Data Science Vs. Web Development: A Complete Guide (2023 update)
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Web development isn’t new to the job market, but data science is a more recent concept. Data Science and Web Developers are in high demand right now. However, the need for those capable of analyzing information for the company’s benefit and insight is quickly growing.
Growth in both fields moved upwards of 35% in the last year, and there’s no indication they’ll slow anytime soon. Salaries in each career path are no different; these in-demand careers also mean the pay is climbing.
Those with a passion for technology will enjoy a career in either field, but it all comes down to which one is better for you. This article will review everything you want to know about the differences between Data Science and Web Development.
There are differences between the two fields, such as skills, career outlooks, and responsibilities. Those interested in technology can easily navigate the difficult decision of choosing data science or web development.
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Important Differences Between Data Sciences and Web Development
Data Science and Web Development are different concepts altogether. Data Science experts are tasked with analytical interpretations of computer sciences and creating innovative solutions based on the collected data. Web developers are creators of new websites and applications.
Where Web Developers focus on creating and maintaining new websites and applications, people in the Data Science field often work with AI, Finance, and other roles that analyze information. Data Science is a field for those who enjoy statistics and collecting information.
Both career opportunities rely heavily on Python, but Web Developers use it to create new websites. Data scientists use Python to analyze information. Web Developers don’t have to understand Data Science to perform their job duties, but people working in Data Science need to understand Web Development.
Important Data Science Skills
Data Scientists use many skills every day to perform their job duties well. Critical aspects of data science are collecting, analyzing, and interpreting data. Communication skills are essential to individuals in this role because they need to take complex information and relay it for others to understand.
Data Scientists make educated and informed decisions for businesses too. These decisions must weigh heavily on statistical data and accuracy.
- Statistics
- AI and Machine learning
- Python, Statistical Analysis Systems, and more
- Translating raw data into easy-to-read formats
- Creating graphs
- Structured Query Language (SQL) to extract information
- Unstructured Data
Skills such as creating graphs and other visual aids mean Data Scientists need to have a creative side because this is one of the best ways to convey information to others. Teamwork is another core aspect of the role.
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Important Web Development Skills
Web Developers do much more than input code and create applications or websites. Web Developers work with teams of other developers, managers, and end-users. An employer expects Web Developers to have soft and hard skills, meaning expertise in the technical and team-related aspects of the job.
- Client-side coding, HTML, CSS
- Sites that function on different browsers
- JavaScript
- Database creation and maintenance
- Teamwork
- Troubleshooting
- Back-end programming languages like Python, Ruby, Java, etc.
- Search Engine Optimization
Although the skillsets expected of Web Developers aren’t nearly as analytical as those of Data Scientists, the role and its responsibilities are vital to company growth and client satisfaction.
Data Science Job Opportunities
One key skill data science experts need for success is communication. This role is incredibly analytical; therefore, the data scientist must relate complex ideas in a simplified way. Data scientists must also have similar analytics mindsets as scientists in other fields. Although it’s a newer field, Data Science appeals to industries such as tech startups, large companies, and research engineers.
On the low end, Data Science workers make about $71,000 annually. Those with more can earn upwards of $130,000 per year. Titles range from Data Scientist to Senior Data Analyst and DataOps engineer. Machine Learning Engineers and Business Intelligence Operators also fall under the Data Science umbrella.
Career satisfaction is higher in this field because it offers many advancement opportunities and promotes work/life balance.
Web Development Job Opportunities
Web developers do a lot of research to meet their client’s and company’s needs. Like data scientists, web developers must relay technical ideas to people with little knowledge.
As a career path, web Development continues to grow. Approximately 25,500 new web development jobs opened up between 2020 and 2029. But opportunities vary depending on one’s scope of expertise.
Developers can make between $55,000 and $108,000 annually, with a median salary of $77,000 annually. Outstanding growth opportunities, job flexibility, and work/life balance await those entering the Web Development sector.
What is Easier to Learn?
Many successful Web Developers are independent learners. The discipline requires little instruction, and therefore, anyone can do it. But this also means there is more competition. On the other hand, Data Science involves more math and science. While some colleges have begun to offer the field, it takes a little more time and knowledge to become a successful Data Scientist.
However, Data Scientists are still a new concept; therefore, the market is still reasonably competitive. After researching the field, it’s best to choose the one that suits your passion and decide which course you’d like to take.
Of course, as stated previously, you can start in Web Development and move to Data Science when you have a better command of Python and an understanding of complex technical skills.
Making the Most of Tech Skills
Data Science is the perfect field for those who like to work with information and better understand trends. Web development is the field for those who love to create new things but, perhaps, don’t want to analyze data at the highest levels.
