of making dashboards.
● Detecting fraud for credit cards or any kind of financial decision-making, is crucial.
Software Engineering skills
● Proficiency in a programming language
● Debugging and problem-solving skills
● Algorithm and Data Structure
● Version control and collaboration
The responsibilities of a Software Engineer are-
● Code and implement
● Architect and software designing
● Software upgradation and maintenance
● Mobile application development and web development are crucial responsibilities in this field.
Education and Training
When it comes to education and training, in the field of Data Science, one has to have strong domain knowledge, and a strong foundation in Mathematics, Statistics, Computer Science, or any related field (Ho et al., 2021). On the other hand, Software Engineering requires a strong computer science background, or a related field.
In the discussion of Data Science vs Software Engineering, both have facilities in online courses. Though the platforms are different from each other. There are many credits alongside online courses for these 2 fields.
The professional developments have unique characteristics in both fields. When it comes to the professionalism of Data Science vs Software Engineering, in both fields you have to be updated with recent developments. In Data Science, participating in various online competitions on platforms like Kaggle, contributes to various real-time projects experience and helps in develop in skills. Software engineers collaborate with other engineers, sharpen their knowledge and skills, there are also platforms like LeetCode, HackerEarth where they can practice and learn to enhance their coding skills.
Tools and Technologies of Data Science vs Software Engineering
Data Science makes data-driven decisions. There are varieties of tools and technologies that are used for analyzing, exploration, and data modeling (Spiegelberg et al., 2021). Those tools enable the scientists to extract insights from any complicated dataset and they build various predictive models. Some tools are discussed below-
Programming languages and libraries:
R: This programming language is known for statistical analysis which can come from extensive analytical processes and their visualization packages.
Python: This tool is used for a rich ecosystem of various libraries like Pandas, NumPy, and sci-kit-learn. They provide very impactful analysis, data, manipulation, and ML capabilities.
Data Analysis and Data Visualization Tools:
Tableau, Power Bi: These are the most powerful visualization tools which are considered for making dashboards, compelling data, and for making visualization reports.
Jupyter Notebook: Web-based, interactive code facility provider Jupyter Notebook is one of the most important environments for this field. This allows code execution, exploration of data as well as visualization.
ML frameworks:
PyTorch: This is a popular framework that is used for deep learning which provides the dynamic computation of the graphs and research purposes.
TensorFlow: This is an open-source library that supports deploying and building ML models; basically, Neural Networks.
Big Data Technologies and Cloud Platforms:
Google Cloud Platform (GCP) or Amazon Web Services (AWS): These platforms are for offering a wide range of services and ML infrastructure.
Apache Spark: A distributed and fast “data processing framework” which enables analytics and handles large-scale data.
In the discussion of Data Science vs Software Engineering, Software Engineering enrolls an assorted range of technologies and tools for supporting the testing, development, and software application deployment. This enhances the collaboration and efficiency increment in software.
Programming languages and libraries:
Java: This is a very versatile programming language that is mostly used for developing software and scalable application-building processes.
JavaScript: This is initially used for creating user interfaces and web development.
C++: This is a very powerful language for “system-level programming” and making performance applications critical.
Integrated Development Environments (IDEs):
IntelliJ IDEA: An IDE is specially built for development of Java which basically provides intelligent code completion, integrated testing frameworks, and refactoring tools (Smirnov et al., 2022).
Visual Studio Code: This is a highly extensible and lightweight IDE that can support various programming languages and creates advanced code and edit it with debugging capabilities.
Collaboration Tools and Version Controls:
Bitbucket or Github: These platforms are built on Git which imparts the hosting services to control repositories, collaboration, and sharing code among the group members who are related to Software Engineering.
Git: This distributed version control system allows the Software Engineers for tracking change, manage codes, and collaborates efficiently for various problems and solutions.
Continuous Integration Tools and Software Testing:
Travis Cl or Jenkins: These tools ease the Continuous Deployment (CD) and Continuous Integration (CI) process, test, automating building and pipelines of deployment.
Selenium: This is a very popular automation testing framework to make web applications.
Challenges and future trends for Data Science vs Software Engineering
Challenges
● Data Science vs Software Engineering faces different and unique challenges between them. The challenges are based on innovation and adoption. The ethical consideration of AI is one of the great issues in the Data Science domain (Wang et al., 2022). There should be transparency for making data-driven decisions.
● In the field of Software Engineering, there are scalability and complexity for significant challenges. Distributed systems, cloud computation, and big data formation produce significant challenges. There are also ML challenges out there for software engineers (Giray, 2021). They will face Inclusion and Exclusion criteria for different management aspects and future trends.
The future trends
Data Science vs Software Engineering have different kinds of future trends but those trends are related to the innovation of new technologies, software, knowledge, etc.
Data Science trends-
● AutoML
● Augmented Analytics
● ExplainableAI(XAI)
● Privacy-Preserving Techniques
Software Engineering trends-
● No-Code Development
● CD/CI and DevOps
● Cloud-Native Development
● AI development
Conclusion
It can be concluded from the discussion of Data Science vs Software Engineering that Data science is a research area that essentially draws insightful conclusions from data and bases decisions on that information. Software development and design are the focus of software engineering. This is the result of two parts, one of which is engineering and the other of which is software. Data exploration and business decision-making using data are highly important responsibilities for a data scientist. The key tasks for this profession are web development and mobile application development. One of the major problems in the field of data science is AI. Data-driven decision-making should be transparent. Scalability and complexity provide important obstacles in the field of software engineering. Big data generation, cloud computing, and distributed systems present formidable obstacles.
