Data Analytics Major, Bachelor of Science (BS)
The development of large-scale data collection in recent years has resulted in a growing gap in the work force as employers struggle to find those with the broad skillset needed to navigate in this environment while developing the narrative of meaning that underlies the data. The Bachelor of Science in Data Analytics concentrates at the undergraduate level on equipping graduates with the hybridization of programming, information systems, applied statistics, management science, data analysis and decision support skills needed by employers.
Majoring or minoring in an additional discipline is suggested as data science and analytics is used in many fields, such as science, education, medicine, government and business.
| Prerequisite Courses | ||
| ENGL 201 | COLLEGE COMPOSITION: ANALYSIS, RESEARCH AND DOCUMENTATION | 5 |
| MATH/HONS 161 | CALCULUS I (recommended) | 5 |
| or MATH 142 | PRECALCULUS MATH II | |
| MISC 311 | INFORMATION TECHNOLOGY IN BUSINESS | 4-5 |
| or CSCD 210 | PROGRAMMING PRINCIPLES I | |
| or CSCD 211 | PROGRAMMING PRINCIPLES II | |
| Required Courses | ||
| DSCI 245 | BUSINESS STATISTICS 1 | 4 |
| DSCI 346 | BUSINESS STATISTICS 2 | 4 |
| DSCI 352 | MIXED RESEARCH METHODS, SECURITY AND ETHICS FOR ANALYTICS | 4 |
| DSCI 353 | DATA MANAGEMENT, CLEANING AND IMPUTATION | 4 |
| DSCI 445 | OPTIMIZATION VIA MANAGEMENT SCIENCE | 4 |
| DSCI 446 | BUSINESS FORECASTING | 4 |
| DSCI 449 | MULTIVARIATE DATA ANALYSIS | 4 |
| DSCI 450 | DATA VISUALIZATION | 4 |
| MISC 371 | BUSINESS APPLICATIONS PROGRAM DESIGN | 4 |
| MISC 373 | BUSINESS DATABASE APPLICATIONS | 4 |
| MISC 374 | SPREADSHEET MODELING FOR BUSINESS APPLICATIONS | 4 |
| MISC 485 | ADVANCED DATABASE APPLICATIONS DEVELOPMENT | 4 |
| Electives–choose one from the following or see the department advisor for a list of approved electives | 4 | |
| DESIGN OF EXPERIMENTS | ||
| BUSINESS SIMULATION | ||
| PROFESSIONAL INTERNSHIP | ||
or MISC 495 | INTERNSHIP | |
| DIGITAL ENTREPRENEURSHIP | ||
| SEMINAR | ||
or DSCI 498 | SEMINAR | |
| Required Senior Cohort Sequence | ||
Taken sequentially these hybrid classes are composed of online material from Microsoft Learn, Microsoft role-based certifications, community projects, supplemental material and weekly discussion sessions with the course instructor. | ||
| DSCI 481 | ML-DATA SCIENCE FUNDAMENTALS | 4 |
| DSCI 483 | ML-APPLIED DATA SCIENCE | 4 |
| Required Senior Capstone | ||
| DSCI 490 | ANALYTICS SENIOR CAPSTONE | 4 |
| Total Credits | 78-79 | |
University Competencies and Proficiencies
English
Quantitative and Symbolic Reasoning
Placement and Clearance
Prior Learning/Sources of Credit AP, CLEP, IB
General Education Requirements (GER)
- Minimum Credits—180 cumulative credit hours
- 60 upper-division credits (300 level or above)
- 45 credits in residence (attendance) at Eastern, with at least 15 upper-division credits in major in residence at Eastern
- Minimum Cumulative GPA ≥2.0
Breadth Area Core Requirements (BACR)
Humanities and Arts
Natural Sciences
Social Sciences
University Graduation Requirements (UGR)
Diversity Course List
Foreign Language (for Bachelor of Arts)
Global Studies Course List
Minor or Certificate
Senior Capstone Course List
Application for Graduation (use EagleNET) must be made at least two terms in advance of the term you expect to graduate (undergraduate and post-baccalaureate).
Use the Catalog Archives to determine two important catalog years.
Degree Works calculates based on these two catalog years.
- The catalog in effect at the student's first term of current matriculation is used to determine BACR (Breadth Area Credit Requirements) and UGR (Undergraduate Graduation Requirements).
- The catalog in effect at the time the student declares a major or minor is used to determine the program requirements.
Students who successfully earn a BS in Data Analytics from EWU should be able to do the following:
- analyze a variety of data types, including both structured data and unstructured data;
- build mathematical models to assist decision-making processes;
- discuss ethical issues related to data analytics;
- interpret analytic information visually to relevant audiences;
- make critical decisions to engineer data management.