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According to Google Economist, the sexiest job in 2019 will be Data Scientist and it is proven true so far because expert data scientists are high in demand everywhere today and taking up higher salary packages too. The job role of a Data scientist is highly similar to a Software Engineer with an average salary of $137k approximately.
With almost the same salaries, do they share the same roles or responsibilities? Well, it depends on the Company how they are defining the roles. In most cases, both have a different set of responsibilities.
So in this blog we would discuss a few of the useful comparisons between.data science vs software engineering
Software Engineering is a structured approach to design, develop, and maintain the software program and avoid quality issues. It makes the requirements clear so that the development process can be easier to understand and implement.
A Software Engineer is a person who applies principles of Software Engineering to design, develop, maintain, and test the software program. The job role of a Software Engineer involves analyzing a problem, thinking, design and implement the best software solution and test it continuously. Software engineers majorly deal with complex business problems and try to find out the best solution too. The work of a Software engineer ends when he finishes implementation work for the problem.
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Data Science converts or extracts data in multiple formats to meaningful information. Businesses have the flexibility to use this knowledge to make decisions and improve overall business processes. With data science, businesses can become intelligent enough to sell or push products.
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Data scientists are data wranglers. The job role of a Data scientist involves making good data prediction on the basis of past behavior. A data scientist does not create solutions but he creates data models to generate such predictions or classifications. He tries to optimize data models so that new modes can be generated for better predictions or classifications. The work for a Data Scientist never ends because data behavior changes over time.
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It may take a lot of time when a data scientist creates its first data model. Data scientists must analyze the data, clean the data, generate new features, decide the best features, train models, try different AI or machine learning algorithms, and measure different online metrics. Each of these steps demands extra efforts and time like weeks or even months.
In this way, the work of Data Scientists does not have hard deadlines. It is tough for any data scientist to estimate when a data model can be deployed in production.
Read: Data Science Career Path - Know Why & How to Make a Career in Data Science!
The impact of IT is changing everything about science. Huge data is generated from everywhere and we need experiments for data management as it grows. Data science emerged as a solution here for data analysis, data management, etc.
Without following any discipline, designing a software solution is not possible. Software engineering defines a set of principles to create, maintain, and develop a software product without any vulnerabilities.
Read: What Exactly Does a Data Scientist Do?
In Data Science, ETL is the process for data extraction, transforming it into a logical format that is easy to understand and loading it into a system for processing. At the same time, SDLC (Software Development Life Cycle) forms the basis of software engineering. So, you can choose data science or software engineering after analyzing the requirements of client or project.
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Data Science follows the process-oriented approach and allows pattern recognition, algorithms implementation etc. Software Engineering is framework-oriented that involves Waterfall, Spiral, agile frameworks and more.
There are various tools available on the market for data science vs software engineering. This makes it easier for all types of data science software engineers professionals who are whether technical or non-technical. Both data science software engineers are the technical workforce.
Data science involves data visualization tools, data analytics tools, and database tools. Software engineering involves programming tools, database tools, design tools, CMS tools, testing tools, integration tools, etc.
Read: Data Science Course – Kickstart Your Career in Data Science Now!
The nature of frameworks or platforms depends upon the type of work that we need to perform whether it is data science or software engineering.
Data science involves platforms like Hadoop, MapReduce, Spark, Data warehouse or Flink etc. Software Engineering involves platforms like data modeling, business planning, programming, maintenance, project management, reverse engineering, etc.
To become a data science expert, the person should know how to build data products. He should have the basic knowledge of domains, algorithms, big data processing, data mining, structure or unstructured data, statistics, probability, AI, machine learning,etc.
Read: SQL- A Leading Language for Data Science Experts
To become a software engineer, the person should have the knowledge of core programming languages, testing or build tools, configuration tools, release management tools, etc.
Now you know the basic concepts of Data Science and Software Engineering, let us look at the major comparisons between the two.
Data Science Training - Using R and Python
An important observation is that software design is made by the software developer based on the requirements as identified by the Data scientist or Data engineer. So, data science and software engineering usually go hand-in-hand. It is also useful to find information and patterns about specific functions or products in data science.
Effective communication with clients or end-users helps to create more powerful business solutions because requirement gathering is the most important step in the SDLC. To know more about data science vs machine learning and its important tools and techniques, join data science certification program at JanBask Training and become a certified data scientist right away.
Read: PCA - A Simple & Easy Approach for Dimensionality Reduction
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