Machine Learning/Artificial Intelligence Major
The Machine Learning/Artificial Intelligence major is designed to provide an opportunity to all business majors. They will gain an understanding of the applied use of data mining, data visualization, and machine learning and artificial intelligence.
The International Data Corporation (https://www.idc.com/) predicts that data will grow from 33 zettabytes to 175 zettabytes by 2025. A zettabyte is approximately the size of a trillion gigabytes. This is a 61% compounded annual growth rate. Around half of this data will likely live in the cloud. The numbers are staggering and the implications are huge. MLBA give analysts the ability to process and find meaning in these extremely large data sets. MLBA are not only prized skills, but will likely become the most demanded skill for job applicants in the coming years.
Further, the SAS Institute asserts that “…it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale.” This helps organizations to be increasingly capable in a highly competitive world, while minimizing unknown risks (https://www.sas.com/en_us/insights/analytics/machine-learning.html).
Objective 1: Students will be able to demonstrate a conceptual and intuitive understanding of the common machine learning algorithms (inc. Supervised and Unsupervised Learning) and when each kind of technique may be appropriate.
Objective 2: Students will be able to define the structure and components of a Python program (using loops, decision statements, functions, and libraries). Additionally, they will be able to work with Python libraries for data processing, and data visualization.
Objective 3: Students will be able to design and implement various machine learning algorithms in a range of business applications.
Objective 4: Students will demonstrate the use of data mining models that can identify hidden patterns and rules.
Objective 5: Students will be able to communicate clearly and effectively in composing and delivering oral presentations to the target audience.
The traditional undergraduate programs includes a minimum of 120 credits distributed across three components: A General Education component divided into Signature Courses, Variable Courses, and an Integrative Learning requirement; a Major and Divisional component; and Free Electives. In addition to course requirements as specified in each area, students must complete one certified course in each of the following overlay areas1:
- Diversity, Globalization or Non-western Area Studies,
- Ethics Intensive
- Writing Intensive, and
- Diversity
- 1
Overlay requirements are part of the 120 credit requirements
General Education Signature Courses
See this page about Signature courses.
General Education Variable Courses
See this page about Variable courses. Six to Nine courses
General Education Overlays
General Education Integrative Learning Component
See this page about Integrative Learning Component. Three courses:
| Code | Title | Hours |
|---|---|---|
| ILC 1: Economics | 3 | |
| Introductory Economics Macro | ||
| ILC 2: Mathematics | 3 | |
| Differential Calculus | ||
| Fundamentals of Calculus | ||
| Calculus I | ||
| ILC 3: Non Department ILC: Select one from the following courses: | 3 | |
| Accounting | ||
| Fin Acc Info Sys I | ||
| Management Acc Info Systems | ||
| Business | ||
| Business Strategy | ||
| Computer Science | ||
| Computer Science I | ||
| CSC I:Programming Fundamentals | ||
| English | ||
| Writing for Organizations | ||
| Scientific Writing | ||
| Finance | ||
| Markets and Institutions | ||
| Fund of Quantitative Finance | ||
| Mathematics | ||
| Math of Games & Politics | ||
| Psychology | ||
| Intro Research Method Soc Sci | ||
| Research Methods | ||
| Theology | ||
| Technology Ethics | ||
| Total Hours | 9 | |
Business Foundation
Ten courses, including:
| Code | Title | Hours |
|---|---|---|
| ACC 101 | Concepts of Financial Acct | 3 |
| ACC 102 | Managerial Accounting | 3 |
| DSS 100 | Excel Competency | 1 |
| DSS 200 | Intro to Information Systems | 3 |
| DSS 210 | Business Statistics | 3 |
| DSS 220 | Business Analytics | 3 |
| FIN 200 | Intro to Finance | 3 |
| or FIN 225 | Fund of Quantitative Finance | |
| MGT 110 | Essent'ls of Organzational Beh | 3 |
| or MGT 120 | Essentials of Management | |
| MGT 360 | Legal Environment of Business | 3 |
| MKT 201 | Principles of Marketing | 3 |
| BUS 495 | Business Strategy 1 | 3 |
| or ACC 423 | Accounting Control Systems | |
| Total Hours | 31 | |
Major Requirements
| Code | Title | Hours |
|---|---|---|
| DSS 325 | Open Source Program Lang | 3 |
| DSS 415 | Data Wrangling & Visualization | 3 |
| or DSS 416 | Data Wrangling: Ethics Int. | |
| DSS 420 | Introduction to Data Mining | 3 |
| DSS 451 | Machine Learning for Bus I | 3 |
| DSS 455 | Machine Learning for Bus II | 3 |
| DSS Elective (select one of the following): | 3 | |
| Database Management | ||
| Analytics Cup | ||
| Advanced Business Analytics | ||
| Statistical Programming Lang | ||
| Supply Chain Analytics | ||
| DSS Special Topics I | ||
| DSS Special Topics II | ||
| Independent Study I | ||
| Independent Study II | ||
| Total Hours | 18 | |