Questions and answers regarding Big Data or the Master of Science in Applied Data Analytics.
What is the short answer to the question "What is Data Analytics"?
Data Analytics is a process of inspecting, cleaning, transforming and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making. In a nutshell, it is a series of tools and techniques to help you ask the right questions or confirm what you suspect to be true. In addition, there are tools to help you visualize (or report) your findings to explain the results to upper management.
How do we turn a generalist with domain knowledge into a lean, mean, data-driven problem-solving machine in only 30 credits?
The solution is a fairly strict prerequisite structure where we can use a sequence of courses to build up a set of skills that can be applied to a greater variety of problems. This is similar to the need to progress from addition, to multiplication, to algebra, to trigonometry, to understand and apply calculus to many mathematical problems.
What is the difference between "Big Data" and "Data Analytics" and why are they sometimes used interchangeably?
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Big data is one of those buzz words that the media has applied to the most data intensive problems that data analytics can be used to solve. There are many new tools for efficiently processing very large data sets to discover information and we will focus on those.
What background is necessary to enter the program with a reasonable chance of success?
This program is for students of any undergraduate major because data is everywhere. The defined prerequisites are an undergraduate statistics course and an undergraduate computer programming course. We would also expect a certain level of computer literacy such as the ability to install software on your own computer, navigate your file system, and competency with office productivity software including word processing and spreadsheet programs. Students will also need full administrative access to a computer with a reliable internet connection. Incoming students who lack any of these prerequisites can take an appropriate course online from Clarion University the summer before they begin their core data analytics courses.
So how do you implement a strict prerequisite structure without taking forever to graduate?
The solution here is to break away from the traditional university practice of offering all courses over 15-week semesters while still working within the university's established academic calendar. The courses will be sequenced so that a student can enter the program in the summer if they need prerequisite coursework in statistics and/or computer programming. They would then take the core database courses in consecutive seven-week periods in the fall, a three-week course over winter, two consecutive seven-week courses in spring, three consecutive courses in summer, a 15-week course in fall and a capstone 15-week course in spring. That will allow a student to graduate within two years without ever taking more than one course at a time even if they have to complete the prerequisite coursework. The down-side to this approach is that when a student starts, they must continue in order through the courses. If they skip a course (other than their elective or CIS 570), they may have to wait until the following year to continue the program.
How much time will a student need to dedicate to this program?
Make no mistake, this is a hands-on, technical program and you are dealing with leading-edge software to find real solutions to problems. This program will require a substantial commitment to complete successfully. Some students will have to dedicate much more time than others based on their technical ability and background. The courses are offered in sequence so your attention will not be divided too many ways so you can balance work, family, and academic commitments but you will definitely have to manage your time wisely.
How many credits may a student transfer to apply to this degree?
The Applied Data Analytics master's degree program is the minimum of 30 credits. Although a student may transfer in up to nine credits, only three of those credits are elective so it is unusual to transfer in more than three credits. CIS 570 (Project Management) is another course that could be transferred in. The strict prerequisite structure for the other courses will demonstrate specific tools in earlier courses and apply them in later courses. This makes it difficult to match a database course from another university with the one that is part of this program.
What if a student took one or more of these required core courses at Clarion University for undergraduate credit and cannot repeat them for graduate credit?
Initially, that will most likely be an Information Systems or Computer Science major but we are planning to offer a Data Analytics baccalaureate program in the near future so it will become more common. In this case, additional elective courses will be taken to reach the 30-credit minimum required for graduation and those electives may be transferred in from another accredited graduate program up to a maximum of nine.
The program is designed for a student to begin in the summer or fall and finish in May two years later. Although only one course at a time is taken, skipping a course could require an extra year to retake it.
Can a student go full-time and graduate in one year?
No because the prerequisite structure does not allow multiple courses to be taken in parallel.
Can a student start in the spring semester and finish sooner?
Not really. They could possibly work on prerequisite coursework or their elective but the completion date will still be in May two years later due to the prerequisite structure.