Is Data Science For All the New Computer Science For All? (berkeley.edu)

UC Berkeley's fastest-growing class is their introduction to data science. (The Wall Street Journal calls it a combination of computer science and statistics "to mine the growing troves of data on everything from traffic patterns to the habits of social-media users.") But that's only the beginning. UC Berkeley plans to create a new Division of Data Science -- one of their biggest reorganizations in decades -- and this fall they even began offering a major in data science. "The division will enable students and researchers to tackle not just the scientific challenges opened up by pervasive data, but the societal, economic and environmental impacts as well."

"We need to consider the ethical implications of these technologies as they are being developed," says Data 8 instructor David Wagner -- "what does the world look like when decisions are made by algorithms rather than people, and how do we ensure that when we analyze data our decisions reflect not just numbers but the humans behind them?"

Slashdot reader theodp writes: With a reported 1,295 students enrolled this semester, Berkeley's Data 8: The Foundations of Data Science boasts even bigger numbers than Harvard's most popular course, the more traditionally CS-focused CS50, which saw 724 students enroll this Fall....

Berkeley's embrace of Data Science coincidentally comes as Code.org is giving kudos to partners Microsoft, Facebook, Google, and Amazon for helping it convince lawmakers and tens of thousands of educators that more traditional computer science is what's needed for the K-12 masses, including the adoption of a new AP Computer Science program for high school students (an AP CS version of CS50 was funded by Microsoft).

So, is Data Science for All the new Computer Science for All? And, if so, will U.S. schools be looking at a major case of buyer's remorse?

Comments

Capitalism

[Anonymous Coward]

As with all things, education needs to be tied to reality.

Society needs (or wants!) certain things, and the only sustainable and humane way to figure out what society wants, how much of it society wants, and who should be paying for it is Capitalism.

Data science for all? Let those businesses who are seeking data scientists recruit promising folks, and pay for their education in an apprentice-style program. The government has business playing around with this nonsense.

Can't you people see it? It's right in the goddamn summary:

Berkeley's embrace of Data Science coincidentally comes as Code.org is giving kudos to partners Microsoft, Facebook, Google, and Amazon for helping it convince lawmakers and tens of thousands of educators that more traditional computer science is what's needed for the K-12 masses

Government corrupts business, not the other way around. They are just using their deep pockets to pay Big Government to swing its pistol this way and that; there needs to be a Separation of Business and State; there needs to be a Separation of Education and State.

If it's a trendy major it's already too late

[Hognoxious]

Four years from now the alumni of these courses will be able to take data about the number of college courses, the number of graduates emerging therefrom, the number of jobs available and the salaries offered and spot some really interesting patterns.

Because one thing's for sure - they'll have the time.

Re:Splitting of diciplines.

[garcia]

Data Science is more than the math-heavy side of CS; it should include a lot of business courses too as the single most important part of being a Data Scientist is understanding the business context of the models being built.

Business Analytics courses try to make a Business-heavy Data Science program; however, there can be balance there, IMO.

I have worked in the field (Data Engineering/ETL focus) for a decade and watched the massive changes in tools, need and understanding. These sorts of programs are doing a great job but still need to do more, based on what Iâ(TM)ve seen to date.

Heh... "ethics"

[MikeRT]

How about a basic course in logic, like the people who objected to Amazon's AI resume reviewer preferring men getting taught what "post hoc ergo propter hoc" means.

The only thing that should concern us even more than black box algorithms is knowing that the people above will not rest until the algorithm gives them the expected output. Even if that means effectively demanding "garbage out, no matter the input."

The problem is industry doesn't know what it wants

[Shadow of Eternity]

Every single job I see with the word "data" in the name has some of the most comically excessive and overbroad requirements along with every adjective in the thesaurus for "expert". Positions described as "entry level" demand 5+ years of experience in a half dozen technologies ranging from python and SQL to tensorflow, hadoop, spark, and you have be a ninja, wizard, expert, and rockstar in all of them. As for degrees? That's the most hilarious part. They'll take anything from computer science to economics as long as it's a "highly quantitative field".

Personally I'd rather take someone who proves they understand how to work with noisy and ugly real world data, and tell when the numbers are bullshit, and teach them to code than take someone who knows how to code and try to teach them to grok data.