With an array of career options, all that matters is choosing the right career path. The right career path for one depends on their skill set, interest, job availability in that field, and, most importantly, your passion for the same. Speaking of job vacancies, the two careers have high demands till date and in upcoming years are Data Scientist and a Software Engineer.
Per the BLS, the expected growth rate of job vacancies for data scientists and software engineers is around 22% by 2030. Although both Data Science and Software Engineering domains focus on math, code, data, etc., there are many minute differences between them.
Several questions come up in the mind of a student. Is mastering data science beneficial or building software is a better career option? This article clearly explains the topics of data scientist vs software engineer, including data scientist vs software engineer salary, with relevant information which hopefully leads the readers to a doubt-free decision.
What is Data Science?
Data Science is a field of study that handles large volumes of data using technological and modern techniques. This field uses several scientific procedures to understand structured, semi-structured, and unstructured data.
It entails using various technologies, including data mining, data transformation, and data cleansing, to examine and analyze that data. Both data science and software engineering rely largely on programming skills. However, data scientists are primarily concerned with working with massive datasets.
Data Science is strongly influenced by the value of accurate estimates, data analysis results, and understanding of those results. Data scientists, like software engineers, strive to optimize algorithms and handle the trade-off between speed and accuracy.
More data has been generated in the actual world in the last two years than in the complete history of humanity. Data is an important feature for any organization because of its ability to guide decision-making based on facts, statistical numbers, and trends. Data Science is a notion that entails data collection, processing, and exploration, which leads to data analysis and consolidation. Get to know more about SQL for data science.
Daily tasks of a Data scientist may include:
- Frame the problem.
- Get data and analyze the problem.
- Make error corrections in the data.
- Conduct an in-depth analysis.
- Share the results of the analysis.
What is Software Engineering?
Software engineering is the process of creating software by systematically applying engineering principles. For example, a software engineer analyses user requirements before designing, building, and testing software applications to ensure they meet them.
As per reputed German computer scientist, Fritz Bauer, software engineering is "the establishment and use of sound engineering principles to obtain economically software that is reliable and works efficiently on real machines."
Successful engineers understand how to use suitable programming languages, platforms, and structures to create everything from game consoles to network systems. In addition to developing, one's own devices, software engineers test, improve and preserve software developed by others.
Daily tasks of a software engineer may include:
- Software system design and maintenance
- New software programs are evaluated and tested.
- Software optimization for speed and flexibility
- Code creation and testing
- Consultation with customers, engineers, security experts, and other stakeholders
- Making new features available to various stakeholder groups and internal customers
Difference between a Data Scientist and Software Engineer
The comparison between software engineer vs data scientist salary can be clearer if we consider the following differences.
Data Scientist | Software Engineering |
Data Science is concerned with the collection and processing of data. | Whereas Software engineering is concerned with the creation of features and applications for end-users. |
Machine learning and analytics are the major focus in Data Science | More emphasis is given to coding languages in Software Engineering |
Data Visualisation utilities, Data Analytics tools, plus Database Tools are usually covered in Data Science | Software Engineering, on the other hand, is concerned with programming tools, database services planning tools, CMS devices, testing tools, integration apparatus, and so on. |
Exploratory data is dealt with in Data science. | Software engineering is usually concerned with the development of systems. |
The process is central to Data Science. It is the central focus in this field as it's highly process oriented. | The methodology is the central focus of Software engineering. |
Qualifications Required for Data Science and Software Engineering
For Software Engineering, many students choose a bachelor's degree in a relevant field to consider a career in computer programming. Most software engineers have computer science, programming, or mathematics background.
A degree like Data Science is not required to be a programmer. Employers have recently favored practical knowledge over formal education programs. They understand that while degrees can provide an excellent foundation, they do not always provide specific career-ready skills. Candidates with real-world experience and excellent programming proven expertise will have a better chance of getting hired. One can enhance their chances by having these additional skills like management and communication.
Data scientists, like software engineers, are highly skilled professionals. While nearly half have a post-doctoral degree, over 80% have a master's degree in relevant areas.
A bachelor's computer science degree, social science, engineering, or statistical data can help one get started in this field. While a degree provides a solid foundation for your professional career, it is not required to get started. Some ambitious data professionals may benefit from alternative education programs.
Skill Requirement of a Data Scientist
Programming Skills
To become a Data Scientist, one must have programming skills. Numerous features in data science require programming, from creating data models to constructing analytical models, so recognizing one or more programming languages is essential.
However, knowing only one programming language will not help. If a student wants to succeed in data science, they should be familiar with Python, R, Java, or SQL. They can also benefit from being familiar with programming packages and library resources such as TensorFlow.
Statistics and Math
Data science is more than just coding. If it had been, any programmer could have been a data scientist. Statistics and mathematics are central to data science. Only by thoroughly comprehending them would anyone be able to apply them accurately to establish data models with precise assumptions. With statistical and mathematical knowledge, anyone can draw accurate conclusions.
Data Analysis
Once the raw data has been processed and manipulated, it must be analyzed. This is among the most crucial steps in the entire procedure. It is difficult to extract sense and meaning from the data unless analyzed.
One must recognize trends, disparities, and oddities in unstructured data, which requires an analytical perspective. Moreover, shorting through massive quantities of data without missing important details is required.
Machine learning and AI
Data science relies heavily on machine learning (ML) and artificial intelligence (AI). As a result, skills in these two areas guarantee positive results in data science.
Data Science offers jobs to people with degrees in machine learning or artificial intelligence. However, even if the degree is absent, passionate enthusiasts can continue pursuing data science to gain more opportunities. As a student, one can take up a Bootcamp data science to learn all the above-mentioned skills.
Skill Requirement of a Software Engineer
Coding and computer programming
Even though it is uncommon for Software Engineers to spend all their time writing code, a good Software Engineer needs to be acquainted with all the same programming languages as a Web Developer, including Python, Java, C/C++, and Ruby, and many other programming basics including such data structures and algorithms.
Software testing
Even today, understanding the intricacies of software testing and debugging is among the core technical skills required to become a Software Engineer.
Though new AI programs can conduct some of these tasks, the test method is still an expensive and time-consuming component of just about any software development project, so becoming proficient in these tasks is beneficial.
Object-oriented design OOD
OOD is a software design approach that has grown in popularity over the last few decades. It is defined by key principles such as abstraction, encapsulation, inheritance, decomposition, and generalization. In simpler terms, understanding how to implement object-oriented design standards to create flexible, modular, and reusable software is worthwhile for a Software Engineer.
Software Development
It may appear strange to include that in software engineer skills at first. Still, Software Engineers are supposed to be able to lead a software development team, which is complicated to do efficiently without substantial experience in developing software. So, working with real computer systems and developing projects and programs is a good way for software engineers to hone their technical skills.
Common Skills Required for a Data Scientist and Software Engineering
Detailed work procedure
Paying attention to details is a valuable and praised skill. Before sending off a project, check the details to avoid further misunderstandings. Details always come with asking the right questions. Hence, note down the relevant points and proceed to ask. For delivering detailed work, one needs to have a proper method or a structure. Do not make quick decisions. Instead, formulate a plan of pros and cons.
Adaptiveness
Adaptiveness shows a lot about your abilities in the workplace. Handling criticism in a positive manner is one example of adaptive behavior. Managers or higher officials prefer someone who can fit in and portray an understanding behavior.
As a person employed, there is likely to be a lot happening on the worksite or among your work colleagues that your manager hasn't notified. Moreover, freshers may not comprehend why a new protocol or procedure has been implemented or what everybody in the office is willing to take responsibility for. Managers value open-mindedness as an adaptive skill because it indicates that as an employee, one is willing to take many occurrences in the place of work at face value while remaining pleased to do your work.
Strong Communication Skills
Communication is the bane of human existence. Morality, politeness, understanding, and patience are the main components of communication skills. Be an active listener. Pay attention to the details given by the speaker. An employee can show respect and attention to the speaker by being an active listener. This skill is well regarded by everyone. Being an active listener helps to communicate better once given a chance. Moreover, it also allows altering the communication from formal to informal as the situation requires.
Time Management
Time management is the process of planning and stabilizing your time among various activities. Good time management allows completion of a task within a specific time frame despite challenges and tight deadlines. In addition, strong organizational skills help improve the reputation and advance in the workplace if one maintains their schedule and deadlines.
Good time management skills help you use time efficiently and effectively to achieve your goals. Time management skills can assist in allocating your time effectively and completing tasks efficiently. Some of the essential qualities for effective time management include:
Prioritization, Setting objectives, Communication, Stress control, and Flexibility.
Business astuteness
An astute leader is a manager who recognizes opportunities and challenges and can reorient employees and organizational resources to deal with change. Astute leaders are keenly aware of their business environment and recognize when it is necessary to change. They cultivate positive relationships with stakeholders such as employees, suppliers, and investors so that they can rely on their assistance when needed. Astute leaders can build and sustain a successful business even in difficult circumstances by combining insight, sound judgment, and flexibility.
Team management skills
The ability of a manager or organization to lead a group of individuals in completing a task or achieving a common goal is called team management. Effective team management entails encouraging, communicating with, and encouraging team members to play to the best of their skill sets and grow as professionals.
However, what qualifies effective team management varies relating to the work environment and the community involved. Some managers succeed with an authoritative approach, while others prefer to manage their teams in a more relaxed manner. In addition, some team members may react differently to different management styles. Understanding your own management style and what works best for your team is a critical component of team management.
Career Paths for Data Science vs Software Engineering
Before getting into the details of data science vs software engineering salary, one needs to understand the career path of both fields. Students can choose to become high-ranking software engineers or continue obtaining accreditations and expertise to progress to project manager and systems manager roles. A software engineer gets the freedom and mobility to design the career path that is most enticing to them.
Because nearly all businesses use software, becoming a software engineer allows one to work in various industries and fields. Whether the students are interested in finance, entertainment, sports, real estate, or another industry, there is a good chance that there are jobs for software engineers available.
This is also a profession that enables people to work from anywhere. At times one might be able to stay home for business or in other states or countries. The most important thing is that one can meet deadlines and complete projects on time. Some of the software engineer careers are:
- Front End Engineers
- Back End Engineers
- Full-Stack Engineers
- Mobile App Developers
- Graphics Engineer
- Game Engineer
- Data Engineer
- DevOps Engineer
- Software Development Engineer in Test
- Embedded Systems Engineer
- Security Engineer
Given the increased use of analytics and artificial intelligence to generate strategic business decisions and conduct their business, experts believe Data Science is the most forward-thinking skill set. In addition, it has significantly aided organizations in gaining meaningful insights from unorganized and raw data.
Data scientists are primarily responsible for assisting organizations in making smart investment decisions, targeting the right consumers, assessing associated risks, and contributing to capital allocations.
After honing all the data science skills and acquiring years of experience, students can branch into marketing, sales, data quality, finance, business intelligence, and so on, and even work as a consultant for leading data-driven companies.
Data science has beginner and expert roles in its field.
- Data Scientist
- Senior Data Scientist.
- Machine Learning Engineer.
- Lead Data Scientist.
- Chief Data Officer.
- Data Analyst
- Quantitative Analyst
- Business Intelligence Analyst
- Business Analyst
- Systems Analyst
- Marketing Analyst
- Operations Analyst
Data Scientist vs Software Developer Salary
Data Science and Software engineering are rapidly growing fields. A firm specializing in Data Science leads to entry positions as an Analyst in various fields. Pay is typically based on skill, competency, or performance. This section will provide a high-level overview of pay rates in the Data Science and Software Engineering fields.
Data Analysts and Software Engineers are both highly paid professionals. Let us compare software developer vs data scientist salary.
These are approximate values that companies offer if we compare data scientist salary vs software engineer. Of course, one can also ace a high-paying job if they have the required skill set.
| Entry-Level | Mid-Level | Experienced |
Software Engineer | ₹274k | ₹561k | ₹1M |
Data Scientist | ₹341k | ₹823k | ₹2m |
Conclusion
Students have several queries from what is a data science course to is data science or software engineering a rewarding career option. Both the career options have their own perks and facilities. However, it is ultimately a personal choice that should be made. Apart from selecting what is best for you, it is important to note that one should give the best they can after making career choices. KnowledgeHut Bootcamp data science can help you justify your choice of getting into the field of data science.
