Jen-chien Yu, a student in the University of Illinois at Urbana-Champaign’s Master of Computer Science in Data Science (MCS-DS) program, is a wonderful example of how online learners can immediately start to apply their new knowledge to their professional field. Yu has been able to apply skills in areas of study like Machine Learning, Data Visualization, Data Mining, and Cloud Computing to her work in library assessment, a field that aims to measure and leverage the performance and resources of libraries.
We sat down with Yu, who is the Director of Library Assessment for the University Library at the U of I – which holds one of the largest collections among public institutions in the U.S., and had her tell us more about the field, her interest in data, the applicability of her coursework to her profession, and her advice for potential MCS-DS students.
Yu describes the purpose of library assessment in higher education as endeavoring to “measure the performance of libraries.” This involves looking at metrics like the size of a library’s collection, how many people come into the library, how people interact with the library’s services, and how satisfied people are with the library. It also involves looking at a library’s many data sets.
Prior to joining the U of I seven years ago, Yu, was a social science data librarian at Miami University in Oxford, Ohio. There, she worked with faculty and students from sociology and political science and helped them find data sets, like public opinion polls, census data, or election records, and analyze the vast amounts of stored information.
This close proximity to data in both positions is what sparked Yu’s interest in getting a Master of Computer Science in Data Science. Yu uses concepts from her MCS-DS courses on a regular basis in the library. Often, she will have faculty members or students come to her and say “I have this particular issue, do you have any data to back it up?” Previously, by locating the data, Yu provided a starting point for many people’s research, but now, with her mastery over algorithms that she learned in the MCS-DS program, she can also assist with the analysis. “They come to me and say, ‘I have a huge amount of data, I don’t know how to analyze it, can you help me look at it?’ I help customize it to what they want to do, or clean it up, a lot of what I do is data-cleaning.” Techniques from MCS-DS courses like cluster analysis and principal component analysis help Yu work with the library visitor’s data set to enable them to extract the information they are looking for. Both methods discern the fastest way to group or organize large, untamed sets of variable-heavy data. “These two particular algorithms I could use immediately with the library’s data,” Yu says.
For Yu, combining knowledge with enthusiasm is a key to success. “Graduate students ask me, ‘What is the key thing to learn in the data science field?’ I think it’s easy to say go learn python, or go learn R. Those are important, but I don’t think that’s the question you want to think about. It doesn’t matter how popular a certain tool is at the time, it will fade.” Data scientists “need to be willing to dig in and say, ‘There must be a structure here, let me figure it out,’ and then ask ‘What kind of questions can I ask of this data?’ or ‘What kind of analysis can I do with this data?’” She continues, “If you don’t have that drive to understand the structure and be able to pick the right tools, then you won’t enjoy or pursue a long career in this field.”
An interest in data brought Yu to the MCS-DS, but, as a full-time working professional with two small children, the flexibility of the program has also been incredibly important. She enjoys the short, modular format of the videos, as it makes reviewing tricky concepts easy. She also enjoys being able to download lectures and listen to them while driving her family around, as it helps her balance her professional, academic, and family life. “That is what is great about online, the program makes it easy for a professional to jump on this,” says Yu.
When asked what advice Yu has for others who are thinking about studying data science she laughed and said, “Your comfort level with math should not be the indicator for whether or not you are suitable for this major — everybody should try it.” Trying data science with a mindset of interest and excitement is Yu’s strongest message. “Data science can apply to a lot of different disciplines, if you have that drive to work with data.” “Try it,” she repeats. “Just try it and see.”
You don’t have to wait to get started towards your MCS-DS degree — you can try out an open course online and begin learning today. If you later apply and are admitted to the degree program, your assignments completed in open courses can count toward completion of degree courses.9