Full Stack Developer VS Data Scientist - Which is better?

Individuals and corporations both need to make investments. They reduce risk in our lives and provide a safety net in times of need. When it comes to enterprises, investments are made not only in terms of money but also in terms of people, such as team development and image building. Businesses must spend today in order to enjoy the advantages tomorrow, as this saying states. Get to know the full stack developer vs data scientists from this blog.

What is a full stack developer?

A Full Stack Developer is someone who works on both the Back End (server side) and the Front End (client side) of an application. To execute their work successfully, Full Stack Developers must have some knowledge of a wide range of coding specialisations, from databases to graphic design and UI/UX management.

Skills required for a full stack developer

• HTML/CSS.
• JavaScript.
• Git and GitHub. 
• Backend languages. 
• Web architecture. 
• HTTP and REST. 
• Database storage. 
• Basic design skills.

Read more: How to become a full stack developer

What is a data scientist?

Data science is an interdisciplinary subject that uses scientific techniques, procedures, algorithms, and systems to extract information and insights from noisy, structured, and unstructured data, as well as to apply that knowledge and actionable insights to a variety of application areas.

Skills required for a data scientist

• Python programming. 
• R programming. 
• Hadoop platform. 
• SQL databases. 
• Machine learning and AI. 
• Data visualization. 
• Business strategy.

Click here: How to become a data scientist

How are data science and full stack development different?  

To start, it can be helpful to understand the fundamental differences between data science and web development.

Full-stack web development is a user-centered discipline that entails designing, developing, and managing websites. Web-based internet applications, electronic companies, and social networking sites such as Facebook and Twitter are examples of this.

Full - stack developers build web applications by combining multidisciplinary abilities such as web design, graphic design, web publishing, web programming, and database administration. They are skilled coders who can build websites from the ground up and are generally regarded as creative and team-oriented due to the sorts of projects and teams they work with.

However, unlike data scientists, no prior understanding of arithmetic or statistics is required. Web developers, in comparison to data scientists, spend more time on larger projects, such as websites, which take longer to deploy. As a result, web developers frequently report pleasant, concrete project outcomes, as well as frequent opportunities to demonstrate innovation in their work and a positive feedback loop.

Data science is a field that focuses on data analysis, prediction, forecasting, and optimization. Data scientists examine data using a combination of subject experience and programming abilities to discover patterns and extract meaning using arithmetic, statistics, and algorithms.

Data scientists create artificial intelligence (AI) systems capable of doing tasks that would typically need human intellect by applying machine learning algorithms to voice, video, text, and pictures.

As a result, these systems provide customers with important input that may translate into significant company income while also assisting them in making decisions and being more efficient. In other words, data scientists devote the majority of their time to analysis, with some programming and development thrown in for good measure.

Data scientists are typically thought of as analytical, skilled in cleaning, analysing, and manipulating data, as well as report writers. They operate in teams and on their own, but on smaller projects with shorter timeframes than web developers. This area necessitates a solid understanding of statistics and mathematics.

Comparison between full stack developer vs data scientist

Full stack developer	Data scientist
It is the creation of websites for the intranet which is a public platform	It is the combination of statistics, algorithms and technology to analyse data
The entire process involves coding	Coding is widely used
Languages used: Photoshop, HTML, CSS, JAVASCRIPT,  PYTHON, ANGULAR, NODE.JS RUBY	Languages used: C, C++ , C# , JAVA, PYTHON, R, SQL
No statistics	Uses statistics to certain extent
No data required	Structured & unstructured data
E-commerce & E-learning	Machine learning and artificial intelligence

Full stack Developer vs Data Scientist. : What's the key Difference?

• Web Development is the process of creating a website for the internet or intranet using company details, client requirements, and technical skills. Data Science is the process of analysing data using specialised skills and technology.
• Full stack development has been around since 1999, but Data Science was launched in 2008.
• Python is a programming language that is utilised by both data scientists and full stack developers. However, it is used in Data Science to analyse data, whereas it is utilised in full stack development to create a website.
• Data Science makes extensive use of coding but also incorporates other components, whereas full stack development is entirely centred on code.
• In Data Science, statistics are used, but in full stack Development, statistics are not used.
• Full stack developers strive to accommodate the client's demand when developing a website, whereas Data Scientists try to answer business-related questions at the conclusion of the study.
• Full stack Development relies on direct connection with the customer to understand demands and obtain the necessary information, whereas Data Science relies on the availability of data.
• Full stack developers deal with company information, whereas Data Scientists work with organised and unstructured data.
• With the introduction of E-Commerce, Data Scientists gained a better grasp of websites, but full stack Developers lacked the ability to work with data.
• In Data Science, such as Machine Learning and Artificial Intelligence, there are several future trends, but in full stack Development, there are few.

What are the forecasts for data scientists and full stack developers in terms of job growth?

Despite the fact that they have certain abilities in common, they have diverse professional strengths and varied income possibilities.

In India, a full stack developer pays for a fresher is Rs.374,000. A full-stack developer with 1-4 years of experience makes an average of Rs.553,006 per year. An individual with 5-9 years of mid-level experience may expect to earn around INR 12-14 lakhs.

The average pay for a data scientist is Rs. 840,000. With 5 to 8 years of experience, a mid-level data scientist may make approximately Rs. 100,000 per year. Early-career data scientists make about Rs.60,000 per year with 1 to 2 years of experience.

A full-stack developer is knowledgeable not only in front-end and back-end development, but also in a variety of other disciplines. As a result, full-stack development is a great career choice since experts in this sector can monitor and manage any issue that arises throughout the product development process.

Data science is a fantastic professional path with a lot of room for growth in the future. Demand is already strong, pay is competitive, and benefits are plentiful, which is why LinkedIn has named Data Scientist "the most promising career" and Glassdoor has named it "the greatest job in America."

While both tracks have tremendous earning potential, according to LinkedIn's 2020 Emerging Jobs Report, data scientists have a 37% annual growth rate and "Full Stack Engineers" have a 35% annual growth rate, it's worth noting the exponential future needs and potential of Machine Learning and AI, which will bolster the demand for data scientists now and in the future. In fact, data science has led the list for the past three years, with AI cited as a major contributor.

Data scientist salary by location in India

Mumbai has the most employment possibilities and the highest yearly data scientist pay in India for data innovators, followed by Bangalore and New Delhi. However, because Bangalore is India's startup capital, it boasts the most startup job possibilities. Because Bangalore is considered the heart of India's IT industry, a data scientist's pay is likely to be greater than in other locations.

• Mumbai – ₹788,779
• Chennai – ₹794,400
• Bangalore – ₹984,478
• Hyderabad – ₹785,023
• Pune – ₹725,156
• Kolkata – ₹402,968

Check out for: data scientist salary in india

Full stack developer salary by location in India 2023

Bangalore has the most employment possibilities and the highest yearly full stack developer pay in India for data innovators, followed by Mumbai and Gurgoan. Bangalore is also the startup capital of India, therefore it has the most startup job possibilities. Because Bangalore is regarded as the heart of India's IT sector, a full stack developer's pay in Bangalore is likely to be greater than in other locations.

The city in which you work will have an impact on how much money you make as a Full Stack Developer. Certain cities have a thriving IT business that generates more revenue than its counterparts in other locations. Expenses for meals, transportation, and accommodation are also greater on a daily basis.

• New Delhi and the NCT- INR 593391 per annum
• Kolkata, West Bengal- INR 504036 per annum
• Mumbai, Maharashtra- NR 634612 per annum
• Gurgaon, Hariyana- INR 620410 per annum
• Ahmedabad, Gujarat-  INR 37987 per month
• Bengaluru, Karnataka- NR 760880 per annum
• Chennai, Tamil Nadu - INR  585956 per annum
• Hyderabad, Telangana- INR 615448 per annum

Know: full stack web developer salary from here

Roles of full stack developer

1. Assisting in software design and development.
2. Testing and debugging software in order to keep it running smoothly.
3. Writing clean code for the software's front and back ends.
4. Creating user interactions within the web application.

Check for road map to full stack web development from here 

Roles of Data scientist

1. Taking massive volumes of organised and unstructured data and turning it into useful information.
2. Identifying the data-analytics solutions that have the most potential to propel businesses forward.
3. Using data analysis tools such as text analytics, machine learning, and deep learning to uncover hidden patterns and trends.
4. Encourages the use of data to solve complicated business challenges.
5. Data cleansing and validation to improve data quality and effectiveness.
6. Data visualisation is used to communicate all of the positive observations and results to the company's stakeholders.

Job Market in abroad

In recent years, Canada's data and development markets have exploded.

In the future, developers and data experts will be the driving force behind Canada's technological progress. “Software engineers, data scientists, cybersecurity analysts, and a variety of other high-skilled occupations will play essential roles in developing, sustaining, and defending not only our digital economy, but also our future communities,” according to the research.

“Canada's demand for digitally-skilled talent is expected to reach 305,000 by 2023, resulting in total employment of over 2 million in the digital economy,” the report continues.

According to 2018 statistics, full-stack developers had the highest amount of job opportunities per million workers, with 1,764 available. Given remote work considerations, data professionals and developers are also adaptable at periods like COVID-19.

According to Statistics Canada, 75% of Canadians in the technical services sector work from home during COVID-19, and the sector has seen less job losses than other sectors. Careers in data science and web development allow you to work from home, either temporarily or permanently.

Bootcamp career path

Web Development Bootcamp and Data Science Bootcamp both lead to distinct career pathways in data science.

To begin, it's critical to distinguish between the two streams. Data scientists gather, clean, and analyse data from a variety of sources in order to create algorithms, models, and machine-learning tools that automate and improve operations.

Full-stack engineers are skilled in both the back-end and front-end elements of software development, which means they can manage databases or servers, create and maintain APIs, and work on user-facing portions of web applications.

Web Development Bootcamp prepares graduates to be junior developers. Later in their careers, they can work as a:

• Full-stack developer
• Front-end developer
• Back-end developer
• iOS developer
• UI developer
• UX developer
• Data engineer
• Database analyst
• DevOps engineer
• Technical product manager

Graduates of the Data Science Bootcamp are prepared to begin a career as a junior data scientist or data analyst. As your career progresses, you may be able to pursue a variety of positions, such as:

• Data scientist
• Senior data scientist
• Senior data analyst
• Data systems developer
• Machine learning engineer
• DataOps engineer
• Business intelligence developer
• Data systems analyst

What is the best option for a beginner?

Neither of our programs require prior experience in the field. But, bootcamp is not easy. It requires determination, grit, and the drive and willingness to commit 100% of your time and effort to learning a new craft during the 12-week program, as well as an eagerness to continue to learn post-bootcamp.

For many people with no technological expertise, full stack development is a more manageable starting point, and it opens up the prospect of data-related positions down the road. Because the Data Science Bootcamp incorporates both programming and mathematics principles, it is a more technically demanding curriculum. However, both are appropriate for novices who are ready to put in the necessary work; the data science bootcamp may require a little more.

Do whatever it is that you want to do?

Do you find data fascinating? Do you have a natural aptitude for math? You could be a good fit for data science. Do you want to use web applications to bring your ideas to life? Do you have visions of web pages dancing around in your head? Web development could be a better match in that situation.

At the end of the day, you need enthusiasm to go through the long, hard grinds of learning a new trade.

Is it possible to have both?

Absolutely, but not at the same time. If your long-term goal is to enrol in both, you should start with the full stack development bootcamp, get a few months of work experience, and then enrol in the Data Science Bootcamp.

What else should you think about if you want to pursue a career in full stack development?

Now that you have a better understanding of the distinctions between full stack developers and data scientists, you can use the big picture to figure out which path is ideal for you. Consider the following when it comes to site design:

A full-stack developer's job is driven by the user experience. Focusing on the user experience adds another dimension to the full-stack development position, as you may need to collaborate with other teams or directly with customers to complete the project. This may be both artistically gratifying and stressful, but it's crucial to know how well you work in multidisciplinary teams before you start.

Coding online courses vary in topic and may prepare you for a variety of web development specialisations, including Front-End Development, Back-End Development, and Full-Stack Development.

When looking for a programme, make sure the curriculum aligns with your professional objectives and includes languages, frameworks, and other courses that will motivate, support, and provide you with a competitive advantage.

With a curriculum focusing on HTML, CSS, JavaScript, React.js, Python, SQL, Java, Node, and more, the Web Development Program helps students become Full Stack Web Developers. Live instruction, Computer Science curriculum, career preparation, group lab projects that may be utilised for an individual's portfolio, and an important peer-to-peer support network are all included in the full-time or part-time programme.

What else should you think about if you want to pursue a career in data science?

Now that you've gained a deeper understanding of full stack development, it's time to think about the big picture questions concerning data science. Take into account the following:

The recruiting procedure may differ. Despite the strong need for data scientists, recruiting in this rapidly developing sector can take time and may benefit from networking and other forms of collaboration. Larger firms are more likely to have openings.

This implies that the job hunt and even the workplace atmosphere may differ from that of someone working in web development, who may have more options in terms of the sorts of possibilities accessible.

Furthermore, the majority of data scientists presently working in the area have a Master's degree, however this demographic is changing as more people enter the profession through coding schools. This does not imply that a Master's degree is necessary or desired, however it may appear to some as a barrier to entrance.

Data science is frequently recognised as a field with high growth potential. Data science has ascended the career ladder since the phrase was coined in 2008, and according to the Bureau of Labor Statistics, it is one of the Top 20 Fastest Growing Occupations in the US, with a predicted growth rate of 31% over the next ten years. It's not only a fantastic area for someone who enjoys learning and experimenting, but it's also a great job for someone who is ready to take risks and try new things.

Data science is a discipline for problem solvers who want to find answers to important issues in order to offer useful feedback and keep organisations functioning smoothly. Providing this service has a lot of value and employment stability, as well as a lot of joy for individuals with a passion for numbers.

What does a fast-track data science programme entail? Prior to the increasing popularity of online data science bootcamps and programmes, students could only learn data science through traditional routes such as a Master's degree in mathematics or accounting.

This indicates that the data science track necessitates in-depth mathematical study and knowledge of a wide range of topics. Consider how thorough the curriculum will be and how much assistance you will receive as you progress through it when you choose your programme.

With a curriculum focusing on Python, SQL, data visualisation, machine learning, linear algebra, databases, statistics and modelling, natural language processing, and more, the Data Science Program helps students pursue a career in applied statistics and machine learning.

Job preparation, helpful mentorship, and an important peer-to-peer support network are all included in the full or part-time programme. Students typically consider the peer-to-peer support network as the most valuable tool in having timely questions addressed and remaining motivated.

At the end of the day, both full stack developer vs data scientist have distinct strengths, but both are highly fulfilling careers with excellent financial and professional prospects for those willing to put in the effort.

FAQs

 

1. What is the difference between full-stack and data science?

The difference between full-stack development and data science is a full-stack developer focuses on web development whereas a data scientist focuses on data analysis. Full-stack developers program the backend of a website and work on the intuitive design of the front end of the site. Data scientists collect large data sets, analyze, interpret, identify patterns, solve problems, and create data visualization reports for stakeholders.

2. Should I become a Web developer or data scientist?

Data science is a fantastic career choice for people who enjoy learning and experimenting, as well as those who are ready to take risks and try new things. Web development is a more safe career option because it has been around for a long time.

3. Can a full-stack developer become a data scientist?

In comparison to the work you will put into mastering data science, full stack engineers may create whole software by themselves and become entrepreneurs quickly. So, if you ask most individuals who work in data science, they are also data engineers who work on SQL/Reporting, and so on.

4. Can you be a data scientist and Web developer?

Structured and unstructured data are the most common types of data that data scientists work with. A web developer may or may not collect data in order to have a better understanding of his or her clients or the market. He or she may also employ other techniques. These days, data scientists are also familiar with how websites operate.

5. Should I study data science or cybersecurity?

In comparison to adversaries, cyber security is about better managing knowledge about exploitable vulnerabilities in information systems. Unless the issue goes into the knowledge management domain, data science will assist in the production of that knowledge on both sides of the fence.

6.Is data science better than web development?

Much quicker and easier to learn than data science is web programming. Web development hardly ever requires math expertise. Data science necessitates a strong background in math of all types, programming, and other abilities.

7. full stack developer vs data scientist salary in india

According to information from the job search and company review website Glassdoor, the average yearly compensation for data scientists in India is Rs. 10 lakh. The average yearly income for a full stack developer in India is 6.6 lakhs, with salaries ranging from 2.5 lakhs to 17.0 lakhs.