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B.S. in Data Science

General Overview: 

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, ÏòÈÕ¿ûÊÓƵ, 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.

You can transfer up to 64 credits from a regionally accredited community college, or 90 credit hours from an accredited four-year university or college.

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 Requirements:

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

Distribution Requirements:

Foundations of 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.)

Foundations-Computer Science (10 cr.)

• Computer Science Programming I:
  • CSCI-A 201 Programming 1 (taught using Python) (3 cr.)
• Computer Science Programming II:
  • CSCI-A 202 Programming II (taught using Python) (3 cr.)
• Computer Science – Data Structures:  
  • CSCI-C 343 Data Structures (taught using Python) (4 cr.)

Foundations-Mathematics (9 cr.)

• Calculus for Data Science I:
  • MATH-M 220 Calculus for Data Science 1 (3 cr.)
• Calculus for Data Science II:
  • MATH-M 230 Calculus for Data Science II (3 Cr)
• Linear Algebra: (choose 1)
  • MATH-M 301 Linear Algebra and Applications (3 cr.)
  • MATH-M 303 Linear Algebra (3 cr.)

Foundations-Statistics (9 cr.)

• Computational Probability & Statistics: 
  • PBHL-B 302 Introduction to Biostatistics (3 cr.) (pre-req: at least college algebra)
• Computational Biostatistics:
  • PBHL-B 285 Classical Biostatical Regression Learning (3 cr.)
• Statistical Learning & Data Analytics:
  • PBHL-B 420 Introduction to Statistical Learning (3 cr.) Or INFO-I 415 Introduction to Statistical Learning (3 cr.)

Data Science-Core (43 cr.)

• Data Fluency:
  • INFO-I 223 Data Fluency (3 cr.)
• Database Programming:
  • CSCI-B 461 Database Concepts (4 cr.)
  • CSCI-C 442 Database Systems (3 cr.)
  • CSCI-N 311 Database Programming Oracle (3 cr.)
  • INFO-I 308 Information Representation (3 cr.)[replace with INFO-I 399 after HLC approval 3 cr.)
• Introduction to Data Management:
  • CSCI-A 213 Database Applications (3cr.)
  • CSCN-N 211 Introduction to Database (3cr.)
• Data Applications:
  • CSCI-N 317 Computation for Scientific Applications (3 cr.)
• Cloud Computing:
  • INFO-I 416 Applied Cloud Computing for Data Intensive Science (3 cr.)
• Data Mining:
  • INFO-I 421 Applications of Data Mining (3 cr.)
• Ethics:
  • INFO-I 453 Computer and Information Ethics (3 cr.)
• Data Visualization:
  • NEWM-N 328 Visualizing Information (3 cr.)
• Data Management:
  • PBHL-B 452 Fundamentals of Data Management (3 cr.) (using R)
• Internship/Capstone:
  • INFO-I 490 Professional Practicum/Internship (no credit) or
  • INFO-I 492 Senior Thesis (3 cr.)