The IT (information technology) industry has always been considered one of the fastest-growing industries today. Its development has created new turning points in technology, such as data science and web development.
Therefore, it is not surprising that these areas offer many job opportunities with attractive salaries for those interested in IT.
But web development and data science: which is for you?
Let’s find the answer through this article!
The answer is dependent. For many beginners, web development is a more appropriate entry point. This area is suitable for those without a technical background. It opens up the possibility of performing data-related roles.
The choice between data science and web development should be based on your passions and preferences. Before making your decision, it is essential to carefully weigh the benefits of these two different career paths.
You should also talk to experts in these two fields and explore some of the software and languages commonly used in the two industries.
This area involves creating, building, and maintaining various web pages. Experts in this field may be responsible for web design, programming, publishing, or database management.
A typical application of this field is creating Web Applications that run on remote servers. Thanks to that, you can access them from your internet browsers.
Data science refers to the study of programming skills and in-depth knowledge of statistics and mathematics to derive valuable information and meaningful insights from collected data.
Scientists in this field often apply machine learning algorithms to texts, images, numbers, audio, video, and more, to create artificial intelligence (AI) systems to perform tasks that require human intelligence.
These systems then generate helpful and detailed information that business users or analysts can translate into tangible business value.
To help you better understand the similarities and differences between these two fields, let’s compare them in the most critical aspects, including career outcomes, job market, average salary, language recommendation, etc.
If you decide to pursue a career in the web design area, you can expect to receive one of the following positions:
- Technical product manager
- Back-end developer
- iOS developer
- Front-end developer
- UX developer
- UI developer
- Data engineer
- DevOps engineer
- Database analyst
Pursuing a career in the data science field can lead to several opportunities, including:
- Senior data scientist
- Data scientist
- Senior data analyst
- Machine learning engineer
- Data systems developer
- DataOps engineer
- Data systems analyst
- Business intelligence developer
Both the web and data markets have exploded in recent years. So, businesses today are in great demand for highly skilled professionals in both these areas.
The website design area seems to appeal to more people. There are now more than 3 million data scientists, while more than 23 million web developers globally.
The average salary of a Data Scientist is more than $94,000, while the average salary of a web developer is more than $75,000. Of course, your salary will depend on your qualifications, location, and overall qualifications.
If you want to be a web developer, you need to be fluent in these languages:
For data professionals, fluency in the following languages is essential:
The difference between the web dev and data science areas points most clearly in this table.
C/C#/C++, Java, Haskell, Julia, Python, Matlab, R, Scala, SAS, Stata, SQL
The development process is closely related to coding.
Encryption is widely used.
The average salary of a Data Scientist is more than $94,000.
The average salary of a web developer is more than $75,000.
– The requirements of the clients are never clear. In addition, they are also constantly changing until the final website is reached.
– Collaboration with IT is essential.
– The budget for website building can increase to get more features.
– Launching a new website takes time.
– Close cooperation with the client’s on-site requirements and content is essential.
– You will need to consider security factors before launching.
– The results you get from Data Science will not be used in making business decisions.
– Low level of clarity to questions that need to be answered from the collected data set.
– You may not apply the findings to your organization’s decision-making process.
– Data is difficult to access or not available.
– Data security should be a top priority.
– Collaboration with IT is essential.
Generally, these fields are great for IT enthusiasts and have bright futures. So, your choice should be based on your motivation, skills, passion, and opportunity.
Consider it carefully and pursue it patiently. As a result, you will soon become an expert in your field. At the end of the day, if you want to get through the long, arduous, and challenging journey of pursuing a career, you need to have passion.
Thank you for reading! Please share this article if it was helpful to you!