DATA.BS - BS in Data Science

Data Science and Statistics

General Information

Data science is an interdisciplinary field that uses methods, processes, algorithms and systems to extract or extrapolate knowledge from data and apply knowledge from data across a broad range of applications.

Data science is a field that sits at the intersection between statistics, data analysis, and informatics in order to derive knowledge from data.  It uses techniques and theories from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science.

Data science deals with quantitative and qualitative data (from images, text, sensors, transactions or customer information) and emphasizes prediction and action.

Data science pairs very well with any other disciplines in pursuit of creating and disseminating knowledge, and hence involves very sought-after skills.

Career Opportunities

Data science-related occupations are likely to enjoy excellent job prospects.  Many companies report difficulties finding highly skilled workers.  For several years in a row, statistician and data scientist have been named the top jobs in the U.S. by Glassdoor.  The U.S. Bureau of Labor Statistics reports that the demand for data science skills will drive a large percent rise in employment in the field for the next few years.  Not only is there a huge demand, but there is also a noticeable shortage of qualified data scientists.

Data science benefits both companies and consumers.  Data science enables retailers to influence our purchasing habits.  Data science can improve public health through wearable trackers that motivate individuals to adopt healthier habits and can alert people to potentially critical health issues. Data science is used by farmers for efficient food growth and delivery, by food suppliers to cut down on food waste, and by nonprofit organizations to boost fundraising efforts and predict funding needs.

To break into these high-paying, in-demand roles, an advanced education is generally required.  Data scientists are highly educated:  about 88 percent have at least a master’s degree and about 46 percent have PhDs.  In general, a very strong educational background is usually required to develop the depth of knowledge necessary to be a data scientist.  Our program is well-positioned to guide students to successfully transition into either a master’s/Ph.D. program to seamlessly finding a job.

Pursuing a career in data science is a smart move, not just because it is trendy and pays well, but because data may be the pivot point on which the entire economy turns.

Program Objectives

The Data Science program follows the recommendations from the ASA Report GAISE (Guidelines for Assessment and Instruction in Statistical Education). Students who earn their B.S. degree in Data Science should be able to do the following:

  1.  Demonstrate knowledge of concepts in statistics and probability.
  2.  Demonstrate relevant computer programming ability.
  3.  Demonstrate proficiency with relevant statistical software.
  4.  Apply knowledge and concepts to statistical and data science modeling.
  5.  Effectively communicate data analysis results in written form.
  6.  Effectively communicate data analysis results orally.

Degree Requirements

Required Data Science courses (46-47 credit hours)

Other Recommended Courses

A student who wishes to use their general electives to obtain additional coursework that supports a career in Data Science could take any of the following:

Data Science Suggested Sequence of Courses:

First Year- Fall Semester

Subtotal: 14-15 cr.

First Year - Spring Semester

Subtotal: 17 cr.

Sophomore Year - Fall Semester

Subtotal: 16 cr.

Sophomore Year - Spring Semester

Subtotal: 15 cr.

Junior Year - Fall Semester

Subtotal: 15 cr.

Junior Year - Spring Semester

Subtotal: 15 cr.

Senior Year - Fall Semester

Subtotal: 15 cr.

Senior Year - Spring Semester

Subtotal: 13 cr.

Total Credit Hours: 120

Note 1: PH 225 Ethics in Digital Technologies is encouraged to satisfy the Social Systems Trail.

Note 2: BAIM 330 Applied Data Science may be replaced with CS 370 Artificial Intelligence, which may be taken in the Fall Semester of Senior Year.

Writing Intensive Requirement

You must take 6 credits of courses designated as Writing Intensive, one at the 200- or 300-level, and one at the 300- or 400-level. These courses can also fulfill other requirements, including Golden Bear Discovery or major requirements.