In the Information Age, enormous amounts of data are generated every day in a range of areas, including social media, search engines, insurance companies, healthcare organizations, hospitals, defense, and retail. Data science is now a rapidly growing, high-paying field.  

As a student in the IU Online BS in Data Science, you collect, organize, and analyze data to make meaningful conclusions. You write programs to perform data analysis on large, complex datasets. You evaluate the social, legal, and ethical issues that arise from the mass collection of data.  

Specific areas of focus include: 

• Data acquisition and storage 
• Data exploration and curation 
• Data modeling and analysis 
• Data visualization and presentation 
• Data ethics and governance 

Your IU Online BS in Data Science prepares you for such careers as:  

• Business intelligence analyst  
• Data mining engineer  
• Data architect  
• Data scientist  

• Analytics manager  
• Research analyst   
• Information officer  

This 100 percent online, consortial program is taught by IU East, IUPUI, IU Kokomo, IU Northwest, IU South Bend, and IU Southeast. This consortial model allows you to take coursework from several campuses and learn from a wide range of faculty. 

Learning Outcomes

1. Data Acquisition and Storage

• Capture and organize different types of data from different sources as performed in a variety of industries.
• Manage data, data infrastructures, and the data science pipeline.
• Store and process data using distributed computing, overcoming issues with the process of data extraction, transformation, and loading.

2. Data Exploration and Curation

• Use metadata and indexing for data discovery, description, information retrieval, and reusability.
• Perform data transformations with justifications.
• Clean and recode data to prepare it for analysis using a variety of techniques.

3. Data Analysis and Modeling

• Apply quantitative techniques, including probability, statistics, optimization, machine learning, and simulation to deploy models for prediction and analysis.
• Write programs to perform data analysis on large, complex datasets.

4. Data Visualization and Presentation

• Assess the purpose, benefits, and limitations of visualization as a human-centered data analysis methodology.
• Design and implement effective visualizations for a variety of data types and analytical tasks to reveal insights and communicate information.

5. Data Ethics and Governance

• Evaluate social, legal, and ethical issues in data science, applying ethical principles to resolve conflicts.
• Support the ethical and appropriate use of technology by following a code of conduct.

Degree Requirements  

To earn the BS in Data Science, you must complete 120 credit hours. 

Requirements are broken down as follows:  

• Data science core courses, including capstone course (43 credit hours) 
• Professional communication courses (6 credit hours) 
• Computer science courses (11 credit hours) 
• Mathematics courses (9 credit hours) 
• Statistics courses (9 credit hours) 
• General education courses and electives, as needed to reach 120 credit hours.  

General education:  

• Students need to follow their home campus’s general education requirements (that probably include any requirements related to grade). 

Professional Communication (6 cr.)  

• Professional Speaking (3 cr.) Choose one:  

• CMLC-C 122 Interpersonal Communication (3 cr.) 
• COMM-C 180 Interpersonal Communication (3 cr.) 
• COMM-C 223 Business and Professional Communication (3 cr.) 
• SPCH-S 122 Interpersonal Communication (3 cr.) 
• SPCH-S 223 Business and Professional Communication (3 cr.) 

• Professional Writing (3 cr.) Choose one:   

• ENG-W 230 Science Writing (3 cr.) 
• ENG-W 231 Professional Writing (3 cr.) 
• ENG-W 233 Technical Writing/Intermediate Expository Writing (3 cr.) 
• ENG-W 234 Technical Reporting Writing 

• ENG-W 270 Argumentative Writing (3 cr.) 

Computer Science (11 cr.)  

• CSCI-A 201 Programming 1 (taught using Python) (4 cr.) 

• CSCI-A 202 Programming II (taught using Python) (4 cr.) 

• Data Structures:    CSCI-C 343 Data Structures (taught using Python) (3 cr.) 

Mathematics (9 cr.)  

• MATH-M 220 Calculus for Data Science 1 (3 cr.) 

• MATH-M 230 Calculus for Data Science II (3 Cr) 

•  MATH-M 301 Linear Algebra and Applications (3 cr.) OR  MATH-M 303 Linear Algebra (3 cr.) 

Statistics (9 cr.)  

• PBHL-B 275 Probability without Tears and Without Calculus (taught using Python)
• PBHL-B 302 Introduction to Biostatistics (3 cr.) (pre-req: at least college algebra)  or PBHL-B 285 Classical Biostatical Regression Learning (3 cr.) 

• PBHL-B 420 Introduction to Statistical Learning (3 cr.) Or INFO-I 415 Introduction to Statistical Learning (3 cr.) 

Data Science-Core (30 cr.)  

•  CSCI-N 211 Introduction to Database; OR CSCI-A 213 Database Applications
• CSCI-N 311 Database Programming, Oracle; OR CSCI-B 461 Database Concepts; OR CSCI-C 442 Database Systems; OR INFO-I 308 Information Representation
• CSCI-N 317 Computation for Scientific Applications
• INFO-I 223 Data Fluency
• INFO-I 416 Applied Cloud Computing for Data Intensive Sciences
• INFO-I 421 Applications of Data Mining
• INFO-I 453 Computer and Information Ethics
• INFO-I 490 Professional Internship (3 cr); OR INFO-I 491 Capstone
• NEWM-N 328 Visualizing Information
• PBHL-B 452 Fundamentals of Health Data Management