Have a passion for coding? Want to change the world with your computing skills? Aiming to contribute to the domain of your choice? Finding the right career in the sea of opportunities is overwhelming. With two equally respectful and in-demand professions, Data Scientist and Software Engineer, differentiating between the two to find the most suited for you might be difficult.
Providing you with an overview, here is a full informative package on data science vs software engineering and data scientist vs software engineer available right at your fingertips.
Here’s what we’ll cover in this article:
- What is Data Science?
- What is Software Engineering?
- Data Scientist Vs Software Engineer
- Qualifications: Data Scientist Vs Software Engineer
- Skills: Data Scientist Vs Software Engineer
- Benefits: Data Scientist Vs Software Engineer
- Career Path: Data Scientist Vs Software Engineer
- Interview Kickstart: Your Choice Our Assistance
- Frequently Asked Questions on Data Scientist vs Machine Learning Engineer
What is Data Science?
Data Science is defined as “an interdisciplinary field that uses scientific methods, algorithms, processes, and systems to extract insights and knowledge from structured and unstructured data.” It uses innovative technologies that include data mining, AI, Machine Learning, and others. The prime actions performed using Data Science are data collection, analysis, exploration, and outcome prediction using the data. The professionals with expertise in Data Science are Data Scientists.
What is Software Engineering?
Software Engineering is defined as “the process of software development through the application of varying engineering principles”. It involves processes like planning, designing, building, and testing software applications to meet the requirements. The prime actions performed using software engineering are requirement analysis, system design, implementation, testing, deployment, maintenance, and version control. The professionals capable of performing software engineering are Software Engineers.
Software Engineer Vs Data Scientist
Let us compare the two professions:
| Parameter |
Data Scientist |
Software Engineer |
| Work |
Data collection, processing, and utilization for positive impact on the organization |
Create software and systems for businesses and organizations |
| Knowledge |
Machine Learning and statistics |
Coding or programming languages |
| Orientation |
Process |
Methodology |
| Responsibilities |
- Coding in SQL
- data in programs like Excel or Sigma
- Create statistical models
- Process or task automation
- Combining data analysis with company goals
|
- Process documentation
- Designing different aspects of software, like user interfaces
- Software reviewing
- Recommending upgrades or modifications
- Utilizing the potential of relevant tools and technologies
|
| Average Salary |
INR 1,32,000 |
INR 8,00,000 |
Data Scientist Vs Software Engineer: Qualifications
The stated two professions require mostly common qualifications. Yet, the requirement of in-depth focus of each varies along with some specific tools.
Data Scientist
The educational background required for becoming a Data Scientist includes getting an engineering or science degree at the graduate or postgraduate level in Computer Science, Information Technology, Mathematics, Statistics, or similar fields. The main aim is to have a candidate familiar with concepts of general and advanced mathematics, statistics, programming languages, and Machine Learning.
Software Engineer
The educational background required for becoming a Software Engineer is similar. However, the ideal candidate fit for this role is more well-versed in programming. They must be familiar with multiple programming languages or excellently well-versed in at least one. Some important and commonly required programming languages are C, JavaScript, C++, Python, R, and others.
Another crucial expectation from software engineers is knowledge about algorithms and frameworks. Hands-on experience is highly preferred in these. The ability to build projects and work in a team is sure to give a head start to the career.
Data Scientist Vs Software Engineer: Skills
You will witness multiple common and multiple unique skills for each profession. Let us check them:
Data Scientist
Technical skills: Data wrangling, preprocessing, visualization, applied mathematics, statistics, and programming in Python and SQL
Soft skills: Attention to detail, team player, willingness to learn, business acumen, analytical thinking and problem-solving
Software Engineering
Technical skills: Cloud computing, programming language, coding in any one language like Java, HTML, or SQL, and object-oriented programming
Soft skills: Multi-tasking, collaboration, problem-solving, and communication
Career Path: Data Scientist Vs Software Engineer
When you start seeking a job in a chosen career domain, you need to start at the beginner level. Here is a sequence of steps to be followed to ace your career in the stated professions.
Data Scientist
The career trajectory for a Data Scientist is working as an analyst, which can be in any sector like Data, Business, Quantitative, Systems, Marketing, or another type of analysis. The next step is to work as a Data Scientist, where you can get further promotions like Senior Data Scientist, Machine Learning Engineer, and Lead Data Scientist, followed by a subsequent topmost position of a Chief Data Officer.
Software Engineer
You can begin your career with core domains that deal with different aspects of software. For instance, it is safe to apply for jobs offering positions like front-end, back-end, full-stack engineers, graphics, game, DevOps, Software Development Engineers, or Mobile App Developers. Further positions offered in this field are Junior Software Engineer, Senior Software Engineer, Tech Lead, Team Manager, Technical Architect, and finally aim to become Chief Technology Officer.
Benefits: Data Scientist Vs Software Engineer
The benefits offered by the prestigious and in-demand career options are as follows.
Common Benefits
Opportunities: Multinational companies seek passionate and skilled individuals. There are plentiful opportunities for growth.
Versatility: Demand for professionals is not limited to a specific domain. Rather, it expands the opportunity to gain experience in different domains while sticking to core technical fields.
Higher positions: Offers possibility to get promoted to higher levels with increment in salary, cash compensation, and respect
Evolving field: The field is currently being more researched and hence promises exciting opportunities in future with more interesting and challenging roles
Remote work: Multiple companies offer remote work positions for both the professionals
Freelancing: The expertise can further be put to work through freelancing
Career transition: The skills learned in any of these fields are of relevance in any other field. It makes the career transition an easy and minimum effort-based job.
Benefits Specific To Data Scientists
Collaboration: The professionals bridge the gap between the IT sector and business decisions and requirements
Security: The dependency on data to get the work done, predict, train the models, analyze behaviors, and identify trends and patterns is irreplaceable. Hence, Data Scientists are hard to get out of the job.
In-demand Job: The Data Scientist’s job is deemed as the ‘sexiest job in 21st century’ by Harvard.
Aid business: They play an important role in the organization by contributing to decision-making, identifying opportunities for the business, automating processes, and risk mitigation.
Business option: Dealing with crucial business decisions and important matters, a great insight into the business functionality allows detailed knowledge. It opens experience-based opportunities to enter the field of business.
Benefits Specific to Software Engineers
Diversity: The career path diversity here is more, as seen with varying entry-level positions in the domain. It offers wide choices to choose a niche based on one’s interest.
Software architecture: Ability to develop the systems and software and deal with complex software systems
User satisfaction: Opportunity to work on User Interface (UI) and User Experience (UX) to optimize the user experience and enhance system accessibility, usability and responsiveness
Interview Kickstart: Your Choice Our Assistance
Recapping the two professions, becoming a software engineer is traditional and a set path to career success with numerous live examples to prove the statement. Data Science is a relatively new field that offers tremendous new opportunities due to current and constant advancements. Closely going through the fields behind these professions offers key differences between the two. While Software Engineer focuses on building and dealing with complex systems, Data Scientist contributes to organizational development and troubleshooting by leveraging the power of data.
The difference between the two professions and fields must have brought clarity about expectations from the aspiring candidates. However, the continued presence of a dilemma in choosing the right career fit for yourself is completely normal.
Having trained 17,500+ candidates, Interview Kickstart knows what it takes to land jobs at tier-1 companies. We bring you the opportunity to identify your passion and prepare for the interview in your chosen field. Your mentors here are recruiters at FAANG companies!
So, enroll in our Data Science Interview Course or Software Engineering Interview Prep Course to get ready to outshine the competitors!
Frequently Asked Questions about Data Scientist vs Machine Learning Engineer
Q1. Does a Data Scientist code?
Ans. Coding is a fundamental part of Data Scientists' daily routine. This is evident in the requirements of skilled programmers and coders.
Q2. Compare the following job roles: data analyst vs software engineer vs data engineer.
Ans. The comparison between the stated job roles is as follows:
Data Analyst: A Data Analyst focuses on data interpretation and analysis for insights.
Data Engineer: The Data Engineer designs and maintains the data architecture.
Software Engineer: They build and maintain software systems.
Q3. Is it too late to become a Data Scientist?
Ans. No, the evolution of technology in the current fourth industrial revolution requires more innovative and analytically skilled Data Scientists.
Q4. Is Python or C++ better for Data Science?
Ans. Choosing among the two, Python is preferred by a large number of organizations owing to the presence of quality libraries and tools. C++ is also in demand among institutions.
Q5. Can I transition between the fields of Data Science and Software Engineering?
Ans. Yes, the transition between the two is possible due to the presence of overlapping skills. The transition requires the acquisition of relevant additional skills and knowledge, which is easily available through both online and offline resources.
Q6. Is a Data Scientist an IT job?
Ans. Data Scientist uses IT and related technologies to use the power of Data for organizational development. Hence, it is an IT-enabled job.
Q7. Is Data Science safe for the future?
Ans. Despite advancements in Artificial intelligence, the Data Science jobs are future-proof. The developments require more technical roles capable of dealing with challenging situations that require innovative approaches possible only through human minds.
