In the tech-driven world where everything is connected to computers and technology, it is tough to decide which career path is right for you. While both full stack developer and data scientist roles have their own benefits and offer exciting career opportunities, you must be confused to decide which one holds more promise for your career goals and aligns more with your interests. Explore Scaler’s Data Science Course to see if a career in data science aligns with your goals, or Discover Scaler’s Full Stack Developer Course to learn more about becoming a versatile developer. Let’s find out the differences between the two to help you make an informed decision.
Full Stack Developer vs Data Scientist
Currently, full-stack developers and data scientists are the most in-demand professions in the tech industry offering a lot of good career opportunities with handsome salaries. However, it is important to understand what each role entails.
What is a Full-stack developer?
A Full-stack developer is someone who can work on both the front-end (that users see and interact with) and back-end (server-side) of a website or web application. They are generally developers or engineers who design user interfaces, develop servers, and also manage databases. But, handling both front and back-end requires a wide range of skill sets. Some of the programming languages and technologies they use are HTML, CSS, JavaScript, Python, PHP, Java, and many more.
What is a Data Scientist?
A data scientist is a professional who has the ability to use data to drive informed decision-making in a business. They use various techniques to collect and analyze the vast amount of data from different sources and use it to extract meaningful insights and patterns. Being a data scientist requires expertise in statistics methods, algorithms, mathematics, and computer science to solve any business problems. They can work in various industries such as finance, healthcare, retail, marketing, and more.
Comparison Between Full Stack Developer vs Data Scientist Roles
| Category | Full Stack Developer | Data Scientist |
|---|---|---|
| Responsibilities | Develop the entire website (both front-end and back-end). | Collect and analyze data, build predictive models, and find valuable insights. |
| Goal | Create and maintain functional and user-friendly websites. | Use data to extract actionable insights and make informed decisions. |
| Required Skills | Proficiency in various programming languages and frameworks, basic designing skills, knowledge of APIs, etc. | Strong background in Statistics, machine learning, data visualization, programming, etc. |
| Typical Tasks | Design Interfaces, implement server-side logic, design and manage databases, troubleshoot and fix bugs, etc. | Collect and clean data, analyze data, create and implement data models, visualize data, etc. |
| Programming Languages | JavaScript, Python, HTML/CSS, Java, Ruby, PHP, etc. | Python, R, SQL, Java, Scala, Julia, C/C++, JavaScript, etc. |
| Coding | Yes, coding is required. | Yes, coding is required. |
| Data Requirement | No, data is not required. | Yes, Data science requires data. The data can be structured or unstructured. |
| Tools and Techniques | Frameworks like React, Angular; Databases like MySQL, MongoDB | Tools like Jupyter Notebook, TensorFlow, pandas |
| Industry Focus | Web development, software engineering | Finance, healthcare, retail, e-commerce, and many more. |
| Future Trends | Artificial Intelligence, Low-code development, E-learning, Blockchain, E-commerce | Artificial Intelligence, Augmented analytics, Machine learning, Big data technologies, Predictive analytics |
Comparing Skills and Education Required
Next, we will explore the skills and education required to become a full-stack developer or data scientist.
Full Stack Developer Skills and Education
Full Stack Developers generally have a bachelor’s degree in information technology, computer science, software engineering, computer application, or a related field. If you earn a relevant post-graduate degree, it will increase your chances of earning higher pay. However, there are many developers out there who are self-taught and do not have any formal education. So, you don’t need a degree to become a full-stack developer. Developing essential full-stack developer skills only requires a continuous learning mindset and a genuine interest in a particular field. But, you can increase your chances of getting hired by having a technical background, as well as having practical experience and projects.
Skills Required –
- Front-end Development: Strong grasp of HTML, CSS, JavaScript, React, Angular, Ruby, Vue.js, etc.
- Back-end Development: Proficiency in JavaScript, SQL, Java and Python. Also having experience in these frameworks ExpressJS, Flask, NodeJS, Django is a plus.
- API Integration: Understanding of how to design and consume APIs.
- Databases: A good understanding of database management systems like MySQL, PostgreSQL, MongoDB, or Firebase.
- Version Control System (VCS): You should be comfortable in using version control systems like Git.
- Deployment: Knowledge of server management and different deployment tools like Docker, AWS, and Kubernetes.
- Problem Solving and Debugging: Strong analytical and problem-solving skills for troubleshooting issues during the development process.
Data Scientist Skills and Education
To become a data scientist, what truly matters is a strong proficiency in Python and a dedication to continuous learning. However, most data scientist roles do require a bachelor’s degree in data science, computer science, applied mathematics and statistics, or a related field. Additionally, career opportunities and salaries tend to increase as you attain higher degree levels, such as a Ph.D. or Master’s Degree, and master the most in-demand data scientist skills.
Skills Required –
- Programming Languages: Strong programming skills like proficiency in Python, R, or SQL.
- Machine Learning and Deep Learning: Knowledge of machine learning and deep learning techniques for quick decision-making and predictive analysis.
- Statistical analysis and mathematics: Understanding of statistical concepts and mathematical principles like probability, regression, linear algebra, and more.
- Effective Communication: Ability to communicate complex findings to other stakeholders (can be tech and non-tech) clearly and effectively.
- Data Visualization: Excellent data visualization skills to help translate these data into different graphs or charts. Some data visualization tools include Tableau, Power BI, Excel, and Qlikview.
- Big Data: Knowledge of tools like Hadoop, Spark, and Hive to process big data.
- Data Wrangling and Database Management: Good understanding of tools like SQL and MySQL or database management. Also, data wrangling techniques for cleaning and preprocessing data to ensure high-quality data for analysis.
Data Scientist Vs. Full Stack Developer: Career Opportunities and Demand
We can’t deny the fact that both data scientists and full-stack developers are highly sought-after positions since almost every organization relies on technology to drive business operations. The demand is only expected to grow even higher with plenty of growth opportunities across all industries. Let’s explore these career opportunities in detail –
| Category | Full-stack Developer | Data Scientist |
|---|---|---|
| Job Roles | – Front-end Developer – Back-end Developer – Software Engineer – System Analysts – Application Developer – UI/UX Developer, and more. | – Data Scientist – Data Engineer – Data Architect – Data Analyst – Business Intelligence Analyst, and more |
| Industries | – IT Companies – Software Development Companies – Startups – Freelancing | – Healthcare – Finance – E-commerce – Retail – Marketing, etc. |
| Specializations | – Front-end development Back-end development Database Administration and Management – Mobile Development – Game Development | – Machine learning – Deep learning – Natural Language Processing (NLP) – Big Data Analytics – Computer Vision |
| Salary (IN) | Average INR 4-10 lakhs per annum (entry), up to INR 10 lakhs (experienced) as per Glassdoor. | Average INR 6-19 lakhs per annum (entry), up to INR 20 lakhs (experienced) as per Glassdoor. |
| Salary (Global) | Average salary: $76K – $126K/year as per Glassdoor. | Average salary: $132K – $190K/year as per Glassdoor. |
| Demand | High driven by skill set and experience | Continuously rising with an increase in data-driven strategies |
| Job Market Outlook | As per Paul Barnhill, Managing Director Cloud Engineering Deloitte Consulting LLP, “Companies consistently seek to have FSDs on their delivery teams as go-to leaders who can work front- and back-end environments in a single role.” | As per U.S. BUREAU OF LABOR STATISTICS “Employment of data scientists is projected to grow 35 percent from 2022 to 2032, much faster than the average for all occupations.” |
Note: salary information was taken from Glassdoor, Indeed, etc.
Work Environment and Culture
Full Stack Developers work in teams to develop websites and apps, whereas Data Scientists work with large data sets to find meaningful insights. Let’s dive into detail –
| Category | Full-stack Developer | Data Scientist |
|---|---|---|
| Culture | Focused on teamwork, adaptability, and continuous learning mindset. | Focused on problem-solving, analytical research, and predictive analysis. |
| Team Collaboration | Collaborate with back-end developers, designers, other engineers, developers, and clients. | Collaborate with project managers, stakeholders, analysts, and other data professionals. |
| Project Variety | Work on diverse projects such as making websites, mobile apps, databases, and APIs. | Work on data-related projects such as data visualization, predictive analysis, machine learning, and statistical analysis to extract insights. |
| Work-life Balance | Generally balanced work life. But may have to work overtime or on weekends depending on project requirements. | Work-life balance can vary based on project requirements, industry, and business needs. Some projects require long working hours to meet deadlines. |
Common Real-time Challenges Faced by Full-Stack Developers
Being a Full-Stack sounds like a dream job, right? Good career opportunities, lucrative packages, and a lot of perks. But as we know, challenges are a part of the journey. Becoming a Full-stack developer is an exciting and dynamic career path, but it has its hurdles and challenges. Overcome these challenges with Scaler’s Full Stack Developer Course and master the skills you need to excel in your career. Let’s explore some of the common challenges faced by full-stack developers –
- Working on a wide range of technologies: Being a full-stack developer requires skills in various programming languages and tools. And mastering all these areas can be really tough and time-consuming as well.
- Difficulty working on both front-end and back-end development: Managing both ends simultaneously can be difficult, especially for beginners when coordinating with different teams.
- The challenge in understanding and learning new codes: Learning and applying new coding languages, frameworks, and libraries can be challenging for developers in this fast-changing world.
- Testing and debugging of coding in software: The code should be error-free before going live. However, testing and debugging both front-end and back-end code takes more time and effort.
How do Full-stack developers solve real-time challenges?
Being a full-stack developer is not an easy path. But the following tips will help you overcome these real-time challenges with ease –
- Continuously learn different programming languages
- In-depth understanding of technologies
- Gain knowledge of server administration tools
- Understand the database you work on
- Know about various cloud computing platforms
- Learn to efficiently solve problems
Common Challenges Faced by Data Scientists
Nowadays, almost all companies rely on data to make informed business decisions. Data-driven decision-making has increased the demand for data scientists. However, data scientists often encounter various challenges in their day-to-day work, and as a result, some may find data science to be somewhat challenging. Elevate your data science skills with Scaler’s Data Science Course and tackle these challenges with confidence. Let’s see some of these common challenges Faced by Data Scientists –
- Finding and Accessing the Right Data: Generally, data is collected from different sources in different formats, so it becomes difficult for data scientists to find the correct data.
- Data Quality and Cleaning: The data we collect is often messy and inconsistent. This makes it hard for data scientists to ensure the quality and accuracy of their analysis.
- Reporting to Non-Technical Stakeholders: Most data scientists often struggle with translating their findings into clear and meaningful business insights for non-tech stakeholders.
- Data Privacy and Security: With the increase in cyberattacks, data scientists find it difficult to ensure the security and confidentiality of sensitive data.
- Unclear KPIs or Metrics: Poorly defined KPIs make it tough for data scientists to understand if their data analysis efforts are actually impacting the work.
How do Data Scientists overcome real-time challenges?
Data scientists can easily overcome these challenges by implementing the right strategies and using the right tools. Some of them include –
- Staying updated with the latest tools and techniques
- Using automation for daunting tasks like data cleaning
- Improving communication skills
- Regularly reviewing KPIs and metrics
- Collaborating with different teams for better insights
- Implementing data security measures
Future Trends and Job Opportunities
Looking ahead, both data science and full-stack development are expected to undergo tremendous changes with the emergence of new technologies and the increasing volume of data. Let’s explore some recent trends in full-stack development and data science –
1. Full Stack Developer
Future Trends:
Full-stack developers are often considered “jack of all trades”. And they are increasingly valued by employers because of their versatility to handle overall website development. So, it’s important to explore the major trends in full-stack development to stay ahead in the field. Some key trends include –
- AI (Artificial Intelligence) Integration
- ML (Machine Learning) Usage
- IoT Development
- Blockchain Adoption
Future Job Outlook:
With the increase in web and app-based applications, demand for Full-stack developers has also increased. They have a wide range of skill sets making them versatile and a highly valuable asset for companies. And therefore, companies are increasingly hiring full-stack developers instead of hiring front-end and back-end developers separately.
As per Paul Barnhill, Managing Director of Cloud Engineering Deloitte Consulting LLP, “Companies consistently seek to have FSDs on their delivery teams as go-to leaders who can work front- and back-end environments in a single role.”
2. Data Scientists
Future Trends:
Data science is an ever-evolving field with many new tools, techniques, and applications. So, we strongly emphasize continuous learning and staying updated with the latest technologies and developments in this field. Following are some of the top data science trends for 2024 and beyond –
- Auto-ML
- Generative AI
- MLops
- LLMs
- AI and Databases Based on Cloud
- Augmented Analytics
- Focus on Edge Intelligence
- Actionable Data
Future Job Outlook
The future job outlook of data scientists looks promising with an increase in data science applications across various industries – finance, banking, automobile, and more. The demand for skilled data scientists is expected to rise even further as companies now recognize the value of data-driven decision-making. This creates ample career opportunities in the field.
As per U.S. BUREAU OF LABOR STATISTICS “Employment of data scientists is projected to grow 35 percent from 2022 to 2032, much faster than the average for all occupations.”
Deciding Your Path: Full Stack Developer or Data Science – Which Suits You Best?
Well, full-stack developers and data scientists are the most sought-after and in-demand careers offering many opportunities. Choosing the right path can be difficult so let’s make it easier for you:
- Your Interest – If you enjoy building user interfaces and creating interactive web apps, front-end development may be the right choice for you. But if you enjoy working with data uncovering hidden insights and patterns, you can go ahead with data science.
- Your Career Goals – If you want a career path having a dynamic environment for growth and rapid advancement opportunities, front-end development will be the perfect fit. But if you want to make a significant impact through your work and data-driven career, data science is the right choice.
- Your Skillset and Aptitude – Assess your current strengths and weaknesses. Those with proficiency in programming (HTML, CSS, and Javascript) and creative skills might be interested in front-end development, while those with proficiency in statistics, mathematics, Python, and R might prefer data science.
However, you can upskill at any time with Scaler, offering comprehensive courses in Full Stack Development and Data Science. Get the right skills, training, and practical experience needed to excel in your desired field with these courses.
Conclusion
By now, you must be clear on which career path to choose – Full-stack developer or Data Scientist.
So, it doesn’t matter which field you choose as both full-stack developer and data scientist jobs are in high demand. Both offer unique opportunities, challenges, and different growth factors. Remember, the career outcomes of Full-stack Developers vs Data Scientists will be different. However, with the right experience and knowledge, you can surely excel in both fields. So, choose the career path that better aligns with your interests, skills, and passion. Improve your skills, get hands-on experience, work on real-world projects, and you’ll definitely have a growing phase in your career.
FAQs
Who earns more full-stack developer or data scientist?
According to different websites, the average salary of a full stack developer is 4-8 LPA in India whereas a data scientist earns around 6-12 LPA. Overall, data scientists tend to earn higher salaries due to their high demand across all industries.
Will Full-stack development be replaced by AI?
No. AI can be used to automate certain tasks of full-stack developers such as testing and debugging. But it cannot replace human creativity and problem-solving skills.
Will data scientists be replaced by AI?
No. AI can be used to provide certain tools and techniques to help data scientists in enhancing their capabilities. But it cannot replace human intelligence, intuition, and common sense.
Is full-stack easier than data science?
Difficulty level depends on your strengths and interests. Full-stack developer requires proficiency in both front-end and back-end development, whereas, data science requires strong statistical and mathematical skills. Acquire the necessary skills for your desired career.
Which is difficult; data science or web development?
Data science requires strong statistical and analytical skills, whereas web development requires more specialized programming skills. So, start improving your skills in your desired field now.
What are the differences between a data analyst and a full-stack developer?
Data analysts collect and analyze large data sets to help businesses make better decisions. Full-stack developers focus on the front-end and back-end development of a website.
How can I transition from being a Full Stack Developer to a Data Scientist, or vice versa?
You can register for online courses, gain formal education, and work on related projects to showcase your proficiency in a particular field.
Which field offers a more promising future: Data Science or full-stack development?
Overall, it’s difficult to say which one offers more. Both Data Science and full-stack development offer promising career opportunities.
