The world of tech has so many career options that it can be overwhelming to choose one. In this blog post, we review and compare two careers in vogue: data science and full-stack development. As the tech world continues to evolve, it gets even more challenging to decide whether to become a full stack developer or data scientist. Data science or full stack? Which one is for you? If you are unsure and don’t know which role has more benefits and salary or what skills you need for each position, you are about to find out!
What is a full-stack developer?
In the past years, this term has gained relevance and popularity in the tech industry. A full-stack developer is a professional able to work on both the front-end and back-end of a mobile or web application. What does this mean? It means they can handle both the visual and technical aspects of a website. Front-end developers are in charge of the design and layout of a site, while back-end developers program on the server side and interact with databases. A full-stack developer can do everything! To sum everything up, they’re proficient at doing two different jobs. These professionals need a varied and wide range of skills and knowledge to create an application from scratch. As mentioned, they work in two different roles, which can be overwhelming. They often rely on the help of a team and supervisors who can give them a hand when a project gets too complicated.
To become a full-stack developer, you need a vast combination of skills.
- On the back-end, you need knowledge of server-side programming languages, including Python, PHP, or Java. Database management is also crucial, including MySQL, the most popular one.
While these professionals can solo work on a project from scratch to finish, everyone can use a hand when work gets too complex. They often have a team who helps them and works with them to polish certain aspects of the project. Full-stack developers are highly valued in any IT company and a rare gem, but the path to becoming one is challenging and requires discipline.
What is data science?
In the era of big data and globalization, companies need someone who can combine statistics and computer science to make data-driven decisions. Thus, data science is an interdisciplinary field whose purpose is to gather, clean, and analyze data. If you wear a smartwatch or have a smartphone flooded with apps, which you probably do, you know these devices collect every tiny piece of information you could imagine. When you have large amounts of data, data scientists can collect them and analyze them, seeking patterns and solving problems for the company.
If a corporation finds out that users are disengaging with their mobile app, they might want further insights into why this is happening. A data scientist will use their skill set to clean and analyze the data, hopefully finding a convincing answer to the problem and, ideally, a solution. Data science also plays a critical role in predicting the future behavior of customers and users. Modern psychology explains that people tend to repeat their behavior, and thus, if you always go with X, you’re unlikely to switch to Y. Data scientists are there to ensure all this information is accurately collected, analyzed, and presented to stakeholders and CEOs, who then make the final decision.
They can also identify trends to play ahead of the game and make their company millions of dollars simply by understanding what users are seeking in one app: What features do they need? Or which ones are they rejecting? How can the app improve? It’s only natural that data science is a highly valued position these days.
Data science or full stack? Which one would fit you best? To become a data scientist, you need to master some skills and software:
- Programming languages: Whether one likes it or not, data scientists need a basic understanding of coding to do an excellent job. Python or R allow data scientists to easily manipulate data, build machine learning models, and analyze every tiny piece of information.
- Tableau: Tableau is a tool that allows these professionals to create interactive dashboards to present the results they’ve found to stakeholders and CEOs. It’s an essential data visualization tool for anyone who wants to work in this field.
- Statistical models: It’s crucial for data scientists to understand statistics. This mathematical specialization allows professionals to make predictions, identify trends and patterns, and solve problems even before they materialize.
Which is better?
Full Stack Developer VS Data Scientist Carrer: Which is better for you?
According to Indeed, the average salary for data science professionals in the United States is $124,693 annually, $105,452 if you lack years of experience. On the other hand, regarding the average salary of full-stack developers, they make an estimated $119,177 per year. If money is your most powerful motivator, then you might want to become a full -stack developer.
Data science or full stack developer? Full-stack development is often a more versatile field, as you will work on different tasks and projects. At one given moment, you can be designing the layout of a website. At the very next moment, you begin programming the back code that allows the site to run. Data scientists, on the other hand, have a more repetitive job: Collect data, clean data, analyze data, identify patterns, trends, and problems, visualize data, and write reports with your insights. Both positions have their good things and downsides. In the end, it’s about finding what works best with your personality and interests.
Full stack or data science? Becoming a full-stack developer takes more work and is more challenging, but it also comes with better job conditions. Data science is easier to master, yet it can be repetitive for some people and even boring for those without an analytical and visual mind. Ultimately, the choice depends on your career and personal goals and where you see yourself in the next five years.
Deciding between data science or full stack development can be challenging. Full-stack developers program the back end of a website while also working on the design and intuitivity of the front end of a site (what customers see). Data scientists collect large data sets, analyze them, identify patterns, solve problems, and create data visualization reports for stakeholders.
Definitely! Online bootcamps like the ones we offer at TestPro help you become familiar with all the skills and tools you will use in a nine-to-five job. It takes dedication and patience, but once you complete a bootcamp, you’re ready to join the workforce.
Yes, you can. Online bootcamps for full-stack development like the one we offer at TestPro give you the perfect opportunity. They are flexible and help you manage your own time, but require effort too. You will learn all the skills you need, which is not an easy task. It takes dedication and patience, but once you complete a bootcamp, you’re ready to join the workforce.
According to Indeed, the average salary for data science professionals in the United States is $124,693 annually. On the other hand, regarding the average salary of full-stack developers, they make an estimated $119,177 per year.