Data Science

Data science is a relatively new interdisciplinary area of study that combines knowledge and skills from statistics, mathematics and computer science in novel ways to address a broad range of real-world applications.

A data scientist finds solutions to problems using data from a multitude of different sources. These sources include not only different disciplinary domains and channels, but also a variety of platforms such as cell phones, social media, e-commerce outlets, medical datasets, internet searches, and more. Thus, a data scientist must cultivate skills in all the areas related to working with large, complex datasets, and produce the information necessary for planning, forecasting, and decision-making.

Using data to find solutions

Glassdoor’s annual report on the “50 Best Jobs in America” has ranked data scientist among the top three jobs every year for the past five years (2016-2020).

Dynamic preparation for success

As an Earlham data science major, you will receive great exposure to a broad range of possibilities due to interdepartmental collaboration with Earlham’s psychology, biology and economics departments. As a result, our first cohort of sophomores and juniors have reported an increasing number of internship interviews and offers as a result of the introductory data science courses they have taken and the research projects they have worked on.

In-demand skills

The U.S. Bureau of Labor Statistics reports that by 2026, the demand for data science skills will drive a 27.9 percent rise in employment, resulting in about 11.5 million new jobs in the field. Industries seeking data science skills include technology, banking and finance, entertainment and video gaming, and hospitals and pharmaceuticals.

Student using laptop in CST

Our faculty

Because data science is an interdisciplinary major that examines issues through multiple lenses, our faculty has expertise in a variety of departments, including mathematics, computer science, physics and engineering.

Program details

You’ll begin the data science program by taking one of our introductory statistics, calculus or computer programming courses. In the first two years of the program, you’ll take courses that build a rigorous foundation in the analytical and computational skills needed. Following that, you’ll take upper-level courses that emphasize hands on, project-oriented learning in different application contexts, culminating with the capstone project in your final year.

As a liberal arts college, Earlham offers multiple disciplinary and interdisciplinary majors and minors in which students cultivate deep and specific knowledge and experience. Equally important, the College expects every student to develop broad, general skills and proficiencies across the curriculum.

As part of their general education, students complete six credits in each academic division of the College: humanities, natural sciences, social sciences, and visual and performing arts. In addition, students meet requirements for first-year courses, analytical reasoning, perspectives on diversity and wellness.

Learn more about general education at Earlham.

To earn a Bachelor of Arts in data science, you must complete the following courses, in addition to general education requirements.

The Data Science Major will consist of 13 courses (42 credits) with 12 core courses (39 credits):

  • MATH 120 Elementary Statistics (3 credits)
  • MATH 180 Calculus A (4 credits)
  • MATH 280 Calculus B (optional but strongly recommended) (4 credits)
  • MATH 310 Linear Algebra (3 credits)
  • CS 128 Programming and Problem Solving (4 credits)
  • CS/MATH 190 Math Discovery (2 credits)
  • CS 256 Data Structures (4 credits)
  • CS 310 Algorithms (3 credits)
  • CS 430 Database Systems (3 credits)
  • MATH 300 Mathematical Statistics (3 credits)
  • DS 401 Data Science (3 credits)
  • DS 488 Senior Capstone (3 credits)

and one of the following courses (3 credits):

  • CS 365 Artificial Intelligence and Machine Learning (3 credits) strongly recommended
  • CS 290 Computational Modeling/CS340 Scientific Computing (3 credits)
  • CS 345 Software Engineering (3 credits)
  • CS 360 Parallel and Distributed Computing (3 credits)
  • CS 383 Bioinformatics (3 credits)
  • DS 481 Internship (3 credits)
  • MATH 330 Mathematical Modeling (3 credits)
  • BIOL 241 Care and Use of Collections (3 credits)
  • BIOL 410 or ENSU 310 Applications of GIS (3 credits)
  • PSYC 245 Research Design and Statistics (3 credits)
  • ECON 305 Econometrics (3 credits)

View a full list of courses and their descriptions.

Yes! To earn a minor in data science, you must complete 24 credits (28 with credit inflation).

Course range: 7 courses

  1. MATH 120 Elementary Statistics (3 credits)
  2. MATH 180 Calculus A (4 credits)
  3. CS 128 Programming and Problem Solving (4 credits)
  4. CS 256 Data Structures (4 credits)
  5. MATH 300 Mathematical Statistics OR MATH 330 Mathematical Modeling (3 credits each)
  6. DS 401 Data Science OR CS 430 Database Systems OR CS 365 Artificial Intelligence and Machine Learning (3 credits each)
  7. One of the following courses (each 3 credits):
    • Any additional course from items 5 or 6 above
    • CS 310 Algorithms (required for the CS 430 option in item 6)
    • CS 290 Computational Modeling
    • CS 340 Scientific Computing
    • CS 345 Software Engineering
    • CS 360 Parallel and Distributed Computing
    • CS 383 Bioinformatics
    • DS 481 Internship
    • BIOL 241 Care and Use of Collections
    • BIOL 340 Applied Biostatistics
    • BIOL 410 or ENSU 310 Applications of GIS
    • PSYC 245 Research Design and Statistics
    • ECON 305 Econometrics

View a full list of courses and their descriptions.

As a data science major, you’ll be prepared for such careers as data scientist, data architect, data engineer, business intelligence developer, statistician and data analyst.

Data science is a broad term and may lead to very different careers, but people who do well in data science often have a strong mathematics background and love to find and solve quantitative puzzles. Data science also often involves programming, so projects of that kind are a great foundation for the work you’ll do in college.

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