Make better lending decisions through data analysis education
Getting back to the question of when to start big data education: I contend that the best time to commence data analysis education is when the student encounters data of interest. A classic example of introducing histograms is to march a large class of captive statistics students to a field and arrange them in columns of comparable heights -- . Surely, the students participating will remember the experience and maybe even recall something about bi-modality. This example suggests that earlier opportunities of statistics had not been exploited.
Data analysis is the study of methods and approaches on how data gathered in various ways is formed into models and higher or more specific insight. The education emphasizes data gathering, processing and visualization. The goal of the data analysis education is that a graduated Master of Science is able to solve complex problems by utilizing mathematical, statistical, computational and information technological skills combining them to other research data.
A related and more pertinent question is when should one's data analysis education begin? A few years ago, I visited my daughter in Japan, where she was teaching English as a second language via the wonderful . In a third-grade mathematics class, the day's lesson involved collecting data on the favorite sports of each student in the class. Each student in the class of about 35 kids came to the front of the class, picked a magnetic plaque with their favorite sports name (soccer, running, table tennis, etc.) and put it on the blackboard.Students will be offered a data analysis curriculum that utilizes IBM Watson. The curriculum will be organized by the Faculty of Information Technology and all students enrolled in the University of Jyväskylä are entitled to attend the education. In the first phase of the curriculum, the data analysis education is targeted at Master’s degree students in statistic, applied mathematics and computational sciences. The curriculum enables students in these fields to profile themselves in large data mass analysis from each discipline’s point of view using various research tools.