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What is Data Science?
Humanity’s inexorable digitization of existing data, creation of “born-digital” data, and the rapidly evolving Internet are driving the need for global transformation of higher education and its research, education, and service missions. Indeed, “Big Data” now impacts nearly every aspect of our modern society, including retail, manufacturing, financial services, communications and mobile services, and education. It has also transformed scholarship in numerous fields including the life sciences, engineering, natural sciences, social sciences, as well as in the arts and humanities. A recent information industry report estimates that by 2020 there will be more than 5 petabytes stored worldwide for every man, woman and child on the planet. 
The data available to us today are not just voluminous, they also are incredibly heterogeneous, including social media posts, videos, and readings from sensor arrays, in addition to databases in multiple formats and structures. Furthermore, some data arrives in a “firehose”, at very high rates, and swiftly mutating. New capabilities to harness these data for deeper understanding, knowledge creation, dissemination, and translation are urgently required by all sectors of the economy to rapidly adapt to the world around us.
The emergence of Data Science responds directly to the needs of processing and extracting knowledge from very large, dynamic, heterogeneous, and incongruent data sets – "Big Data" consisting of structured, unstructured or semi-structured data that disparate (digital) sensors and instruments produce at ever increasing frequency and sensitivity. The Data Science explosion is fueled organically by new data generated from diverse sources, devices, web-services, mobile communication, scientific studies, and social media. Data scientists require a versatile and unique set of skills to manage, process, and extract data from these complex information streams, and then interrogate, analyze, visualize and interpret the information.
Nationally, there is a pressing need for Data Scientists, and in fact for people with every level of Data Science training. Harvard Business Review called it “the sexiest job of the 21st century.” The employment tracking service, Glassdoor, named it the “best job of the year” for 2016. Forbes magazine, in its issue dated Sep 24, 2015, explained:
“With the explosion of big data and the need to track it, employers keep on hiring data scientists. But qualified candidates are in short supply. The field is so new, the Bureau of Labor Statistics doesn’t even track it as a profession. Yet thousands of companies, from startups that analyze credit card data in order to target marketing and advertising campaigns, to giant corporations like Ford Motor and Price WaterhouseCoopers, are bringing on scores of people who can take gigantic data sets and wrestle them into usable information. As an April report from technology market research firm Forrester put it, “Businesses are drowning in data but starving for insights.”
The field of Data Science offers excellent career paths for people with the right skills, ranging from business analysts across a variety of industries, to novel IT careers, to working on scientific research teams. For such careers, students require solid training in an array of computational and statistical analysis techniques shared across industries and disciplines.
Data Science is often viewed as the confluence of (1) Computer and Information Sciences (2) Statistical Sciences, and (3) Domain Expertise. These three pillars are not symmetric: the first two together represent the core methodologies and the techniques used in Data Science, while the third pillar is the application domain to which this methodology is applied. In this program, core data science training is focused on the first two pillars, along with practice in applying their skills to address problems in application domains.
It is also often valuable for experts in an application domain to acquire skills in the first two pillars to be able to apply Data Science methods to important problems in their domain. However, this class of training is not the focus of this program.
We characterize the required Data Science skills in two categories: statistical skills, such as those taught by the Statistics and Biostatistics departments, and computational skills, such as those taught by the Computer Science and Engineering Division and the School of Information. The design of the program is to require every student to receive balanced training in both “buckets.” To create an academic plan that achieves this balance, and to foster a greater sense of shared community, we do not intend to offer any sub-plans or tracks within the proposed degree program. Rather, we will expect graduates of this program to understand data representation and analysis at an advanced level. With the MS in Data Science all students will be able to: identify relevant datasets, apply the appropriate statistical and computational tools to the dataset to answer questions posed by individuals, organizations or governmental agencies, design and evaluate analytical procedures appropriate to the data, and implement these efficiently over large heterogeneous data sets in a multi-computer environment.
The program is taught by eminent faculty members working on the frontiers of modern data science, with a broad range of research interests from core theory to applications. Many of the faculty have received prestigious honors from national and international statistical organizations and serve on editorial boards of top journals in the field. Most faculty have led multiple federally funded research grants. Most faculty have interdisciplinary collaborations with researchers in other fields.
The program is run collaboratively by faculty from four units on campus: Statistics department, Biostatistics department, School of Information, and division of Computer Science and Engineering. Faculty from all four units advise students in the program, and participate in program management and admissions. Courses in the program are taught by faculty from all four units and also by faculty from other units.
The Michigan Institute for Data Science (MIDAS), is a valuable resource on campus, serving as a nexus for interdisciplinary collaborations in Data Science, and providing a range of opportunities for industrial engagement. MIDAS also runs a seminar series that brings a talk every week by a different person pushing the boundaries of Data Science.
Our diverse community of graduate students comes from many different countries and many undergraduate majors, including statistics, mathematics, computer science, physics, engineering, information, and data science. While a Data Science undergraduate major is not required, it is expected that applicants will have at least the following background before they join: three semesters of college calculus, one semester of linear algebra, and at least one introductory computing course. A greater level of proficiency in Computer Science and some exposure to probability are desirable.
The degree program requires at least 25 units of coursework. A student entering with a bachelors in Data Science can expect to complete this coursework, and meet all requirements, in two semesters. Most students may need more background in Statistics or Computing, and so will take 3 or 4 semesters to finish.
A limited number of graduate credits earned elsewhere can be transferred to meet the coursework requirements for the master's degree. In addition, a course taken elsewhere that is equivalent to a course satisfying a degree requirement here can be used to satisfy the requirement, even if course credits are not transferred. However, such equivalancy will not reduce the total number of credit hours required.
Financial Aid Information
The program has limited financial aid to award to master's students at the time of application. On the other hand, master's students occasionally receive a research or teaching assistantship after they begin their studies. Master's students are encouraged to apply for fellowships from sources outside the University.
Current students desiring a GSI position must take part in the departmental application process, at one of the sponsoring departments, which takes place twice during the academic year. GSIs are usually expected to work 16 to 20 hours a week, receive full tuition and fees, a monthly stipend, and health care coverage.
Information regarding loans and FAFSA is available from the University of Michigan Office of Financial Aid (734) 763-6600.
Office of Financial Aid - Graduate Programs
The University of Michigan is a world-class university, ranked consistently in the top 20 in the world. Many of our faculty members have interdisciplinary collaborations, and our graduate students have the opportunity to work with leading scientists in a range of fields. Michigan also has an excellent medical school and a large and a highly respected university hospital, providing many additional opportunities for collaborations in the life sciences.
Ann Arbor is a very pleasant place to live. The cultural and recreational opportunities are comparable to those found in much larger cities but without many of the disadvantages of big city life. The climate is milder than in many other areas of the Midwest due to a relatively greater distance from the Great Lakes, and a major international airport (DTW) is only 30 minutes away. Ann Arbor also has many good restaurants, beautiful parks, and excellent public schools. For more information about all that the University of Michigan and Ann Arbor communities have to offer, visit Uniquely Michigan.
 IDC Report 2014, available at http://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm