Which career choice is better DevOps vs Data Science?

Asked by Nana Rai about 2 years ago

Answers 4
paul larsenee

paul larsenee

Head of Capital Markets at Williams Trading, LLC

It depends on what grounds you are asking. Like if you are asking for better earnings, the answer may differ in comparison to the better scope in the career. I’m not making this confusing but rather trying to tell you that you should see all the aspects. For example, answering the best  profession in the world is not possible.


If we look differently, DevOps is a more niche area, don't you think? I mean it's possible that you can switch your career after a few years of experience but how much can it really be? DevOps is all about automations and unification of processes. The professionals in this area are very talented in application maintenance, code combination, management etc., and this is precisely why they are paid very handsomely. Like if you have a limited number of experts, obviously their demand will be high.


In general, a DevOps Engineer annual salary in a country like the USA can be somewhere around $125,000. It is equivalent to around 95 lakhs. Plus they earn cash compensation as an additional income. This makes them one of the highest paid professionals in the world. But as I mentioned, this comes with a dedicated area of work and niche knowledge and skills.


On the other hand, if you look at the data scone it is a different story. However, this subject also asks for great press and good scientific knowledge, data science is a much wider area than DevOps Engineering. You can basically get a job in multiple sectors with a data science degree and experience. Plus the jobs in data science are widespread and valued almost everywhere. The average earnings of a data scientist is somewhere around 75 lakhs per annum.


When you look at the figures, both of the professions are well-paid. However in terms of score, data science is definitely the better one. Also, it is currently #3 professions in almost all major countries now. So if you want relevancy and a wider career, I suggest going for data science.


However, if you like to work in a more niche area and want to earn potentially more, DevOps is the better choice. Just keep in mind that switching professions in DevOps will affect your earnings and position in the industry.


The easiest  procedure for a  career in DevOps or say data science is a master program abroad. The colleges in USA, UK and Canada are quite connected with the industry itself and offer you needed knowledge and network to launch yourself in the industry. If you are interested, let me know how I can help.


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Asheema Nagar

Asheema Nagar

Student of Delhi University

Choosing between DevOps and Data Science depends on your interests, skills, and career goals. Both fields are in high demand, but they involve different skill sets and responsibilities. Here's a brief comparison to help you make an informed decision:

DevOps:
1. Role:

DevOps (Development and Operations) focuses on improving collaboration between software developers and IT operations. It aims to streamline the software development lifecycle, from coding to testing to deployment.

2. Skills:

Knowledge of automation tools (e.g., Docker, Jenkins, Ansible).
Proficiency in scripting languages (e.g., Python, Shell).
Understanding of infrastructure as code (IaC).
Collaboration and communication skills.

3. Responsibilities:

Continuous integration and continuous deployment (CI/CD).
Infrastructure automation and management.
Collaboration with development and operations teams.

4. Career Path:

DevOps Engineer, Automation Engineer, Site Reliability Engineer (SRE).

Data Science:
1. Role:

Data Science involves extracting insights and knowledge from structured and unstructured data. It combines expertise in statistics, programming, and domain knowledge to analyse and interpret complex data sets.

2. Skills:

Proficiency in programming languages (e.g., Python, R).
Statistical analysis and machine learning techniques.
Data visualisation and storytelling.
Domain-specific knowledge.

3. Responsibilities:

Data analysis and interpretation.
Building and deploying machine learning models.
Communicating findings to non-technical stakeholders.

4. Career Path:

Data Scientist, Machine Learning Engineer, Data Engineer.

Considerations:
1. Interest and Aptitude:
Consider which field aligns better with your interests and aptitude. If you enjoy working with data, statistical analysis, and machine learning, Data Science may be a better fit. If you prefer automation, infrastructure, and collaboration, DevOps could be more suitable.
2. Industry Demand:
Both DevOps and Data Science are in high demand. Research the job market and industry trends in your specific location and field of interest.
3. Career Growth and Advancement:
Assess the potential for career growth and advancement in each field. Both offer opportunities for specialisation and advancement into leadership roles.
4. Work Environment:
Consider the work environment that appeals to you. DevOps professionals often work closely with development and operations teams, focusing on improving processes. Data Scientists may work on a variety of projects, from analysis to machine learning model development.
5. Continuous Learning:
Both fields require continuous learning due to evolving technologies. Consider which field you are more excited about staying updated on.

Ultimately, the better choice depends on your personal preferences, strengths, and career aspirations. If possible, gaining some hands-on experience or internships in both fields can provide valuable insights into which aligns better with your goals.


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Neha Shrivastava

Neha Shrivastava

Student of MITS College

DevOps

Innovation and Efficiency: Focuses on streamlining software development and deployment processes, emphasising continuous integration and delivery.
Industry Demand: High demand in industries prioritising efficient software deployment.
Skillset: Requires proficiency in coding, system administration, and a strong understanding of software development lifecycles.

Data Science

Data-Driven Decision Making: Centers around extracting insights from data, crucial for strategic decisions in businesses.
Growing Relevance: With the surge of big data, the demand for skilled data scientists is rising across sectors.
Skillset: Involves statistical analysis, machine learning, and proficiency in programming languages like Python and R.


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Gaurav Prakash

Gaurav Prakash

MentR-Me
MentR-Me Team

Choosing between a career in DevOps and Data Science largely depends on your personal interests and career goals, as both fields offer unique opportunities and challenges.

DevOps focuses on the practices, tools, and methodologies that enable an organization to deliver applications and services at high velocity. This field blends software development (Dev) and information-technology operations (Ops), aiming to shorten the systems development life cycle while delivering features, fixes, and updates frequently in close alignment with business objectives. If you enjoy working collaboratively across various teams, have a knack for systems automation, and are interested in the continuous integration and continuous deployment (CI/CD) pipeline, DevOps could be a rewarding path.

On the other hand, Data Science is centered on using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. This field requires a strong foundation in statistics, machine learning, and programming. Data Scientists often handle large volumes of data to find patterns and help make data-driven decisions. This career is ideal if you are curious about uncovering hidden insights in data and using that knowledge to solve complex problems.

Both careers are highly demanded and well-compensated, but they require different sets of skills and mindsets. DevOps engineers thrive in fast-paced environments and are crucial in optimizing development cycles and improving operational efficiency. Data Scientists, meanwhile, must be adept at statistical analysis and predictive modeling, providing the groundwork for strategic decision-making based on data.

Ultimately, the better career choice depends on what you are more passionate about.

If you enjoy making processes more efficient and working towards operational excellence, DevOps is the way to go. If you prefer deep analytical tasks and forecasting future trends from data, then a career in Data Science might be more satisfying.

Consider your interests, skills, and the kind of work that excites you most when making your decision.


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