Program Overview

Introduction to the Data Science Program

The Data Science program at Western Washington University is an interdisciplinary field of study that focuses on the mathematical and computational methods for extracting meaning from data. It involves the collection, processing, organization, quantitative analysis, visualization, and modeling of data. Data science draws from various fields, including computer science, mathematics, statistics, and information science, among others.

Why Consider a Data Science Major?

Data science empowers students to solve important and challenging problems that are otherwise impractical or impossible to solve. These data-centric problems exist in various disciplines, from science and the arts to business, humanities, social sciences, engineering, and beyond. The skills obtained in the Data Science program enable students to obtain, process, organize, analyze, visualize, and model vast amounts of data. Data science jobs are among the fastest-growing in the country, and data scientists often land interesting, challenging, and lucrative jobs directly out of college.

Student Resources

• Department Website: Computer Science
• Department Advising:
  • Data Science Pre-Major Advisor: Kjatosia Ruvalcaba, Kaiser Borsari Hall 421D
  • Data Science Program Director: Brian Hutchinson, PhD, Communications Facility 475
• Degree Works: Current students should log on to Degree Works to check student-specific program progress.
• Career Services Center: Connect major to a career
• Sample Careers: Machine Learning Scientist, Data Scientist, Data Engineer, Data Analyst, Data Consultant, Data Architect

Admission and Declaration Process

Admission to the Data Science major is a two-phase process. Students are advised to declare the pre-major as soon as they are enrolled in CSCI 141 by contacting the Data Science Undergraduate Advisor and completing the pre-major application. Transfer students should seek advising immediately upon transfer to Western. Students cannot apply to the Data Science major until they have completed the pre-major courses, namely, CSCI 141, CSCI 145, CSCI 241, CSCI 301, and DATA 311.

Grade Requirements

A grade of C- or better is required for a student’s major or minor courses and supporting courses for majors and minors.

Program Requirements

The Data Science program requires 94 credits of data science, computer science, and mathematics, plus 12-15 credits in supporting science.

Major Core (78 credits)

• Computer Science (33 credits):
  • CSCI 141 - Computer Programming I (4 credits)
  • CSCI 145 - Computer Programming and Linear Data Structures (4 credits)
  • CSCI 241 - Data Structures (4 credits)
  • CSCI 301 - Formal Languages and Functional Programming (5 credits)
  • CSCI 305 - Analysis of Algorithms and Data Structures I (4 credits)
  • CSCI 330 - Database Systems (4 credits)
  • CSCI 345 - Object Oriented Design (4 credits)
  • CSCI 405 - Analysis of Algorithms and Data Structures II (4 credits)
• Data Science (14 credits):
  • DATA 311 - Fundamentals of Data Science (4 credits)
  • DATA 471 - Machine Learning (4 credits)
  • Choose one of the following 6-credit options:
    • DATA 491 - Senior Project 1 (2 credits)
    • DATA 492 - Senior Project 2 (2 credits)
    • DATA 493 - Senior Project 3 (2 credits)
    • or
    • DATA 490 - Senior Research (2 credits) (must complete a total of 6 credits)
• Math (31 credits):
  • Calculus sequence (complete one of the following):
    • MATH 124 - Calculus and Analytic Geometry I (5 credits)
    • MATH 125 - Calculus and Analytic Geometry II (5 credits)
    • MATH 224 - Multivariable Calculus and Geometry I (5 credits)
    • or
    • MATH 134 - Calculus I Honors (5 credits)
    • MATH 135 - Calculus II Honors (5 credits)
    • MATH 224 - Multivariable Calculus and Geometry I (5 credits)
    • or
    • MATH 138 - Accelerated Calculus (5 credits)
    • MATH 224 - Multivariable Calculus and Geometry I (5 credits)
  • Linear Algebra sequence:
    • MATH 204 - Elementary Linear Algebra (4 credits)
    • MATH 304 - Linear Algebra (4 credits)
  • Probability and Statistics sequence:
    • MATH 341 - Probability and Statistical Inference (4 credits)
    • MATH 342 - Statistical Methods I (4 credits)

Science (12-15 credits)

Students must complete one of the following science sequences:

• Biology:
  • BIOL 204 - Introduction to Evolution, Ecology and Biodiversity w/lab (5 credits)
  • BIOL 205 - Introduction to Cellular and Molecular Biology w/lab (5 credits)
  • BIOL 206 - Introduction to Organismal Biology w/lab (5 credits)
• Chemistry:
  • CHEM 161 - General Chemistry I (5 credits)
  • CHEM 162 - General Chemistry II (5 credits)
  • CHEM 163 - General Chemistry III (5 credits)
• Geology:
  • GEOL 211 - Physical Geology (5 credits)
  • GEOL 212 - Historical Geology (4 credits)
  • Must complete one of the following courses:
    • GEOL 308 - Earthquakes (4 credits)
    • GEOL 309 - Volcanology (3 credits)
    • GEOL 314 - Engineering Geology (4 credits)
    • GEOL 315 - Minerals, Energy and Society (4 credits)
    • GEOL 340 - Geological Oceanography (3 credits)
• Physics:
  • PHYS 161 - Physics with Calculus I (5 credits)
  • PHYS 162 - Physics with Calculus II (5 credits)
  • PHYS 163 - Physics with Calculus III (5 credits)

Electives (16 credits)

16 credits chosen from the following, of which a maximum of 4 total credits can be taken from CSCI 400 or CSCI 496 projects:

• CSCI 400 - Directed Independent Study (1-15 credits)
• CSCI 402 - Artificial Intelligence (4 credits)
• CSCI 404 - Natural Language Processing (4 credits)
• CSCI 424 - Social Network Analysis (4 credits)
• CSCI 436 - Technology for Social Good (4 credits)
• CSCI 471 - Advanced Machine Learning (4 credits)
• CSCI 474 - Bioinformatics (4 credits)
• CSCI 476 - Computer Vision (4 credits)
• CSCI 477 - Data Mining (4 credits)
• CSCI 479 - Spoken Language Processing (4 credits)
• CSCI 481 - Deep Learning (4 credits)
• CSCI 482 - Computational Neuroscience (4 credits)
• CSCI 496 - Undergraduate Research (1-4 credits)
• CSCI 497 temporary courses under advisement
• MATH 443 - Linear Statistical Models (4 credits)
• MATH 444 - Categorical Data Analysis (4 credits)
• MATH 445 - Computational Statistics (4 credits)
• MATH 447 - Multivariate Statistics (4 credits)
• MATH 456 - Applied Time Series Analysis (4 credits)
• MATH 457 - Bayesian Statistics (4 credits)
• MATH 458 - Stochastic Processes (4 credits)
• MATH 473 - Numerical Linear Algebra (4 credits)
• M/CS 335 - Linear Optimization (4 credits)
• M/CS 375 - Numerical Computation (4 credits)
• M/CS 435 - Nonlinear Optimization (4 credits)
• M/CS 475 - Numerical Analysis (4 credits)

University Graduation Requirements

• General University Requirements
• Writing Proficiency Requirement (WP)
• 180 Minimum Total Credits
• 60 Minimum Upper Division Credits
• Residency Requirement
• Minimum Grade Requirements
• Final Quarter Requirement

Conclusion

The Data Science program at Western Washington University offers a comprehensive and interdisciplinary approach to data science, equipping students with the skills and knowledge necessary to succeed in this rapidly growing field. With its strong foundation in computer science, mathematics, and statistics, as well as its emphasis on practical application and real-world problem-solving, this program provides students with a unique and valuable educational experience.