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)

CS 102/IT 102	Introduction to Programming	4 cr.
CS 200	Data Structures	4 cr.
CS 364	Design of Database Management Systems	3 cr.
MATH 121	Introductory Probability and Statistics I	3 cr.
MATH 127 - MATH 133	Calculus I with Pre-Calculus Review or Calculus I	5 cr. - 4 cr.
MATH 134	Calculus II	4 cr.
MATH 221	Introductory Probability and Statistics II	3 cr.
MATH 331	Computation in Statistics	3 cr.
MATH 306	Linear Algebra	3 cr.
MATH 384	Applied Regression & Time Series	3 cr.
MATH 441	Data Visualization & Data Techniques	3 cr.
BAIM 330 - CS 370	Applied Data Mining or Artificial Intelligence	3 cr. - 3 cr.
DATA 410	Statistical Learning	3 cr.
DATA 470	Data Science and Statistics Capstone	3 cr.

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:

BAIM 450	Multivariate and Big Data Analysis	3 cr.
EC 386	Econometrics	3 cr.
IE 429	Design and Analysis of Experiments	3 cr.
MATH 235	Calculus III	3 cr.
MATH 372	Probability	3 cr.
MATH 383	Mathematical Statistics	3 cr.
MATH 401	Long-Term Actuarial Models	3 cr.
MATH 402	Short-Term Actuarial Models	3 cr.
MATH 405	Applied Stochastic Processes	3 cr.

Data Science Suggested Sequence of Courses:

First Year- Fall Semester

ENGL 132	English Composition I	3 cr.
MATH 127 - MATH 133	Calculus I with Pre-Calculus Review or Calculus I	5cr. - 4 cr.
BLUE 101	BLUE Course	1 cr.
GBD XXX	Golden Bear Discovery / GOLD	3 cr.
GBD XXX	Golden Bear Discovery	3 cr.

Subtotal: 14-15 cr.

First Year - Spring Semester

CS 102/IT 102	Introduction to Programming	4 cr.
ENGL 133	English Composition II	3 cr.
MATH 134	Calculus II	4 cr.
MATH 121	Introductory Probability and Statistics I	3 cr.
GBD XXX	Golden Bear Discovery	3 cr.

Subtotal: 17 cr.

Sophomore Year - Fall Semester

CS 200	Data Structures	4 cr.
MATH 221	Introductory Probability and Statistics II	3 cr.
GBD XXX	Golden Bear Discovery	3 cr.
GBD XXX	Golden Bear Discovery	3 cr.
GEN XXX	General Elective	3 cr.

Subtotal: 16 cr.

Sophomore Year - Spring Semester

MATH 331	Computation in Statistics	3 cr.
MATH 306	Linear Algebra	3 cr.
GBD XXX	Golden Bear Discovery	3 cr.
GEN XXX	General Elective	3 cr.
WIC 2XX	Writing Intensive Course	3 cr.

Subtotal: 15 cr.

Junior Year - Fall Semester

GEN XXX	General Elective	3 cr.
DATA 410	Introduction to Statistical Learning	3 cr.
GEN XXX	General Elective	3 cr.
GEN XXX	General Elective	3 cr.
GEN XXX	General Elective	3 cr.

Subtotal: 15 cr.

Junior Year - Spring Semester

GEN XXX	General Elective	3 cr.
GEN 3XX	Upper Level General Elective	3 cr.
CS 364	Design of Database Management Systems	3 cr.
WIC 3XX	Writing Intensive Course	3 cr.
BAIM 330	Applied Data Mining	3 cr.

Subtotal: 15 cr.

Senior Year - Fall Semester

MATH 384	Applied Regression & Time Series	3 cr.
GEN XXX	General Elective	3 cr.
GEN XXX	General Elective	3 cr.
GEN XXX	General Elective	3 cr.
GEN XXX	General Elective	3 cr.

Subtotal: 15 cr.

Senior Year - Spring Semester

DATA 470	Data Science and Statistics Capstone	3 cr.
MATH 441	Data Visualization & Data Techniques	3 cr.
GEN 3XX	Upper Level General Elective	3 cr.
GEN XXX	General Elective	3 cr.
GEN XXX	General Elective	1 cr.

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.

DATA.BS - BS in Data Science

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