Data Science and Analytics (BS)
By now, nearly everyone has heard of the term “big data,” the large, constantly changing, frequently unstructured data sets that modern technology has led to in every organization. Big data can be leveraged to gain insight into complex phenomena, make better predictions, and help organizations to run more effectively—but big data can also come with many challenges, including privacy, security, and the inherent complexity of processing and analyzing large quantities of data. Big data has spawned the emerging and growing field of Data Science and Analytics (DS&A), which combines statistical and computing methods to extract meaningful information from datasets which have a variety of sizes and formats.
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 that will contain anonymized information about every breast cancer tumor that has been genetically sequenced, including the treatment and outcomes. Oncologists will then be able to query it for patients with newly diagnosed tumors to select an optimal treatment approach.
The DS&A major at Simmons integrates learning from multiple foundational disciplines, including required courses in statistics, computer science, and mathematics; an application domain course; and a capstone experience.
Learning Outcomes
- Select from, use, and interpret results of descriptive statistical methods effectively.
- Select from, use, and interpret results of the principal methods of data science and analytics.
- Communicate the results of analyses accurately and effectively, in writing, orally and visually.
- Make appropriate use of relevant software, using and modifying standard techniques.
- Apply principles of leadership and reproducible research to make responsible decisions involving privacy, data management, and scientific rigor.
- Demonstrate the ability to plan, manage, and document moderately sized projects.
Program Requirements
Core Courses
Programming Depth (2 courses)
Statistical Depth (2 courses)
Application Domain 1 course
Choose from a list of approved upper-level courses in another department. Examples:
| PSYC 203 | Research Methods in Psychology | 4 |
| SOCI 239 | Introduction to Social Research I | 4 |
| PH 201 | Introduction to Epidemiology | 4 |
| BUS 221 | Project Management | 4 |
Or other application domain course identified by the student with their advisor.
Electives (2 courses)
Any course in the Programming/Stats depth areas not taken for another requirement area.
Capstone (1 course)
A typical schedule of courses is CS 112, , STAT 118 and STAT 228 in first year; STAT 227, STAT 229, CS 214 and CS 221 in second year; CS 232 MATH 211, STAT 391 and CS/STAT 347 in third year; Internship and CS/STAT 346 in fourth year. Students starting the major in their second year will combine the third and fourth year programs.
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 University.
- 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.