Data Analysis and Interpretation

Discover the fundamentals of data management and visualization, and learn how to develop a research question, describe variables, calculate basic statistics, and present results clearly.

• Understand the integral role of data in decision-making
• Learn to use powerful data analysis tools such as SAS or Python
• Gain insights into managing and visualizing data effectively

Develop and test hypotheses about data using statistical tests and strategies to apply the appropriate test to specific data and questions. Explore ANOVA, Chi-Square, and Pearson correlation analysis using SAS or Python.

• Learn a variety of statistical tests and their applications
• Gain insights into applying statistical tools to answer research questions
• Receive valuable feedback and provide insights to other learners

Focus on regression analysis using SAS or Python, starting with linear regression and progressing to multiple predictors, identifying confounding variables, and interpreting regression coefficients.

• Understand the assumptions underlying regression analysis
• Learn to interpret regression coefficients and use regression diagnostic tools
• Share and evaluate regression models with peers

Explore the process of developing, testing, and applying predictive algorithms using machine learning concepts. Learn about basic classification, decision trees, and clustering to predict future outcomes using data.

• Apply, test, and interpret machine learning algorithms
• Address research questions using alternative methods
• Build on integral supervised machine learning concepts

Engage in the Capstone project to apply and refine data analytic techniques learned from previous courses to address significant societal issues using real-world data from industry and academic partners.

• Work with industry partners such as DRIVENDATA and The Connection
• Choose information that best conveys results and implications
• Develop a professional-quality report of findings