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Are you considering earning your master of computer science? Get insider advice from John C. Hart, Professor and Director of Online and Professional Programs in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Professor Hart recently participated in a Quora session, sharing advice on everything from the hottest trends in computer science research to what to consider when pursuing an advanced degree in computer science.
What are the hottest trends in computer science research in 2018?
In general, data science has been one of the hottest trends but it is a broad area that intersects computer science. We offer a general Master of Computer Science (MCS) degree program both on campus and online via Coursera that allows students to focus on four core areas from a list of nine areas of CS. We also offer a specialized track that focuses on data sciences called the MCS-DS (available online via Coursera), which sets those four areas to be machine learning, data mining, data visualization and cloud computing. All four of those are among the hottest trends in computer science research in 2018.
Of these, machine learning is by far the most popular area of CS research, because the methods are working better than we expected. We used to think artificial intelligence was all about simulating the human brain, but ML showed us that combining probabilistic models with optimization algorithms has been a more successful way to make computers act more “intelligently.” These methods have outgrown AI and we use ML now in some form in almost all of the different areas of computer science.
One of the reasons ML works is because of Big Data, using unsupervised machine learning to discover patterns from lots of data points, or supervised machine learning to create a model that can generalize from some known “answers” (human labeled data points) to a larger collection of “questions” (unlabeled data points). When you apply those techniques to databases, you get data mining. In order to get this to work on very large databases, you need to use cloud computing to spread the computation (and network bandwidth) over a larger array of processors.
Then there is data visualization, which I like to think of as the opposite of machine learning. Fred Brooks stated it very nicely in his Turing Award acceptance speech that “Human + Computer > Computer.” We’ve learned that there are things that computers can do better than humans, and ML is making that list longer, but there are still insights and patterns in large datasets that require human intuition to discover. Data visualization shows us how to format data so it can be efficiently transmitted to and effectively processed by the most powerful compute engine we are currently aware of: the human mind.
Does it really matter where you get your C.S. master’s degree?
It can matter, but in subtle ways. I think CS classes are taught well in any accredited colleges or universities. But when you take classes for masters degree in a top ranked CS program, you’re getting the same courses that professors are using for their doctoral students to come up to the state-of-the-art in an area. You’re also often learning material from professors that had a hand in the development of the field, which leads to some interesting insights and perspectives on the field. Also, top ranked programs are more selective and the resulting master’s degree can signal to a future employer that a student is stronger in the area. But the most important thing is to find a master’s degree program that is structured in a way that best works with your learning needs.
For example, our online MCS program uses the Coursera platform to deliver a master’s degree in a more flexible, asynchronous manner. The entire course is available to the student from day one so the student can work arbitrarily far ahead as needed, especially if the student knows they need to take a week or two off for a scheduled event, such as a family vacation. The courses are also based on our open-enrollment Coursera MOOC courses, so a student can “audit” one of our courses (informally sitting in for the lectures) by taking the MOOC version of the course, and then later enroll in the for-credit version and complete the projects and comprehensive exams (and get help from the instructor and teaching assistants as needed) to work toward our MCS. This works great for some people, especially when they have a full-time job, family or caretaker responsibilities, whereas others may learn better in the focused classroom lecture environment on campus.
These questions originally appeared on Quora – the place to gain and share knowledge, empowering people to learn from others and better understand the world.
Apply for your Master of Computer Science or Master of Computer Science in Data Science from the University of Illinois today or get started in one of these Specializations to start earning credit toward your degree:
The post Why a Quality CS Degree Matters to Employers appeared first on Coursera Blog.
Continue reading...
What are the hottest trends in computer science research in 2018?
In general, data science has been one of the hottest trends but it is a broad area that intersects computer science. We offer a general Master of Computer Science (MCS) degree program both on campus and online via Coursera that allows students to focus on four core areas from a list of nine areas of CS. We also offer a specialized track that focuses on data sciences called the MCS-DS (available online via Coursera), which sets those four areas to be machine learning, data mining, data visualization and cloud computing. All four of those are among the hottest trends in computer science research in 2018.
Of these, machine learning is by far the most popular area of CS research, because the methods are working better than we expected. We used to think artificial intelligence was all about simulating the human brain, but ML showed us that combining probabilistic models with optimization algorithms has been a more successful way to make computers act more “intelligently.” These methods have outgrown AI and we use ML now in some form in almost all of the different areas of computer science.
One of the reasons ML works is because of Big Data, using unsupervised machine learning to discover patterns from lots of data points, or supervised machine learning to create a model that can generalize from some known “answers” (human labeled data points) to a larger collection of “questions” (unlabeled data points). When you apply those techniques to databases, you get data mining. In order to get this to work on very large databases, you need to use cloud computing to spread the computation (and network bandwidth) over a larger array of processors.
Then there is data visualization, which I like to think of as the opposite of machine learning. Fred Brooks stated it very nicely in his Turing Award acceptance speech that “Human + Computer > Computer.” We’ve learned that there are things that computers can do better than humans, and ML is making that list longer, but there are still insights and patterns in large datasets that require human intuition to discover. Data visualization shows us how to format data so it can be efficiently transmitted to and effectively processed by the most powerful compute engine we are currently aware of: the human mind.
Does it really matter where you get your C.S. master’s degree?
It can matter, but in subtle ways. I think CS classes are taught well in any accredited colleges or universities. But when you take classes for masters degree in a top ranked CS program, you’re getting the same courses that professors are using for their doctoral students to come up to the state-of-the-art in an area. You’re also often learning material from professors that had a hand in the development of the field, which leads to some interesting insights and perspectives on the field. Also, top ranked programs are more selective and the resulting master’s degree can signal to a future employer that a student is stronger in the area. But the most important thing is to find a master’s degree program that is structured in a way that best works with your learning needs.
For example, our online MCS program uses the Coursera platform to deliver a master’s degree in a more flexible, asynchronous manner. The entire course is available to the student from day one so the student can work arbitrarily far ahead as needed, especially if the student knows they need to take a week or two off for a scheduled event, such as a family vacation. The courses are also based on our open-enrollment Coursera MOOC courses, so a student can “audit” one of our courses (informally sitting in for the lectures) by taking the MOOC version of the course, and then later enroll in the for-credit version and complete the projects and comprehensive exams (and get help from the instructor and teaching assistants as needed) to work toward our MCS. This works great for some people, especially when they have a full-time job, family or caretaker responsibilities, whereas others may learn better in the focused classroom lecture environment on campus.
These questions originally appeared on Quora – the place to gain and share knowledge, empowering people to learn from others and better understand the world.
Apply for your Master of Computer Science or Master of Computer Science in Data Science from the University of Illinois today or get started in one of these Specializations to start earning credit toward your degree:
The post Why a Quality CS Degree Matters to Employers appeared first on Coursera Blog.
Continue reading...
