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BS(Hons) Data Science

Western Washington University, United States
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Course Overview

The BS(Hons) Data Science program at Western Washington University is designed to provide students with a comprehensive understanding of data science principles and practices. The program covers a wide range of topics, including machine learning, statistical analysis, data visualization, and database management. Students will gain hands-on experience through practical projects and real-world applications, preparing them for a career in the rapidly growing field of data science. The program is taught by experienced faculty members who are experts in their fields, ensuring that students receive the highest quality education.

Additional Information

Students in the BS(Hons) Data Science program will have access to state-of-the-art facilities and resources, including cutting-edge software and hardware, as well as opportunities for internships and research collaborations with industry partners. The program also offers a range of elective courses, allowing students to specialize in areas such as data analytics, artificial intelligence, or cybersecurity.

Discover the Best Data Science Program at Western Washington University

Western Washington University is an excellent choice for students pursuing a degree in Data Science due to its reputation for academic excellence and innovative teaching methods. The university has a strong focus on experiential learning and provides students with numerous opportunities for hands-on experience, internships, and research collaborations with industry partners. Additionally, the university's location in Bellingham, Washington provides a unique blend of urban and natural environments, offering students a diverse and enriching learning experience.

Why You Should Study This Course

Data Science is the study of the mathematical and computational methods for extracting meaning from data. It involves the collection, processing, organization, quantitative analysis, visualization and modeling of data. It is interdisciplinary, drawing from the fields of computer science, mathematics, statistics, and information science, among many others. As data plays an increasingly important role in understanding and discovery across a wide range of disciplines, data science provides a set of tools that enable scholars to better analyze and answer important problems in their own field. In this way, data science often serves as a hub to foster collaboration within and across organizations.

Data science empowers you to solve important, challenging problems that are otherwise impractical or impossible to solve. These data-centric problems exist everywhere, from science, the arts, business, the humanities and social sciences, engineering and beyond. Today’s world is accumulating data at unprecedented rates, the sheer magnitude of which means that the insights, knowledge, and discoveries buried within that data cannot be found through traditional techniques alone. Algorithms to learn from this data have already revolutionized many fields, from speech recognition to computer vision, producing breakthroughs in artificial intelligence.

The skills you will obtain in the Data Science program will enable you to obtain, process, organize, analyze, visualize and model the troves of data out there. Beyond the intellectual satisfaction of data science work, these skills are in high demand. 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.

General Eligibility

The admission requirements for undergraduate courses are given as follows:

  • Completed application form

  • Academic Transcripts(certified english translations if necessary)

  • Relevant High school degree/Grade Report/Certificates

  • SAT or ACT Scores (Optional)

  • A cumulative GPA of 2.0

  • Personal Statement or essay

  • One letter of recommendation

  • List of extracurricular activities(if any)

  • Portfolio

What You'll Study

1 Electives

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 Credits: 1-15

CSCI 402 - Artificial Intelligence Credits: 4

CSCI 404 - Natural Language Processing Credits: 4

CSCI 424 - Social Network Analysis Credits: 4

CSCI 436 - Technology for Social Good Credits: 4

CSCI 471 - Advanced Machine Learning Credits: 4

CSCI 474 - Bioinformatics Credits: 4

CSCI 476 - Computer Vision Credits: 4

CSCI 477 - Data Mining Credits: 4

CSCI 479 - Spoken Language Processing Credits: 4

CSCI 481 - Deep Learning Credits: 4

CSCI 496 - Undergraduate Research Credits: 1-4

CSCI 497 temporary courses under advisement

MATH 443 - Linear Statistical Models Credits: 4

MATH 444 - Categorical Data Analysis Credits: 4

MATH 445 - Computational Statistics Credits: 4

MATH 447 - Multivariate Statistics Credits: 4

MATH 456 - Applied Time Series Analysis Credits: 4

MATH 457 - Bayesian Statistics Credits: 4

MATH 458 - Stochastic Processes Credits: 4

MATH 473 - Numerical Linear Algebra Credits: 4

M/CS 335 - Linear Optimization Credits: 4

M/CS 375 - Numerical Computation Credits: 4

M/CS 435 - Nonlinear Optimization Credits: 4

M/CS 475 - Numerical Analysis Credits: 4

2 Major Core

Computer Science (33 credits)

❑  CSCI 141 - Computer Programming I Credits: 4

❑  CSCI 145 - Computer Programming and Linear Data Structures Credits: 4

❑  CSCI 241 - Data Structures Credits: 4

❑  CSCI 301 - Formal Languages and Functional Programming Credits: 5

❑  CSCI 305 - Analysis of Algorithms and Data Structures I Credits: 4

❑  CSCI 330 - Database Systems Credits: 4

❑  CSCI 345 - Object Oriented Design Credits: 4

❑  CSCI 405 - Analysis of Algorithms and Data Structures II Credits: 4

Data Science (14 credits)

❑  DATA 311 - Fundamentals of Data Science Credits: 4

❑  DATA 371 - Machine Learning Credits: 4

❑  DATA 491 - Senior Project 1 Credits: 2

❑  DATA 492 - Senior Project 2 Credits: 2

❑  DATA 493 - Senior Project 3 Credits: 2

Math (31 credits)

❑ Choose either:

MATH 124 - Calculus and Analytic Geometry I Credits: 5

MATH 125 - Calculus and Analytic Geometry II Credits: 5

or

MATH 134 - Calculus I Honors Credits: 5

MATH 135 - Calculus II Honors Credits: 5

or

MATH 138 - Accelerated Calculus Credits: 5 (Students who complete this course receive five credits of advanced placement (unless previously received)). 

❑  MATH 204 - Elementary Linear Algebra Credits: 4

❑  MATH 224 - Multivariable Calculus and Geometry I Credits: 5

❑  MATH 304 - Linear Algebra Credits: 4

❑  MATH 341 - Probability and Statistical Inference Credits: 4

❑  MATH 342 - Statistical Methods I Credits: 4


3 Science Courses

❑ Students must complete one of the following science sequences:

Biology

BIOL 204 - Introduction to Evolution, Ecology and Biodiversity w/lab Credits: 5

BIOL 205 - Introduction to Cellular and Molecular Biology w/lab Credits: 5

BIOL 206 - Introduction to Organismal Biology w/lab Credits: 5

Chemistry

CHEM 161 - General Chemistry I Credits: 5

CHEM 162 - General Chemistry II Credits: 5

CHEM 163 - General Chemistry III Credits: 5

Geology

GEOL 211 - Physical Geology Credits: 5

GEOL 212 - Historical Geology Credits: 4

Must complete one of the following courses:

GEOL 303 - Dinosaurs and Their Environment Credits: 3

GEOL 308 - Earthquakes Credits: 3

GEOL 309 - Volcanology Credits: 3

GEOL 314 - Engineering Geology Credits: 4

GEOL 315 - Minerals, Energy and Society Credits: 4

GEOL 340 - Geological Oceanography Credits: 3

Physics

PHYS 161 - Physics with Calculus I Credits: 5

PHYS 162 - Physics with Calculus II Credits: 5

PHYS 163 - Physics with Calculus III Credits: 5

Language Requirement

  • IELTS6.5
  • TOEFL93
  • PTE65
  • Duolingo89

Minimun IELTS Score 6.5 overall.

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