There’s been lots of talk of whether a master’s degree in library and information science is worth the cost. Only time will tell if this MLIS (Masters in Library and Information Science) student’s investment pays off. In the meantime, I’m continuing my survey of job opportunities in this field. This week, I’ve been thinking about data science.
What’s data science?
Donate to support a local public school, pick up a six pack of beer at the grocery store, Google directions to the closest record store: you’ve just generated data. Everywhere we go, we leave behind streams of data that can be analyzed by organizations of all types for information. So, what is data science? It’s combing through this gigantic and growing mess of data to look for trends and bits of meaningful information.
Consider what Mike Loukides of O’Reilly Media Inc. wrote in 2010:
The question facing every company today, every startup, every non-profit, every project site that wants to attract a community, is how to use data effectively — not just their own data, but all the data that’s available and relevant. Using data effectively requires something different from traditional statistics, where actuaries in business suits perform arcane but fairly well-defined kinds of analysis. What differentiates data science from statistics is that data science is a holistic approach. We’re increasingly finding data in the wild, and data scientists are involved with gathering data, massaging it into a tractable form, making it tell its story, and presenting that story to others.
How to Become a Data Scientist
Students in MLIS program here at Syracuse University’s School of Information Studies can choose to take classes that will prepare them to work in data science. We can take classes on database development and management, information systems project management, research and statistical methods for information science and technology, information architecture for internet services, and data mining.
A data scientist needs the math and computer science skills classes like these provide. Good communication skills are vital, since many data scientists work in teams. If you’re also able to understand where a company’s coming from, you’ll be able to turn pools of data into information that cab drive business decisions. You’ll become indispensable.
What’s the Bottom Line?
So, what does this mean for the career of tech-minded MLIS grads who do data science coursework? It means you’ll be likely to land a lucrative job in the field of data science. Big data has big potential.
Data science is a growing field. The preservation of culture and modern knowledge is becoming more dire every day as electronic records are lost. Digital preservation is a brand new field where stakes (losing our cultural heritage) are high. (Read about DigCCurr for more info.) As more corporations begin making decisions based on the analysis of data, they need creative curious data scientists who can work collaboratively with programmers, graphic designers, and statisticians to run data experiments. (Read “Data Science Revealed: A Data-Driven Glimpse Into the Burgeoning New Field” for more compelling info.)
Just look at this persuasive infographic:
Exploding Data by Column Five Media, http://bit.ly/1K2wvBS
Have I convinced you to learn more about jobs in data science?
Search for jobs here:
Search for data science jobs using keywords like these:
pattern based strategy
Search for job titles like these (click for real job postings):
business intelligence specialist
competitive intelligence managerdata scientist
data curation specialist
data mining specialist
manager of market research analytics
data mining specialist
data analytics engineer
manager, strategic planning and data analytics
data visualization specialist
digital curation librarian
data services librarian
Are you a librarian with a job in data science or a related field? What’s your job title? What keywords and job titles would you use to look for positions in this field? Where should I search for data science jobs? Leave suggestions in the comments.