Software Engineering Immersive

This is a Full Time Course

A Well-Rounded Technical Foundation
Dive into the software development environment and the basics of computing, networks, and data structures. Discover how to build applications that meet user needs, model data, develop wireframes, and work collaboratively through version control.

Fluency in Multiple Frameworks and Stacks
Learn to build applications from the ground up using key languages and frameworks. Write high-quality JavaScript and leverage JS libraries like React to build a dynamic front-end. Incorporate functionality from third-party APIs and add complexity and nuance with Ruby on Rails, Python, Django, and more.

Beyond “Full-Stack Development”
Discover how to effectively communicate and collaborate with development teams to carry out projects from concept to completion. Work as a team in Git and GitHub while executing software engineering projects in an Agile development workflow.

By the end of our Software Engineering Immersive, you will be able to:

• Dive into core computer science elements including algorithms and data structures.
• Create responsive webpages for modern browsers using HTML, CSS, and JavaScript.
• Write full-stack applications and deploy them to cloud-based services like Heroku.
• Consume application programming interfaces (APIs) from third-party sites such as Twitter or Yelp and add complexity and nuance with Ruby on Rails, Python, Django, and more.
• Write secure full-stack applications that meet user needs and deploy them to cloud-based services like Heroku.
• Build a RESTful API using a technology like Sinatra, Rails, Django, or Express.
• Build a richly interactive, front-end single-page application using a modern framework like Angular or library like React.
• Collaborate as a team using Git and GitHub, widely accepted collaboration practices, and an Agile development workflow.
• Implement common data structures encountered in technical interview situations, such as linked lists and trees.
• Solve algorithm challenges and analyse the computational complexity of algorithms using Big O notation.