Earlier this month, our office organized a career event introducing our students to the concept of Big Data and its applications. We had a great time with panelists from Standard & Poor’s, Bloomberg, and IBM. Many interesting topics were discussed, i.e., internship, interviews, and professional skills. Since some students did not manage to attend this event, we wrote a blog summarizing a couple of important points from our discussions.
The importance of internships was emphasized by all panelists. They suggest that students interested in the field take full advantage of summer vacations and do several internships before marching toward the job market. This will not only enhance your resume, but also, more importantly, (i) let you know what specific types of professional skills, e.g., programming language preferences, are currently required in the market. You do not want to end up being a Python guru but find out your favorite company uses Java or C++ exclusively; (ii) help you put your skills into practice. You may have acquired a multitude of skills, possibly more than required, through graduate school. This is a great head start, but a potential problem is that the particular application of those skills that you are familiar with is tied to your own discipline, say Chemistry, which is very different from the industry that you are going into, say finance. Thus, doing an internship will be a wonderful chance for you to get acquainted with the target practice; (iii) network, network, and network…
During this event, some students were concerned about the resource for internships. As we know, some internship positions are unadvertised and even for those advertised ones we might not be well informed. For this issue, Frank Chen, our panelist from Standard & Poor’s, made several suggestions: (i) talk to your adviser. Especially in computer science and some engineering disciplines, professors have connections with industry and maybe they themselves are founders of some companies. Talk to them. They may have first-hand information; (ii) talk to those senior/past students in your lab; (iii) get to know people in academic conferences.
Some sample internship positions at Standard & Poor’s:
When interviewing for jobs in data analysis related fields, two general skills were looked at by interviewers: hard skills and soft skills. For the former, they are normally demonstrated by your dissertation topics and the techniques you employed therein, and also by your on-site problem solving performance. Employers like to present you with real life problems and expect you to solve them within a short period of time. Thus, a working knowledge of some required techniques is almost a must. As for soft skills, HRs and interviewers look at your resumes (best organized by bullets, suggested by Frank Chen), some form of demonstrated working efficiency, and your communication skills. As revealed by James Zhang, our panelist from Bloomberg, a data team may consist of people with non-tech background, e.g., MBA, economics, fashion, so these soft skills should never be overlooked, especially when you are a tech person and you have to deliver a solution to your non-tech teammates.
Professional skills and relevant courses at GC
Here we provide a sample list of IT skills that were mentioned by our panelists. At IBM, they use their own software and platforms. For instance, SPSS is the tool used for data analysis. As for programming languages, data scientists use Java at Standard & Poor’s while Bloombergers use C++. In addition to these very specific IT software, James Zhang also pointed out the importance of several data analysis technologies/methodologies: machine learning, natural language processing, and data mining.
If you are interested in building up these IT skills, the Graduate Center regularly offers relevant courses. The Computer Science department offers a course titled machine learning annually in the spring semester. If you are versed in calculus, do not hesitate to take it. For natural language processing, a course titled language technology is offered by the MA Computational Linguistics program of the Linguistics department, normally in the fall semester. In addition, CUNY Data Mining Initiative offers regular talk series featuring a multitude of interesting topics in data mining. However, if you are a newcomer to the tech world or your IT skills are a little rusty, you can always have a fresh (re-)start by taking Computational Linguistics 1 and 2 offered by the Linguistics department, which cover programming skills in Python and basic natural language processing techniques. Many of these resources–including workshops for those just getting started on these techniques–can be found on the GC’s Digital Initiatives page.
– Jiaqi Wang