Data Analytics Minor

Requirements for the Minor

The department of Computer Science offers a minor in Data Analytics, the process of analyzing, revealing, interpreting and visualizing information concealed inside data. The analysis of datasets is already revolutionizing our understanding of fields from genetics to Environmental Science, Economics to Engineering, English, History and Political Science. The minor will introduce students to the concepts and techniques underlying the acquisition, transformation, analysis and visualization of data and analytical outcomes, and will leverage courses across the college that address analytics in a discipline-specific way. Requires the following six courses, four core courses and two electives. A maximum of three courses from the same department as your major may be used for the minor with advisor approval.

Four Core Courses:

• An introductory CS course, one of the following:

• CSC 103 - Taming Big Data: Introduction to Computer Science
• CSC 104 - Robots Rule! Introduction to Computer Science
• CSC 105 - Game Development: Introduction to Computer Science
• CSC 106 - Can Computers Think? Introduction to Computer Science
• CSC 107 - Creative Computing: Introduction to Computer Science
• CSC 108 - Scientific Computing: Introduction to Computer Science

• A statistics course, one of the following:

• BME 210 - Statistical Methods in Biomedical Engineering
• ECO 243 - Introduction to Econometrics
• MER 301 - Engineering Reliability
• PSC 220 - Social Data Analysis
• PSY 200 - Statistical Methods in Psychology
• SOC 300 - Quantitative Methods of Social Research
• STA 104 - Introduction to Statistics
• STA 164 - Strategies of Experimentation: Statistical Design and Analysis of Experiments

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• A data analytics course, at least one of the following (additional courses may be used as electives):

• CSC 233 - Intro to Data Analytics
• ECO 364 - Business Analytics
• ENS 215 - Exploring Environmental Data
• STA 364 - Big Data Analytics

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• A data visualization course, at least one of the following (additional courses may be used as electives):

• CSC 234 - Data Visualization
• ECO 134 - Data Visualization

• Two additional courses:
   

  Minors choose two additional courses. Only one course numbered below 100 may count towards the minor. Suggested courses are below. Additional courses may be approved by the minor director.  Students may also take an independent study with a faculty member, in consultation with the minor director.

• AST 230 - Observational Astronomy
• AST 240 - Radio Astronomy
• BIO 324 - Plant Ecology w/lab
• BIO 375 - Exercise Physiology
• CHM 240 - Analytical Chemistry
• CHN 301 - Advanced Intermediate Chinese 2
• CHN 401 - Media China
• CLS 156 - The Ancient Economy
• CLS 193 - History Done Digitally
• CSC 206 - Text Analytics

• CSC 233 Intro to Data Analytics

• CSC 234 Data Visualization

• CSC 235 - Modeling & Simulation
• CSC 243 - Bioinformatics: Information Technology in the Life Sciences
• CSC 321 - Data Mining and Machine Learning
• CSC 340 - Introduction to Databases
• ECE 329 - Neural Networks
• ECE 347 - Image Processing
• ECO 156 - Analysis of Ancient Economic Activity
• ECO 338 - Quantitative Methods in Economics
• ECO 353 - Seminar in Econometrics

• ECO 364 Business Analytics 

• ENS 204 - Geographic Information Systems

• ENS 215 Exploring Environmental Data

• GEO 207 - Stable Isotopes in Environmental Science
• GEO 209 - Paleoclimatology
• GEO 302 - Geochemical Systems and Modeling
• GEO 303 - Earthquakes and the study of the Earth’s Interior
• GEO 305 - Global Biogeochemical Cycles
• HST 140 - History Done Digitally
• MER 262 - Programming and Writing in ME
• MER 331 - Fluid Mechanics 1
• MTH 140 - Applied Linear Algebra
• MTH 340 - Linear Algebra
• MLT 209 - The New Wall of China
• PHY 122 - Relativity, Quantum, and Their Applications
• PHY 123 - Heat and Light
• PHY 300 - Methods of Modern Experimental Physics
• PHY 310 - Advanced Topics in Physics 1
• PSC 123 - Topics in Mathematical Political Science
• PSY 300 - Research Methods in Psychology
• STA 264 - Regression Analysis

• STA 364 Big Data Analytics