With the rise of automation and big data, DevOps and data science have become two of the topmost career paths in tech. Each one offers immense growth potential to technology aspirants. DevOps provides faster career revamp to those with coding and operations experience. Data science with a steeper learning curve aligns best with analytical thinkers. As such choosing between the two becomes a challenge. Many people also check DevOps vs data science for help.
Various points need consideration to arrive at the right choice. If you miss out on any one aspect, you could end up choosing the wrong career path. While each of these fields pays well, deciding on DevOps or data science stands paramount. If you don’t pick the correct field, you may not be able to scale your career. So, let’s delve deeper to get better insights into data science vs DevOps. That should also help you align your interest with a compelling career path.
Data Science Overview
The technique extracts insights from complicated data sets using programming and mathematical techniques. Data scientists make machine learning models to detect patterns, predict results, or make recommendations.
The data science lifecycle involves collecting data from different sources, cleaning and processing data, conducting analysis, training, and optimising models. Data scientists translate insights into digestible deliverables like presentations, reports, and dashboards.
Data science empowers predictive analytics, fraud detection, sentiment analysis, and other advanced industry applications. With the explosion of data volume, companies need data-driven insights to enhance operations, minimize risks, and detect new opportunities.
DevOps in 2024
The current trend in this DevOps field is positive. However, the problems are equally challenging. For these reasons, there is a great demand for DevOps professionals.
Roles and Responsibilities
DevOps engineers oversee code delivery and system administration for a company. Their daily responsibilities include:
- Building and optimising CD/CI pipelines
- Configuring and handling cloud infrastructure
- Monitoring systems and fixing issues
- Automating product deployments through infrastructure as code
- Collaborating with software developers and operations teams
- Ensuring system resilience and high availability
- Improving development processes and promoting DevOps adoption
Strong DevOps engineers have immense experience spanning development, ops, and QA. They prevent workplace silos by enriching a culture of shared ownership and cooperation.
Skill Set Required
Becoming an expert demands a sheer degree of expertise. The following are the skills to get hired in any organisation.
1. Strong understanding of Linux and Scripting such as file handling, text processing, process management, and system administration
2. Knowledge of various methodologies as well as tools to fortify code mistakes and resolve issues in deployment
3. Soft Skills such as collaboration and communication to bridge the gap between developers, managers, and IT operators
Career Growth and Opportunities
As a DevOps engineer, you must possess vast knowledge of the software development life cycle. Also, you should be an expert in implementing different DevOps automation processes and tools to resolve complicated operations problems. A proficient DevOps engineer juggles between different tasks like coding, testing, and integrating.
If you wish to build a career in DevOps, you can start as a Release Manager. Then you can move up to Test Engineer, Cloud Engineer, and finally, a DevOps Architect.
Tech giants like Facebook, Accenture, and Barclays are always on the lookout for proficient DevOps experts, and skilled DevOps engineers are highly paid professionals in the industry. However, the remuneration is mostly higher for those with a greater number of skills and who have advanced certifications.
However, there is a shortage of skilled talent because the career is challenging. Even at a basic level, it requires complete ownership of your roles and responsibilities and your capability to innovate as a solution provider. So, there is a high demand for DevOps professionals.
Data Science in 2024
Currently, every company is applying statistical knowledge to predict future demand and beat the competition. As such, there is a surge in the demand for data scientists. The situation isn’t going to ease soon.
Roles and Responsibilities
Data scientists use statistical analysis, programming languages like R and Python, and machine learning algorithms to get value from data. Their day-to-day work consists of:
- Detecting business problems that can be solved with data
- Retrieving and cleaning data from structured and unstructured sources
- Performing data analysis to uncover insights and patterns
- Training, assessing, and optimising machine learning models
- Developing visualizations and predictive analytics dashboards
- Communicating findings to stakeholders in a business context
- Monitoring models and retraining as required based on new data
- Working with data engineers to build data architectures and pipelines
To excel in this position, data scientists need curiosity, programming abilities, analytical skills, and communication expertise.
Skill Set Required
You should master the skills needed for data science jobs in different industries if you wish to pursue a data scientist career. Let’s check the must-have data scientist qualifications. Key skills required are:
- Programming Skills – Knowledge of programming languages like Python, R, and database query languages like Pig, SQL, and Hive is desirable. Familiarity with C++, Scala, or Java is an added advantage.
- Statistics – Applied statistical skills, including the understanding of statistical tests, maximum likelihood estimators, distributions, and regression are essential. Statistics is the key factor when deciding DevOps vs data science.
- Machine Learning – Profound knowledge of machine learning techniques like Decision Forests, k-nearest Neighbors, Naive Bayes, and SVM is necessary.
- Strong Math Skills – The basics of Linear Algebra and Multivariable Calculus are important as they form the basis of most algorithm optimization or predictive performance techniques.
- Data Wrangling – Expertise in tackling imperfections in data is an integral aspect of a data science job description.
- Experience with Data Visualization Tools like Ggplot, Matplotlib, d3.js, and Tableau is necessary to visually encode data.
- Excellent Communication Skills – Describing findings to a technical and non-technical audience is a must.
- Strong Software Engineering Background
- Problem-solving aptitude
- Hands-on experience with data science tools
- Great business sense and an analytical mind
- A Degree in Engineering, Computer Science, or a relevant field is preferred
- Proven Experience as a Data Scientist or Data Analyst
Key Differences Between DevOps and Data Science
Focus
DevOps focuses on automating software delivery through continuous integration and infrastructure as code. Data science focuses on extracting insights from data through statistical analysis and machine learning.
Output
A DevOps engineer mainly aims at running software applications. The results lead to more efficient applications. Data scientists, on the other hand, aim at visualising data for further interpretation. Based on the insights, they help to build analytical models to solve complex problems.
Process Orientation
DevOps experts employ CI/CD pipelines to construct, test, and deploy software efficiently. Data scientists, on the flip side, use the data project life cycle to obtain, scrum, model, and interpret data.
Automation Focus
DevOps aims to have an effective infrastructure for speedy software deployments. The goal of data science is to build ETF pipelines for model training for cost-effective problem-solving.
Collaboration Approach
The communicative models of data science and DevOps are similar in many respects. However, the collaborative areas differ. DevOps professionals collaborate with developers and IT operations teams constantly. Also, they communicate with stakeholders and other team members to come up with an efficient product. Data scientists collaborate with data analysts, engineers, and business users to build problem-solving programming models.
Security
DevOps security areas include infrastructure hardening, secrets management, and access controls. Data science security areas include data governance, ethical AI, and model explanation.
Factors to Consider When Choosing a Career: DevOps vs Data Science
At this point, you know both concepts. Now, you might be thinking which is better data science or DevOps? It all depends on particular parameters. The following factors should help you with the DevOps or data science decision.
Interest
Do you love automation workflows and tinkering with systems or analyzing data and developing models? Select the path that intrigues you. DevOps suits those with coding abilities and operations experience, whereas data science aligns with statistical skills and analytical thinking.
Industry Trends and Demand
Both fields have immense growth potential. The demand keeps increasing in each of these sectors. Yet, DevOps enjoys an edge because it’s used in more tech companies. However, data science has wider applications as it’s employed by non-tech organizations too. If technology is your genre, DevOps makes a great choice. If you prefer any field that requires analytical thinking, data science is the way to go. Use this parameter carefully when deciding on DevOps or data science.
Salary
Both fields pay higher compensation. So, you can make a remunerative career in any of these sectors. However, data scientists, especially machine learning engineers earn more than a lead DevOps engineer.
Finishing thoughts
Both DevOps and data science offer a bright future to any aspirant. You can also grow in your position in these two sectors. However, you must know the difference between DevOps and data science to make the best choice. By understanding the core differentiators, you can decide – which is best: DevOps or Data Science. Both careers offer outstanding stability, compensation, and options for advancement to maximize your impact.
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Amol Shete
Senior Software Engineer
A well-experienced DevOps engineer who loves to discuss cloud, DevOps, and Kubernetes. An energetic team player with great communication & interpersonal skills.
FAQ's
If you want a wider career and relevancy, going for data science is a better option. However, if you wish to work in a niche area, DevOps is the right choice.
Both fields have many similarities. Still, you can find DevOps vs data science comparisons due to some differences. DevOps focuses on automation, collaboration, and integration to improve IT operations and software delivery. Data science utilizes analytical and statistical methods to extract insights from data.
Despite the difference between DevOps and data science, the salaries of both experts are quite similar. DevOps engineers earn a median salary of $106,000 per year. Data scientists earn an average compensation of $117,000 a year.
