Berkeley Online Masters Computer Science – The next opportunity for the M.S., M.S./Ph.D. and Ph.D. Programs starting in fall 2024 will be available in September 2023.
You can apply for M.S., M.S./Ph.D. or Ph.D. program in either Electrical Engineering () or Computer Science (CS). To apply, you must select a department. The table below shows which areas of specialization fall under the EE division, under the CS division and under both.
Berkeley Online Masters Computer Science
** Please note that on the official Grad Division application page EE is referred to as Electrical Engineering and Computer Science () and CS is referred to as Computer Science (CS). EE applicants must make sure to apply to CS, and CS applicants make sure to apply to CS.
Eecs At Uc Berkeley |
The student-run Equal Access Assistance Program (EAAA) aims to ensure that all applicants for graduate (MS/PhD) programs in the Berkeley department have access to guidance on the graduate application process. A current (or recent graduate) will provide feedback on your statement of purpose, personal history statement, resume/resume and other application materials before application deadlines for higher degrees in the fall semester. This feedback will be Berkeley and entry focused, as opposed to grammar or formatting advice.
The application for admission to the UC Berkeley department is a process that must be completed separately; Participation in the EAAA does not guarantee admission nor does it influence the admissions decision-making process in any way.
The single MS degree is primarily intended for currently enrolled UC Berkeley Ph.D. Students who want to add the degree. We occasionally admit exceptional applicants with research experience, but the cohort is generally limited to less than 10. Students interested in a research-oriented degree should apply directly to the MS/PhD program.
If you plan to enter the engineering profession directly without obtaining a doctorate, the Master of Engineering (M.Eng.) is specifically designed as a professional master.
University Of California, Berkeley (haas)
If you have a Ph.D. student at one of the other UC campuses and is applying for the M.S. degree in , please note that UC Berkeley may charge additional fees in addition to your home campus fees.
A Ph.D. in combines coursework and original research with exceptional faculty leadership to prepare students for careers in academia or industry.
The department offers two types of Ph.D. degree (EE and CS). The main requirements for the Ph.D. is:
* The Master of Science (MS) Only program is a very small research program for exceptional applicants with research experience. Applicants should consider applying to the MS/PhD program, as there are very few students in the M.S. program only. With Phase II set for the upcoming spring semester for many sophomores and juniors, there is often a sudden rush among students to decide their plans for the upcoming semester, including which classes to take. Looking at the long catalog of classes offered by Berkeley’s EECS department, such a decision can sometimes seem daunting: Which classes are light on the workload? How many midterms do I have to take? This class sounds cool, but do I need a friend to take it with me on projects? What will I learn in this class and will it be useful for industry or research?
Ph.d. Student Guide
These are all questions that every student thinks about every semester when it’s time to choose the next course. We thought it would be helpful to gauge our members’ opinions on the upper division CS classes they have taken and gather all this information in one place for future students to find useful!
Disclaimer: All content in this article are our own opinions only! Different people can have different experiences taking the same classes. These are just the general takeaways that the majority of our members had after each class. Also, these are definitely not all of the top division EECS classes offered at Berkeley, just the ones that many of our members have taken before! In general, since we are a club that focuses on software engineering, most of our members end up taking more systems classes as opposed to theory and math classes.
Data 100 is designated as an introductory course to the overall field of computer science, often taken after Data 8 as a bridge between lower division courses and other more advanced upper division computer science, computer science, and statistics courses.
The course covers a ton of topics, albeit at a fairly surface level, ranging from “question formulation, data collection and cleaning, visualization, statistical inference, predictive modeling, and decision making,” according to the course website. More specifically, you will learn how to use languages to transform, query and analyze data, including Pandas and SQL. You will also learn how to implement core machine learning algorithms, including regression, classification and clustering. You will also explore the principles behind creating informative data visualizations using libraries such as Matplotlib. The last half of the course is devoted to teaching statistical concepts of measurement error and prediction, and techniques for scalable computing (in some semesters Ray is taught).
Master Of Engineering (meng)
That said, these prerequisites are sometimes not enforced, and Data 8 itself is not too necessary to know content to enter the class, there have been some cases where students have been able to enroll and succeed in the class without Data 8 to have taken before. In general, it is strongly recommended to take one linear algebra course and one coding course before taking Data 100.
Overall, compared to other upper division courses, Data 100 is generally considered one of the coolest classes. Unlike some other classes, the workload is very constant and there are no times when you have to spend hours a day in class. It is often recommended that this class be taken in conjunction with one of the more intensive upper division classes, such as CS 162.
In general, most people who take this class fall into two main categories: (1) people who take it as one of their first upper-division classes as they become more involved in computer science and machine learning, or (2) students who are . don’t really think about computer science or machine learning, and are just curious to learn the basics. This class serves its purpose for both groups, although it is definitely not necessary for the first group of people: many students went directly from the EE16 series to Data 102, CS 189, and other state classes without Data 100. If you have already Taking CS 189, then taking Data 100 next will do nothing for you because you already know almost all the concepts taught and you should be able to pick up pandas easily.
Data 100 is a good introduction to data science from analytics to machine learning, although it tends to abstract away almost all the math behind the models you use. If you are looking for a great deep machine learning course, 189 or DS 102 are probably better courses for you. If you are curious about machine learning/data science in general and want to learn how to use industry standard libraries, this class is a great place to start! The work is easy (weekly labs and homework) and the professors are usually great too. 🙂
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The (online) textbook for this class is amazing!! Use it because it is very concise and good for summarizing the topics in class.
EECS 126 is an advanced course that teaches you the basic concepts of probability and random processes. It builds on introductory probability courses like CS 70 and develops a deeper mathematical understanding behind probability and how it can be applied in different fields.
Don’t be fooled by this short list! The work in this class really adds up, and this class can be a grind for most students.
Definitely do not take these requirements lightly! Since this is an advanced course, the content will move very quickly, so be sure to brush up on breaks beforehand.
Master’s In Computer Programming Programs Guide
This is a course that helps you learn design by building your own application. As of fall 2020, the class has switched to a project-based course with no midterm or final assessments. You spend the first half of the class discussing design concepts during lecture and through short readings or video responses, which usually appear twice a week (before each lecture). During the second half of the class, you will get a chance to build an Android application from scratch, which means you will go through every step of the design process: ideas, user research, low-fidelity and high-fidelity prototypes, user testing, and coding of the app.
These assignments are based on the fall semester of 2020, which was very far away. Potentially during a normal semester there is also a midterm and a final.
The official prerequisite for this class is CS 61B only! General, but make sure you know Java and understand object oriented programming. In the class, you design and code an Android application in Android Studio (which can be in Kotlin or Java).
In a normal semester, however, 160 lectures are compulsory
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