Data Science Major and Minor

Students in a variety of disciplines learn how to apply big data to real life situations.

What is Data Science?

"The data science major is the most exciting thing that has happened since I’ve been a faculty member at Luther. This major lets us reach out and really show how computer science and math are integral parts of the modern world of big data. The career opportunities for data science grads are virtually limitless." —Brad Miller, computer science professor

Data science is the study of how we extract meaning from data, and in a data driven world, this is an exciting time to seek a degree in this field. Data science is unique in that it combines techniques and theories from many fields including mathematics, computer science, probability and statistics, machine learning, pattern recognition, communication studies, art, and ethics.

Examples of data science problems and applications in the real world include:

  • Self driving cars and automated piloting of drones
  • Predicting flu virus outbreaks, earthquakes, 500-year floods
  • Product recommendations in e-commerce (Amazon, Netflix, and many others)
  • Understanding consumer behavior

At Luther, the data science major is built around the idea that data science skills can complement many other majors. The major is intended to provide you with the skills and abilities to process and understand large amounts of complex data in order to make new and better decisions. If you have broad knowledge, deep passion, the ability to think critically, and make connections across the disciplines, you will be successful in this major.

"The ability to process and find trends in large data sets is an ever increasing skill in many industries including healthcare, marketing, biology, finance and more. The data science major takes the best of computer science, statistics, economics, communication and creates a degree that will be coveted by companies for years to come. At many of these companies, we have vast amounts of data, but are lacking the resources and know-how to gather meaningful conclusions and test theories with those data sets." —Michael Noltner, '12

What makes data science unique is that it allows for you to explore a specific subject area. Whether you are in the sciences, the social sciences, or the humanities, the reach of data in your discipline and the need to understand its possibilities and its impact on others is growing every day.

The major consists of a required set of courses culminating in a capstone research experience.

Data Science Major Requirements

  • CS 120: Introduction to Data Science
  • CS 130: Fundamentals of Web Programming (7 weeks)
  • CS 140: Database Design and Querying (7 weeks)
  • CS 150: Introduction to Computer Science I
  • Math 115: Statistics (or equivalent statistics course)
  • Math 327: Applied Statistics
  • CS 320: Data Analysis and Visualization
  • CS 420: Applied Machine Learning
  • Subject Matter Courses: 3 courses
  • Capstone Research Project

Total Courses: 10

Examples of Subject Matter Courses

The subject matter preparatory courses provide the required background in one of the subject matter fields. Listed are some sample courses that would focus on the areas where there is an overlap with analytics:

Business Management

  • 250 Data Analysis for Business Decision Making
  • 260 Project Management
  • 368 Electronic Commerce

Economics

  • 130 Principles of Economics
  • 333 Economics of Information and Networks
  • 342 Introduction to Econometrics

Communication Studies

  • 133 Mass Media
  • 246 The Internet and American Life
  • 247 Electronic News Gathering

Biology

  • 248 Genetics
  • 356 Genomics
  • 354 Evolutionary Biology

Data Science Minor Requirements

  • CS 120: Introduction to Data Science
  • CS 130: Fundamentals of Web Programming 1/2 course
  • CS 140: Database Design and Querying 1/2 course
  • CS 150: Introduction to Computer Science I
  • Math 115: Statistics (or equivalent statistics course)
  • Math 327: Applied Statistics I
  • CS 320: Data Analysis and Visualization
  • CS 420: Applied Machine Learning

Total Courses: 7