This paper describes two classes of advanced data mining methods that can obtain actionable insight from cooperative education data: text mining of job descriptions and graph mining of job interview data. While these methods are not new in general, they have not been widely used in co-operative education research. A technical overview of each method is provided, followed by a case study using real data from a large North American university. The case study illustrates that the proposed methods can enable students, employers and institutions to make better data-driven decisions. For example, text mining of job descriptions can reveal sought-after skills while graph mining of interview relationships can characterize the extent of competition for jobs.