In today’s world, going online has never been easier. From the first web pages of the late 20th century to the early 21st-century web of mobile applications and social media platforms, each web iteration has brought new opportunities for data science professionals, developers, and designers. Web design and development offers data scientists the opportunity to learn how to create websites and applications or break into tech careers. Data science students or industry professionals skilled in web application tools can easily transition into a web developer or designer career!
Introduction to Web Application Development
Web application development uses programming tools to create applications for web-based environments. Web applications are types of software that run in browsers, so users don’t have to download the software onto their computers. Hence, web applications are accessible to a network of users across computers and programs connected to the internet. Like mobile application development, web application development integrates technology design, user experience, and data collection using a database and servers.
Web application development is categorized into front end and back-end development, with the front end focusing on what users see and the back-end on what they do not see. Web application developers use the same front end development programming languages used to create websites, i.e., JavaScript, HTML, and CSS. Back-end development also requires training in analytics languages like Python, SQL, and Ruby and an understanding of SQL and NoSQL database management systems. Web applications are hybrid models that incorporate the development of websites and mobile applications.
Moving from Data Science to Web Application Development
Web application development and data science both rely on data. While data scientists collect data to find patterns and trends, web application developers use data to improve product design user experience. Developers use front end and back end development to construct the appearance and management of user data and technologies. Similarly, data scientists use multiple tools and methods to store, analyze, and visualize data for stakeholders and team members.
Both data scientists and developers are expected to have extensive knowledge of programming languages and facilities with data science tools and database design. Each role also requires skills in presenting information and technology in a simplified way for diverse audiences. Data scientists interested in careers with science and technology companies, such as software engineering, social media, and web design, will often pursue opportunities in web application development. Moving from data science to web application development requires data scientists to apply their knowledge and training in programming languages to learning web application tools.
Top Web Application Tools for Data Scientists
One of the easiest ways for data scientists to learn web application development is by practicing with web application tools. Like data science tools, web application development tools include programming languages, databases, and programs that are useful for web developers. Data scientists can use these same tools to create their applications and develop a portfolio of web development projects.
1. JavaScript
As one of the most popular data and computer science programming languages, JavaScript is a go-to for both beginners and experts. JavaScript is known for its libraries, frameworks, and user community and is the foundational language of the world wide web. JavaScript’s extensive collection of resources and learning materials specifically for building web applications makes development even more accessible. For example, data scientists can use open source libraries like React.js to build a web application’s front end. Frameworks like Express.js and Node.js are used for back-end web development.
2. HTML
HTML (Hypertext Markup Language), like JavaScript, is a language used for website and application development. The Internet is built around hypertext, a feature that allows users to create links between websites and pages. HTML is a shorthand used to mark up a webpage, indicating and identifying the page elements with unique tagging features. HTML and CSS are commonly taught together so that data scientists and developers can design the front end of their web programs.
3. CSS
CSS, or Cascading Style Sheets, is a standardized language for web design and development that describes the layout of a website or application. For example, CSS is used to edit the front end of a web page, including its font, colors, spacing, etc. Data scientists and developers learn CSS to customize the appearance of their programs.
4. MongoDB
MongoDB is the most popular NoSQL database used by data scientists and developers. According to the 2021 Stack Overflow Developer Survey, MongoDB is the fourth most common database used by this group overall. MongoDB is an open-source database management program that runs at the back end of a web application to collect user information and data.
5. React Native
React Native is an open-source platform for user interface design and front end development that includes multiple features and capabilities for web application development. As an open-source JavaScript framework, React Native is essential for data scientists and developers constructing a multi-platform application. The platform was created by Facebook (Meta) and supports a community of users and resources to make the process of developing native applications accessible to all users.
Interested in Becoming a Web Developer?
There is enough overlap between building a career as a data scientist and working as a web developer to make the career-change path smoother. Learning web application tools is essential for data scientists looking to begin a career in this industry. Additionally, data science students and professionals interested in becoming web developers can take data science, web design, and web development courses. Noble Desktop’s data science classes and certificate programs include training and experiences applicable to both careers.
Aspiring web developers can benefit from courses like Python for Automation, which includes training in web scraping and gathering data from the internet. In addition, the JavaScript Development Certificate teaches students and professionals various web application tools, such as HTML and MongoDB. The Web Design Certificate prioritizes front end development training with hands-on experience in programming languages like JavaScript, HTML, and CSS.
In today’s world, going online has never been easier. From the first web pages of the late 20th century to the early 21st-century web of mobile applications and social media platforms, each web iteration has brought new opportunities for data science professionals, developers, and designers. Web design and development offers data scientists the opportunity to learn how to create websites and applications or break into tech careers. Data science students or industry professionals skilled in web application tools can easily transition into a web developer or designer career!
Introduction to Web Application Development
Web application development uses programming tools to create applications for web-based environments. Web applications are types of software that run in browsers, so users don’t have to download the software onto their computers. Hence, web applications are accessible to a network of users across computers and programs connected to the internet. Like mobile application development, web application development integrates technology design, user experience, and data collection using a database and servers.
Web application development is categorized into front end and back-end development, with the front end focusing on what users see and the back-end on what they do not see. Web application developers use the same front end development programming languages used to create websites, i.e., JavaScript, HTML, and CSS. Back-end development also requires training in analytics languages like Python, SQL, and Ruby and an understanding of SQL and NoSQL database management systems. Web applications are hybrid models that incorporate the development of websites and mobile applications.
Moving from Data Science to Web Application Development
Web application development and data science both rely on data. While data scientists collect data to find patterns and trends, web application developers use data to improve product design user experience. Developers use front end and back end development to construct the appearance and management of user data and technologies. Similarly, data scientists use multiple tools and methods to store, analyze, and visualize data for stakeholders and team members.
Both data scientists and developers are expected to have extensive knowledge of programming languages and facilities with data science tools and database design. Each role also requires skills in presenting information and technology in a simplified way for diverse audiences. Data scientists interested in careers with science and technology companies, such as software engineering, social media, and web design, will often pursue opportunities in web application development. Moving from data science to web application development requires data scientists to apply their knowledge and training in programming languages to learning web application tools.
Top Web Application Tools for Data Scientists
One of the easiest ways for data scientists to learn web application development is by practicing with web application tools. Like data science tools, web application development tools include programming languages, databases, and programs that are useful for web developers. Data scientists can use these same tools to create their applications and develop a portfolio of web development projects.
1. JavaScript
As one of the most popular data and computer science programming languages, JavaScript is a go-to for both beginners and experts. JavaScript is known for its libraries, frameworks, and user community and is the foundational language of the world wide web. JavaScript’s extensive collection of resources and learning materials specifically for building web applications makes development even more accessible. For example, data scientists can use open source libraries like React.js to build a web application’s front end. Frameworks like Express.js and Node.js are used for back-end web development.
2. HTML
HTML (Hypertext Markup Language), like JavaScript, is a language used for website and application development. The Internet is built around hypertext, a feature that allows users to create links between websites and pages. HTML is a shorthand used to mark up a webpage, indicating and identifying the page elements with unique tagging features. HTML and CSS are commonly taught together so that data scientists and developers can design the front end of their web programs.
3. CSS
CSS, or Cascading Style Sheets, is a standardized language for web design and development that describes the layout of a website or application. For example, CSS is used to edit the front end of a web page, including its font, colors, spacing, etc. Data scientists and developers learn CSS to customize the appearance of their programs.
4. MongoDB
MongoDB is the most popular NoSQL database used by data scientists and developers. According to the 2021 Stack Overflow Developer Survey, MongoDB is the fourth most common database used by this group overall. MongoDB is an open-source database management program that runs at the back end of a web application to collect user information and data.
5. React Native
React Native is an open-source platform for user interface design and front end development that includes multiple features and capabilities for web application development. As an open-source JavaScript framework, React Native is essential for data scientists and developers constructing a multi-platform application. The platform was created by Facebook (Meta) and supports a community of users and resources to make the process of developing native applications accessible to all users.
Interested in Becoming a Web Developer?
There is enough overlap between building a career as a data scientist and working as a web developer to make the career-change path smoother. Learning web application tools is essential for data scientists looking to begin a career in this industry. Additionally, data science students and professionals interested in becoming web developers can take data science, web design, and web development courses. Noble Desktop’s data science classes and certificate programs include training and experiences applicable to both careers.
Aspiring web developers can benefit from courses like Python for Automation, which includes training in web scraping and gathering data from the internet. In addition, the JavaScript Development Certificate teaches students and professionals various web application tools, such as HTML and MongoDB. The Web Design Certificate prioritizes front end development training with hands-on experience in programming languages like JavaScript, HTML, and CSS.