Differences Between Data Scientist vs Software Engineer
A Data Scientist is a professional analytical data expert who has the technical skills to solve complex problems and also finds the way to explore what problems actually need to be solved. And they are responsible for collecting a data, analyzing it and explain large amounts of data to identify different ways to help and improve operations which makes gaining a competitive edge over rivals.
Data scientists will be having knowledge of mathematics, and they are a computer scientist and also part of trend-spotter. And, they are good at both business and IT worlds.
Data Scientist explains what is going on by processing history of the data and they also use various advanced MLA(machine learning algorithms) to identify the occurrence of an event in the future which helps to make decisions and predictions making use of this predictive causal analytics and prescriptive analytics to improve business and operations. For this process, Data Scientist has to look into data from many angles.
A software engineer is a person who has a knowledge and applies the disciplined, structured principles of software engineering to all the levels – design, development, testing, maintenance, and evaluation of the software that will avoid the low quality of the software product.
Software engineers recommend the latest computer software and operating systems, such as iOS on iPhones and Windows 10 to suit those requirements. And they are responsible for creating models and diagrams of the computer code, knowledge of technologies are necessary for these professionals.
Software engineers should have skills like technical expertise, demonstrable achievement and also experience with using open source tools. They should be knowledgeable and experienced with pattern design techniques, automated testing process, and fault-tolerant systems. Software engineers should also know how to create and maintain IT infrastructures, large-scale data stores as well as cloud-based systems.
Head to Head Comparison Between Data Scientist vs Software Engineer
Below is the Top 8 Comparison Data Scientist vs Software Engineer
Key Differences Between Data Scientist vs Software Engineer
Below are the most important Differences Between Data Scientist vs Software Engineer
1. A Data Science consists of Data Architecture, Machine Learning algorithms, and Analytics process, whereas software engineering is more of disciplined architecture to deliver a high-quality software product to end user.
2. The data scientists are the one who analyses the data and makes that data into knowledge which helps in business, software engineers are the one who is completely responsible to build the software product to end user.
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3. Growth in the field of Big Data is an input source for the data science, whereas in software engineering, demanding of new features and functionalities in the market or clients, are driving to design and develop new software(s).
4. By analyzing and processing the data, Data scientist helps to make good business decisions; whereas software engineering makes the life easy by developing required software products.
5. Data science process is driven by data; the software engineering process is driven by end-user requirements.
6. The data extraction process is the basic & necessary step in data science; Requirement gathering and designing as per requirement is an important role in software engineering.
7. With an increase in data generation, it is observed that data engineers emerge as a subnet, within the software engineering discipline. A data engineer builds systems that consolidates all data, store and retrieve data from the various systems and applications built by software engineers.
8.An example for Data science: A suggestion about similar products in E-commerce website (Flipkart, Amazon, etc.); the system automatically processes our search/products we browse and give the suggestions according to that.
9. For software engineering, let’s take an example of designing any applications which helps to improve business and which is collected by user feedback.
Data Scientist vs Software Engineer Comparison Table
Below are the lists of points, describe the comparisons Between Data Scientist vs Software Engineer
| Basis for Comparison |
Data Scientist | Software Engineer |
| Importance | Nowadays, loads of data are coming from multiple areas/fields. Hence as data grows, expertise needed to analyze, manage and make it a useful solution for business/ operation. | Software Engineer is very much necessary to understand the requirement and delivery the software product to end users without and vulnerabilities. |
| Methodology | Methodologies for Data Scientist are similar to ETL process. As same as in the ETL process, data from different multiple & hetero- generous data sources, transforming and cleansing will be performed on it, which makes to load cleansed data into DW systems for further processing. |
For Software engineers, SDLC (Software Development Lifecycle) is the base which consists of requirements gathering, software design, development, QA process and software maintenance. |
| Approach | Approach for Data Scientist is Process Oriented: -Algorithms implementation -Pattern recognition -Data visualization -Machine learning –Text analytics, etc. |
Approach for a Software engineer is Framework/methodology Oriented: -Waterfall -Spiral -V&V model -Agile, etc. |
| Tools | Data Analysis tools, Data visualization tools and also database tools. |
Design and Analysis Tools, Database Tools, Programming Languages Tools, Web application Tools, Project Management tools, Continuous Integration Tools, and test management Tools. |
| Eco-system, platforms, and Environments | Big data is a foremost ecosystem for Data scientist and also Hadoop, Map Reduce, Apache spark, data warehouse and Apache Flink. | Mainly includes : -Business planning and modeling process, -Analysis and designing a software, -Code development, -Developing Programming, -Testing -Maintenance and -Project management |
| Required Skills | – Domain Knowledge, – Quantitative analysis – Programming knowledge – Scientific and Business knowledge. – Data Mining, – Machine learning language – Big Data processing, Structured & Unstructured Data (SQL and NoSQL DBs), – Probability and Statistics – Communication. Overall knowledge about how to build data products and visualization to make data understandable |
– Analyzing and Understanding and User requirements, – Core programming languages (like C, C++, Java etc.), – Data modeling skills. – Testing a software, – Configuration tools (Chef, Puppet etc.), – Build and release management skills. – Project Management skills. |
| Roles and Responsibilities | Data scientist, Business Analyst, Data Analyst, Data Engineer and also Big Data specialist. | Analyzing user requirement. Designer, Developer, Build and Release Engineer, Test engineer, Data Engineer, Product managers, Administrators and cloud consultants. |
| Data Sources | Almost all website data can be considered for data source. Social Media, Business Apps, Transactions, Sensor Data, Machine Log Data etc. |
User requirements, New features developments and also demand for the some functionalities etc. |
Conclusion – Data Scientist vs Software Engineer
A Data Scientist is always more focused on data and hidden patterns, data scientist develop their analysis on top of data. Data Scientist work includes Data modeling, Machine learning, Algorithms, and Business Intelligence dashboards. But software engineer builds software applications. And they will be involved in all stages of SDLC process from design to review with clients.
There is very important observation is that the software application build by a software engineer will be based on the requirements identified by Data engineer or Data Scientist. So the Data science and the software engineering in a way go hand-in-hand.
The conclusion on this is, ‘Data science’ is “Data-Driven Decision”, to make good decisions in business, whereas software engineering is the disciplined and structured methodology for software development without deviating from user requirement.
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