Data Science and Analytics (BS)
Faculty
Margaret Menzin, Professor of Mathematics and Computer Science, Coordinator
Naresh Agarwal, Assistant Professor of Library and Information Science
Michael Brown, Professor of Mathematics and Statistics
Robert Goldman, Professor of Statistics
Susan D. Sampson, Associate Professor of Management
Anthony Scotina, Assistant Professor of Statistics
Amber Stubbs, Assistant Professor of Computer Science
Bruce Tis, Associate Professor of Computer Science
Nanette Veilleux, Professor of Computer Science and Associate Dean for Research of the School of Library and Information Science
Data Science and Analytics is a field which also goes by the names of Data Science, Data Analytics, and Predictive Analytics. Informally this is also referred to as “Big Data.”
By now most of us have heard of the term “big data”, which refers to data sets distinguished by “the 3 V’s”: volume, velocity, and variability. In this context ‘volume’ refers, obviously, to the size of the data set, ‘velocity’ refers to the speed at which new data arrives or the data set changes, and ‘variability’ refers to the lack of a strict organization for formatting all the data.
DS&A has applications in many areas. For example, famously, by analyzing what people were querying, Google was able to predict a flu epidemic several weeks ahead of the CDC. Another well-known example is the use of these techniques to target certain groups of people in election campaigns. Recently, breast cancer oncologists announced the formation of a database which will contain anonymized information about every woman who has had breast cancer and had her tumor sequenced. This database will contain the tumor sequence, other medical information, and details about the treatment and how successful it was. Oncologists will then be able to query it for patients with newly diagnosed tumors to select an optimal treatment approach.
So, what is DS&A? It is a combination of statistical and computing methods to analyze and interpret such large data sets, in a particular discipline. Data Science & Analytics rests on a tripod of statistics, computer science, and domain knowledge.
The DS&A major at Simmons is designed to implement this tripod approach, with required courses in statistics, computer science and management, and a required area of expertise (what we call a concentration; 5 courses in parts of a discipline where “big data” is likely to occur).
Program Requirements:
Required Courses
| CS 112 | Introduction to Computer Science | 4 |
| CS 113 | Gui and Event-Driven Programming | 4 |
| CS 333 | Database Management Systems | 4 |
| MATH 118 | Introductory Statistics | 4 |
| MATH 227 | Intermediate Statistics: Design & Analysis | 4 |
| MATH 228 | Introduction to Data Science | 4 |
| MATH 229 | Regression Models | 4 |
| CS 347 | Applied Data Science | 4 |
| MGMT 100 | Foundations of Business & Management | 4 |
| MGMT 221 | Project Management | 4 |
| LIS 473 | Information Visualization | 3 |
A typical schedule of courses is CS 112-CS 113 and MATH 118–MATH 228 in first year; MATH 227 and MATH 229 and CS 333 in second year; MATH/CS 440 and CS 347and MGMT 100 and MGMT 221 in third year; Internship and LIS 593 in fourth year. Students starting the major in their second year will combine the third and fourth year programs.
List of pre-approved concentrations; students with other interests should consult program faculty or their advisors.
Biology-Bioinformatics:
Biochemistry:
Business-Accounting:
and 3 electives chosen from
Business-Finance:
and 3 electives chosen from
Business-Health Care Management:
and 3 electives chosen from
| MGMT 137 | Entrepreneurship and Innovation | 4 |
| MGMT 222 | Human Resources Management | 4 |
| MGMT 234 | Organizational Communication and Behavior | 4 |
MGMT 290E
| | |
| MGMT 325 | Operations Management & Decision Making | 4 |
| CS 225 | Health Informatics | 4 |
| SOCI 241 | Health, Illness & Society | 4 |
Business-Marketing:
and 3 electives from
Chemistry:
Communications:
| COMM 121 | Visual Communication | 4 |
| COMM 244 | Web I: Design for the World Wide Web | 4 |
| COMM 210 | Introduction to Graphic Design: Principles and Practice | 4 |
| COMM 240 | Intermediate Graphic Design I: Typography | 4 |
| COMM 248 | Intermediate Graphic Design II | 4 |
Computer Science:
| CS 232 | Data Structures | 4 |
| CS 227 | Computer Networks | 4 |
| CS 327 | Cybersecurity | 4 |
| CS 345 | Operating Systems | 4 |
| CS 330 | Structure and Organization of Programming Language | 4 |
or other elective chosen with the advisor.
Economics:
and 2 electives chosen from
Mathematics–Statistics:
and an elective to be chosen with the advisor.
Political Science/International Relations:
| POLS 101 | Introduction to American Politics | 4 |
| POLS 102 | Introduction to International Politics | 4 |
| POLS 104 | Introduction to Comparative Politics | 4 |
and 2 electives from
Public Health:
and an elective to be chosen with the advisor.
Sociology:
and 3 electives form
Honors in Data Science and Analytics In order to receive Honors in Data Science and Analytics a student must:
- Maintain superior academic performance as indicated by a GPA of 3 .5 or higher in major and concentration courses taken at Simmons College
- Conduct independent research through the successful completion of an NSF-REU or similar research program or by completion of a thesis or project supervised within the Program which receives a grade of A- or A.
- Communication of the work by presentation to the Program or another approved forum.