Computer Science Classes NYC

Become a Software Engineer or Full Stack Developer in this immersive course. Master the front-end using HTML, CSS, JavaScript, and React; build the back-end with Python, Django, and Node.js. You'll also learn Git for version control, Flexbox and Grid for layout, MongoDB for creating data structures, and much more. Throughout the program, you'll build a portfolio of projects to help you secure a career in software and development. 

Get to know one of the most widely-used programming languages in the world at this 5-day Java bootcamp. Students get started with Java and use it to learn essential computer science principles such as object-oriented programming, solving programming problems, developing algorithms, understanding recursion, and working with data structures. This bootcamp prepares participants to take the Java SE 11 Programmer 1 Exam.

Learn the essential skills and tools to become a Python Developer. This beginner-friendly course teaches students Python for software development with Django and Django REST in addition to other developer tools such as Git and SQL. After completing this certificate, students will be able to apply for the following roles: Python Developer, Back End Developer, Software Engineer, and many more.

Become a JavaScript Developer with this immersive certificate program. Learn object-oriented programming (OOP), create APIs, and learn popular frameworks and libraries to build functional and responsive websites. Over the course of this class, you'll learn fundamentals for JavaScript programming, front-end development with ReactJS and React Native, and back-end development with Node.js and Express.js. You'll also harness the power of MongoDB to organize and store your data. This class includes one-on-one mentoring and portfolio development to prepare you for a career after graduation.

In this 2-day introductory Python course, participants will learn the fundamentals of Python programming. This hands-on course will give students with no prior Python experience the foundational programming skills needed to continue learning Python’s applications in data science or web development. Topics include Python environment setup, programming concepts, Python syntax, and more. This course offers flexible scheduling options and provides a free retake for students to refresh the material.

In this data science bootcamp, students will build programming skills and data analysis skills using Python. This course is open to beginners and is meant to get individuals up and running with Python programming and data science to generate insights from data. Topics covered include programming fundamentals, working with data frames, data analysis, data visualization, and statistical analysis. This course offers flexible scheduling options and a free retake for students to refresh the materials.

In this course, students expand their Python programming skills into machine learning and algorithms that can independently learn patterns and make decisions. The course begins with linear and logistic regressions, the most time-tested and reliable tools for approaching a machine learning problem. Students then progress to algorithms with a different theoretical basis, such as k-nearest neighbors, decision trees, and random forest. This will bring important statistical concepts to the forefront, such as bias, variance, and overfitting. Participants also learn how to measure the accuracy of your models, as well as tips for choosing effective features and algorithms.

This 1-week Python Immersive is geared towards beginners with no prior coding experience and meant to give students a fundamental understanding of Python to start coding on their own. Students will learn best practices for coding with Python, work on exercises and programs in Python, and conclude with 2 projects of their own.

This course provides an introduction to the use of Python programming language for data analysis. Python, through the use of list and string manipulation, control structures, and data analysis packages, can be used to manage and analyze large sets of data. In this course, you will learn how to use Scipy, Numpy, Pandas, Seaborn and matplotlib to analyze data and create visualizations. This course has 4 units, covering list manipulation, strings and simple I/O, control structures and data analysis packages. IPython notebook is also used to show how codes and change codes are used during class.