Applied Data Science Minor
Unlock the keys to data-driven decision-making
Learn to fulfill the potential of data by earning a minor in applied data science through the IU School of Informatics and Computing at IUPUI.
Businesses, organizations, and institutions need employees who can capitalize on the enormous amounts of information being generated each day. You can be the one who organizes and manages data to provide the basis for valuable knowledge through your courses in this program, jointly offered by the Library and Information Science and Human-Centered Computing departments.
Making the data connections
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.
You’ll learn to develop data-driven solutions that can 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.
The applied data science minor develops your mathematical and technological skills to analyze data sets. You’ll also learn about the societal implications of data work, including privacy and surveillance.
There’s a growing need for people with deep analytical skills who can make effective decisions. Careers for students with a minor in Applied Data Science include:
- Business Intelligence Analyst
- Business Technology Analyst
- Data Project Manager
- Data Warehousing Specialist
- Database Administrator
- Document Management Specialist
- Informatics Project Manager
- Information Architect
- Logistics Analyst
- Operations Research Analyst
- Software Developer
This minor provides students with the skills needed 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.
Whether your goal is to work for a nonprofit or business, hospital or museum, you’ll require specific data competencies. Through your applied data science courses, you’ll learn data management, analytics, and visualization for a career as a data professional.
Plan of Study
Required Courses (12 cr.)
- LIS-S 202 Data Organization and Representation (3 cr.)
- NEWM-N 328 Visualizing Information (3 cr.)
- INFO-I 415 Introduction to Data Analytics for Informatics (3 cr.)
- INFO-I 416 Applied Cloud Computing for Data Intensive Sciences (3 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 15300 College Algebra (3 cr.)
- MATH 15400 Trigonometry (3 cr.)
- MATH 17100 Multidimensional Mathematics (3 cr.)
MATH 159 Precalculus (5 cr.) may be taken in place of MATH 153 and MATH 154.
Select one statistics course:
- ECON-E 270 Introduction to Statistical Theory in Economics and Business (3 cr.)
- PBHL-B 300 Introduction to Biostatistics (3 cr.)
- SPEA-K 300 Statistical Techniques (3 cr.)
- PBHL-B 302 Biostatistics for Informatics (3 cr.)
- STAT 30100 Elementary Statistical Methods 1 (3 cr.)
- STAT 35000 Introduction to Statistics (3 cr.)
Select one programming course:
- CIT 21500 Web Programming (3 cr.)
- CSCI 23000 Computing I (4 cr.)
- INFO-I 210 Information Infrastructure I (4 cr.)
- NEWM-N 220 Introduction to Media Application Development (3 cr.)
Select one database course:
- CIT 21400 Introduction to Data Management (3 cr.)
- CSCI-N 211 Introduction to Databases (3 cr.)
- CSCI 44300 Database Systems (3 cr.)
- HIM-M 200 Database Design for Health Information Management (3 cr.)
- INFO-I 308 Information Representation (3 cr.)
Students must earn a C or higher in each course to graduate with the Applied Data Science minor.
- Understand data science concepts, techniques, and tools to support big data analytics.
- Organize, visualize, and analyze large, complex datasets using descriptive statistics and graphs to make decisions.
- Apply inferential statistics, predictive analytics, and data mining to informatics-related fields.
- Identify, assess, and select appropriately among data analytics methods and models for solving real-world problems, weighing their advantages and disadvantages.
- Conceptualize and design effective visualizations for a variety of data types and analytical tasks.
- Assess the purpose, benefits, and limitations of visualization as a human-centered data analysis methodology.