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CS101 now self paced!

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Video Transcript:. Associated Programs:. Computer Science for Game Development. Computer Science and Mobile Apps. Share this course Share this course on facebook. Share this course on twitter. Share this course on linkedin.

Compare Introductory Computer Science Courses – Department of Computer Science | CSU

Share this course via email. Prerequisites None. Interested in this course for your Business or Team? Train your employees in the most in-demand topics, with edX for Business. Purchase now Request Information. About this course This is CS50x , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. What you'll learn A broad and robust understanding of computer science and programming How to think algorithmically and solve programming problems efficiently Concepts like abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development Familiarity in a number of languages, including C, Python, SQL, and JavaScript plus CSS and HTML How to engage with a vibrant community of like-minded learners from all levels of experience How to develop and present a final programming project to your peers.

Meet your instructors Harvard University. David J. Students have a number of options for further study. Three complementary courses are specifically designed to follow CSE 1 CSE is the continuation course for those who want to develop more advanced programming skills in Java and is the next step toward most upper-division CSE courses, 2 CSE is a web programming course covering many technologies used to implement web pages, and 3 CSE is a programming course focused on data analysis with a variety of data-science programming tools.

CSE builds on the material in CSE to develop more advanced programming skills, essential for writing more complex programs over larger and more interesting data. The format is similar to CSE , with two small weekly recitation sections. While students who already know the material covered in CSE are allowed to take CSE without formal approval, doing so is often not advised because the two courses are designed together.

CSE also may be required for other majors on campus. After CSE , students can take various upper-division computing courses. This course doubles the pace of learning—not the assignment workload.

Lec 1 - MIT 6.00 Introduction to Computer Science and Programming, Fall 2008

CSE X is designed for students who want to take CSE and CSE but feel they have sufficient experience and ability to learn the material in a condensed time frame. CSE teaches students to program for data analysis and visualization using Python, the standard programming language used in various scientific disciplines and in other data-driven fields. Students perform real data manipulation tasks and write programs that solve problems using actual data sets drawn from the sciences, engineering, business and the humanities. Upon completion of CSE , students are prepared to take what they learned in the classroom and apply it in their chosen field.

As practice for this, the course culminates in a final project in which students choose and solve their own data analysis problem, working individually or in pairs. CSE offers a firm grounding in computer programming to any student, and no previous programming experience is assumed.

Computer Programming Courses

CSE is particularly beneficial to students who wish to perform data analysis and visualization. Students who are majoring in or intend to major in one of the natural, physical or social sciences, or another data-intensive discipline, are encouraged to consider CSE as a first course.

After completing CSE , students are ready to apply data analysis independently without the need for further coursework. Students who are interested in taking additional programming classes or who intend to major in computer science or computer engineering may enroll in CSE or they may opt for CSE X, which combines material from both CSE and CSE into one accelerated course.

These courses are based on the Java programming language. Students who would like to learn how to build websites and web-based applications and have achieved a grade of at least 2. CSE is a newer course first offered in Spring , providing students who have taken either CSE or CSE with a programming course about how to write programs that a manipulate different types of data images, text, tables, etc. The course complements CSE , which focuses more deeply on fundamental programming concepts and the internals of data structures. In contrast, CSE emphasizes the efficient use of those concepts for data programming.

CSE uses the Python programming language. CSE is designed for broad interests in data science, data analysis, and programming. Students in any major may benefit from this course. This newer course is currently expanding. In academic year , we plan to offer it in Winter and Spring, with room for approximately students in each offering subject to change. After completing CSE , students will be well-positioned to pursue various data-science courses across campus, including in majors that have data-science options.

CSE provides an introduction to programming languages and tools for creating websites and web-based applications—skills that are increasingly useful in a variety of fields, as people conduct more of their professional and personal lives online. Students also learn how to use Structured Query Language SQL to build web pages that interact with databases and additional tools for creating enhanced online experiences. Students must have achieved a grade of at least 2.

CSE is offered once or twice each year, with room for roughly students per offering, sometimes more. It is a survey course introducing the essential elements of data science: data integrity including the ethics and social context of data; data methods including using tables, graphs, and other visualizations; and data analysis, including statistics methods and machine learning. It is not a programming course: rather than programming, students gain hands-on experience with other software tools and methods.

This course is a joint project of Statistics, the Information School, and the Allen School, synthesizing the perspectives and expertise of the units. The offerings cover the same material, though naturally it is a new course that will evolve. No previous exposure to programming or statistics is expected.