Software Engineering is one of those signature terms that has existed ever since Computer Science came into existence and people made coding their profession. Data Science is the term that has recently come to light but has spread like wildfire considering the exposure and applicability it has concerning the rapidly growing data industry. There are times when Software Engineers work hand in hand with Data Scientists and there are times when they are entirely different verticals. So, there are places where they shake hands but they definitely have a lot of differences between them on several grounds.
You want to understand a huge amount of data, no matter if it is structured, semi-structured, or unstructured data, Data Science is what you need. Data Science is that segment of Computer Science that uses statistics, probability, and other scientific processes to analyse and visualise data.
Talking about the approaches and technologies that Data Science uses to make sense out of chunks of data include Machine Learning, Artificial Intelligence, Data Mining, and Data Transformation, Data Analysis, etc. The main concept behind Data Science is not just analysing and visualising data, but also making the entire process accurate and optimised.
Hence, a Data Scientist is someone who has a top-notch grip over concepts like mathematics, statistics, and Machine Learning to come up with innovative business solutions. No doubt, companies are in search of talented Data Scientists who could not only save time, money, and resources for them but also take them a step ahead in this volatile market.
Now, when you know about Data Science, it is time to check the second term of the debate, Software Engineering.
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Software Engineering is the process of developing a software application utilising the engineering principles and principles of programming languages. The process includes several steps starting from planning and going up to testing to ensure a bug-free running of an application.
It is a structured approach to designing and developing a software application that is of the best quality. Also, the requirements are jotted at the beginning of the process so that there is no lag in the designing and developing process.
Hence, a Software Engineer is someone with an amazing knowledge of coding languages and has a firm grip over topics like Software Development, Operating Systems, and Computer Programming. They build software, fix already existing bugs, and help with Infrastructure and Testing. Furthermore, they can be of assistance in developing software in different domains like Games, Operating Systems, Payment Gateway, Business Software etc.
Now, when you would have understood the basics of Data Science and Software Engineering, let’s have a look at how Data Science is different from Software Engineering.
Data Science and Software Engineering both require a firm grip over programming skills, but while the first is used to gather and process data, the latter is used to develop software applications and features according to user demands.
Let’s have a look at the differences between the topics:
Data Science methodology suggests that to solve any problem or answer a complex question there are a set of steps to be taken. The steps go on from collecting data to finally building and deploying the required model
Software Engineering methodology talks about a well-elaborated and structured plan to develop a software project. The process of Software Engineering involves systematic steps in developing software from planning to deployment and maintenance.
Data Science proves its worth when it comes to making sense and taking great data-driven decisions out of chunks of data. Today, Data Science is being used by companies in different domains like marketing, finance, healthcare, banking, and many more.
Software Engineering marks its importance whenever there is a software application to satisfy any particular need. Today, it is being used by almost every industry and business to ensure effective and efficient working.
There are diverse varieties of tools used in Data Science like Data Visualisation tools, Data Analysis tools, and Database tools. Some widely used Data Science tools include MS Excel, MATLAB, Tableau, Apache Spark, etc.
There are several types of tools used in Software Engineering like Design tools, Analysis tools, Database management tools, Coding languages tools, Web applications tools, and Integration and testing tools. Some popular Software Engineering tools are GitHub, Jenkins, Docker, Eclipse IDEs, JetBrains IDEs etc.
Data Science uses process-oriented approaches ranging from statistical analysis to algorithms implementation. It uses pattern recognition and machine learning to derive insights from structured and unstructured data. The first step is to identify the problem and an approach to fix it. Then, the relevant data is collected in the required format. Once the data is collected, that data is sorted and what’s important is fetched out of that data. Moving on, models are generated and data is evaluated using them. Finally, the model is deployed and feedback is processed.
Software Engineering follows a systematic approach to developing software or a product. There are several models used in Software Engineering to have a systematic process, like Waterfall Model, Spiral Model, or an Agile Model.
Top required skills for Data Science or to be a successful Data Scientist include:
Top required skills for Software Engineering or to be a successful Software Engineer include:
Roles and responsibility
Roles and responsibilities of a Data Scientist include:
Roles and responsibilities of a Software Engineer include:
Data Sources refer to the sources where the data originates. It can be referred to as the initial phase, the place where the raw data comes from. Data Sources in the case of Data Science would include Baking systems, Machine Log data, Social media like Twitter and Facebook, Transactions, etc.
Data Sources in the case of Software engineering would include the needs of the end-user, feature development, and several tracking systems like bug tracking systems or Source Code Management systems (SCM).
Now, You know what is the difference between Software Engineering and Data Science, let’s have a look at the qualification constraints for the two.
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Qualifications required for Data Science
Data Science is a field that is being pursued by people from diverse fields today. Not just B.Tech or M.Tech professionals in Computer Science or Information Technology, but people from B.Sc, M.Sc, and even MBA can be seen pursuing Data Science as a career.
Anybody interested in Data Science can opt for it, all they need is a good grip over skills like Mathematics, Probability, Statistics, Programming or coding languages, and Machine Learning.
So, in short, anybody who has an engineering degree or has a degree in mathematics or statistics can become a Data Scientist.
Qualifications required for Software Engineering
Software Engineer is a job role that specifically belongs to Computer Science or Information Technology. So, anybody who has a degree in the latter two can opt for Software Engineering. Post a degree, the person must have good knowledge of programming languages, at least a few of them. Most common programming languages today include Python, Java, C, C++, etc
Also, knowing about these languages is not enough, they must be well versed in coding, syntaxes, and debugging. Apart from programming, they must be well versed in Data Structures and Algorithms.
So, in short, anybody who has a Bachelor’s degree in Computer Science or a related field can think of pursuing a career as a Software Engineer. All they need after a required degree is an excellent grip over Data Structures and Programming Languages.
Now, when you know about what it takes to be a Data Scientist or a Software Engineer, let’s move forth to discover different Career paths in Data Science and Software Engineering.
Career paths for Data Science
Data Scientist is one of those job roles that are needed by companies or organisations (irrespective of their domains) to scale their business and stand firm in this competitive world. After all, there is nobody else who can make sense out of chunks of data and make reasonable decisions accordingly.
There are a lot of designations that a Data Scientist can fit in, but the four most popular of them are:
Data Scientist is the most important role out of all the Data Science career paths. A Data Scientist is someone who handles the chunks of data from scratch, i.e. begins with collecting the data, sorting or analysing it, and finally interpreting it. The designation is a mixture of several complex roles including mathematician, statistician, programmer, and scientist.
Data Engineers are one of the most well-paid professionals across the technical domains, sometimes even paid higher than Data Scientists. They are IT professionals who play around with data. It is their responsibility to produce a Machine Learning model and a Data Processing system.
Data Analyst is one of the most sought-after jobs in the world today as every company needs an efficient person to analyse all the data they have been producing over the years and continue to process today. So, a Data Analyst is someone who analyses the data, figures out useful insights, and then provides a solution for the problem.
Business Analyst/ Business Development Engineer
A Business Analyst or a Business Development Engineer is someone responsible for evaluating all the data related to a business. They look after the historic data, draw insights from it and try to implement their findings in the present scenario to eventually scale the business and improve their decision-making capabilities.
Career paths for Software Engineers.
Software Engineers is one of those job roles that have never-ending designations. Considering the diverse variety of skills they have, from technical to managerial skills, there are several career paths that a Software Engineer can go for.
Software Engineer is one of the most signature jobs in the Software Engineering domain. He/she is someone who utilises the teachings of Software Engineering to develop successful software following all the steps of the Software Development Life Cycle (SDLC).
Application Developers as the name suggests are the professionals who build an application with respect to the needs of the client. They are in constant touch with the clients and keep on conveying the needs of the client to the engineering team so the product is all improved or re-designed if needed.
Full-Stack Developers are the developers who work with the code both on the front-end as well as the back-end. They have such a diverse background that they can code in both spaces, one that is user-friendly and the second the abstracted part of the software.
Tech Lead is someone who leads the technology team, i.e. oversees them and keeps a track of all the tasks happening in the team. At times, they not only look after their team but hire and train employees to be a part of their team and perform tasks accordingly.
Other career paths that a Software Engineer can pursue include - Technical Writer, Technical Recruiter, QA Engineer, DevOps Engineer, Business Analyst, Data Engineer, Big Data Engineer, and Chief Technology Officer (CTO).
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It won’t be right to vouch for one of the two. Both, Data Science and Software Engineering have their own set of skills required. So, it completely depends on the interest of an individual. There could be a difference in salaries and opportunities but then at the end of the day, it is about interest.
You will have to code in both Data Science and Software Engineering, but that being said there are a lot of differences between the two. If you want to code, develop applications, and debug whenever necessary, then Software ENgineering is your thing. But, if you want to play with huge chunks of data and go from nowhere to having some valuable insights from that data, then Data Science is something you are searching for.
Both the fields have ample amount of jobs and considering the fast-paced environment it would not be wrong to believe that as the companies begin to grow, they will need Software engineers to make excellent applications and products, and will need Data Scientists to handle the chunks of data they are producing every day.
By now, you would be well versed with everything you need to distinguish between the two amazingly popular terms - Data Science and Software Engineering. You began with learning the basics of the two and once you knew their basics you went on to differentiate between them.
While you were checking the differences between Data Science and Software Engineering, you checked several parameters to differentiate between them. While one is more oriented toward Coding and Data Structures, the other one is a blend of coding, mathematics, statistics, and analysis.
Once you knew the differences between Data Science and Software Engineering, you also saw the qualifications needed to be a Data Scientist, followed by the qualifications needed to be a Software Engineer.
Furthermore, you discovered several career paths with respect to both domains. Finally, finishing up with what would be a better fit for you - Data Science or Software Engineering.
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It would be wrong to say that Data Science is easier than Software Engineering. In fact comparing the two domains in terms of easiness won’t be correct, as they both have their own set of specific skills and challenges. While Software Engineering requires an excellent knowledge of coding languages, Data Science requires a firm grip over mathematics, statistics and analysis.
There might be a difference in salaries between both the domains but there is no difference in demand between the two. Both Data Science and Software Engineering are in great demand today and companies are in search of potential employees who have skills in any of the domains.
It is definitely possible to transition from being a Data Scientist to a Software Engineer. If you are thinking of changing boats it must be noted that the two roles are entirely different, so if you want to become a Software Engineer, the programming skills of a Data Scientist can just provide a base, but if you want to excel in your new domain, you will have to develop excellent coding skills and learn about Data Structures and Algorithms too.
Software Engineers and Data Scientists are two popular domains in the market today and both of them have extremely competitive salaries. It would be wrong to say that one earns more than the other, though it fluctuates based on experience and the job location.