Welcome to the datascience@berkeley Blog
By now, you’ve heard the news — the UC Berkeley School of Information is launching the Master of Information and Data Science (MIDS) degree delivered online, and we’re excited about it. Now, it’s time to share that excitement with you.
This blog is not just about our program, nor is it about us. This blog is about data science as a whole, a field that encompasses…everything. What do we mean by that? Every interaction – health care, education, e-commerce — produces data. We have information about traffic jams, graduation rates, poverty levels, and more. And, in order to make sense of this massive amount of data, we need people who can identify the relevant pieces and use them to make smart decisions that will truly make an impact.
We have some pretty lofty goals and dreams for this blog, but this is the perfect time to have them. After all, data science is continually being written up as the career of the future, and ours is the first online Master of Information and Data Science degree in existence. Why shouldn’t we be the ones to aim high?
We need your help, though. Data science, at its most basic level, is about community — developers, comp sci fiends, social scientists, analysts, and intelligent people of all ilk working together to solve some of the most complex problems of our time. We need your help to be engaged. We need your help to tell us what you’d like to hear. And above all else, we need your help to keep right on doing what you’re doing, excelling in your field and pushing the limits of data science. Because after all, we may be writing about you some day in the very near future!
Who am I?
There’s a time and place for nameless, faceless blogs, but this isn’t it. We didn’t want to distance ourselves from our readers, since it’s becoming more and more apparent that data science must be transparent (PRISM, anyone?) if its applications are to be accepted by mainstream society. With that in mind, I’d like to introduce myself.
My name’s Jenna. I’d love to consider myself a professional polymath, but I’ll have to go with “an enthusiast of everything” (trademark pending). From linguistics to medicine to archaeology to forensics, I’ve tried it all and I’ve loved every minute of it. Limiting myself to one topic always sounded stifling to me, and luckily, data science gives me the freedom to explore all of these fields.
How did I get here?
In undergrad, I studied anthropology, with a concentration in skeletal biology. Because this process requires broad catalogs of artifacts and remains, I worked extensively with SPSS databases when completing my thesis.
Coming face-to-face with large datasets as a teenager was certainly intimidating, but I found myself fascinated by the amount of information these analyses could yield. Who were these people I was studying? They’d been buried for hundreds of years by the time I got to them, but I could bring them alive just by querying the extensive datasets I’d been handed. I saw how they’d been injured, if they were employed, where they’d been born, how they died. It seemed amazing to me that formulae and calculations applied to strings of numbers could somehow conjure up these living, breathing people.
I was a convert to data science from those first interactions with SPSS, and my respect for the field and the scientists working within it has only increased as I’ve learned more about it. Degrees and certifications like this have been a long time coming, and this kind of professional training is going to push the field leaps and bounds beyond where any of us are now.
Why data? Why not data? I feel about data science the way I’ve always felt about anthropology: for any subject you want to study, there’s a data equivalent.
Netflix crunches data to bring you movies, Amazon’s employing data scientists to bring you toys and technology, companies around the globe are using data to correct issues with urban planning, health care, and education, and others are currently working on projects we can only dream of.
There is a caveat. Data’s ubiquitous, but that doesn’t mean it can be interpreted by just anyone. As this field grows and changes, one main component will remain the same: we’re going to need people with the proper training, skills, and more importantly, the wherewithal to realize that all data is not, in fact, objective. As contrary as it may seem, truly objective data is what emerges after all of the noise is cleared away, a process requiring human intervention.
Sounds like a conundrum, but the data scientists who grasp this concept are going to be the ones who shape the future of the field. Count yourself among them! With the development of new programs, classes, and many, many job opportunities, the field of data science is hurtling forward at the speed of light. It’s the perfect time to jump on board and make a name for yourself.
I’m excited to be going on this journey with the UC Berkeley School of Information, especially because it comes at such a pivotal moment in the discipline. Ours is the first and only online Master of Information and Data Science degree. Our UC Berkeley faculty is top-notch, each professor an expert in his or her given area, and we’re gearing up to launch our first cohort with some great students. It’s the perfect storm of talent, hard work, and a rapidly growing field — will you be joining us?
Check back here for news articles, commentary, video, and more, all from the field of data science. We’ll have interviews with students, one-on-one interactions with our world-renowned UC Berkeley faculty, and posts on current news, developments, and innovations in the industry, as well as guest posts by some big name data companies. There’s so much happening right now in the world of data science, which gives us a great opportunity to explore, debate, and really learn from each other. I’m excited to be a part of it, and I hope you are too.
Looking forward to seeing you around the blog.
YOU MIGHT ALSO LIKE
This week in data — ESPN poaches Nate Silver from the New York Times, police scan license plates for signs of theft, volunteers crowdsource radiation tracking in Japan, smart cars get even smarter, and Netflix creates television with you in mind.
YOU MIGHT ALSO LIKE
A recent DataEDGE 2013 panel explored the emerging debate over what happens to the digital data that is left behind after a person has passed away. Stephen Wu touched on the legal aspects of the “digital afterlife.” One of the key questions at hand — can digital assets be considered “property,” or do they require new guidelines for appropriate disbursement? How can you best protect your digital assets, and yourself?
YOU MIGHT ALSO LIKE
Public health professor Hans Rosling shows how data visualization tools can “animate and liberate” United Nations’ data about developing nations in his 2006 TED Talk.