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Applied Data Science Minor

Businesses, organizations, and institutions need employees who can capitalize on their data. Our interconnected world, bound by ubiquitous mobile devices and sensors, captures communications and interactions. These technologies—and the volume, variety, and velocity of the data they create—open opportunities to discover insightful correlations and predict outcomes.

When organized and managed, the data provides the basis for valuable knowledge. Data-driven insights help us better understand ourselves, our communities, and the global market, enabling us to run businesses more efficiently, make groundbreaking scientific discoveries, and promote the common good.

Careers

Possible careers for students with a minor in Applied Data Science include:

The minor provides students with the skills need to join a workforce seeking to maximize value from data. Big data differs from small data because its size and complexity demand new tools and techniques to glean useful information. Consequently, whether the goal is to work for a nonprofit or business, hospital or museum, specific data competencies are required. Students in the minor will learn these, including data management, analytics, and visualization for a career as data professionals.

Plan of Study

Required Courses (12 cr.)

Prerequisites (17–19 cr.)

The following mathematics, statistics, and programming courses must be completed before enrollment in INFO-I 415 and INFO-I 416:

MATH 159 Precalculus (5 cr.) may be taken in place of MATH 153 and MATH 154.

Select one statistics course:

Select one programming course:

Select one database course:

Students must earn a C or higher in each course to graduate with the Applied Data Science minor.

Admissions and Advising

The Applied Data Science minor is open to IUPUI students in any major. To declare the Applied Data Science minor, email Jill Mathews at jilmathe@iupui.edu. For academic advising, contact an undergraduate advisor.

Learning Outcomes

  1. Understand data science concepts, techniques, and tools to support big data analytics.
  2. Organize, visualize, and analyze large, complex datasets using descriptive statistics and graphs to make decisions.
  3. Apply inferential statistics, predictive analytics, and data mining to informatics-related fields.
  4. Identify, assess, and select appropriately among data analytics methods and models for solving real-world problems, weighing their advantages and disadvantages.
  5. Conceptualize and design effective visualizations for a variety of data types and analytical tasks.
  6. Assess the purpose, benefits, and limitations of visualization as a human-centered data analysis methodology.