Course Description

Harnessing data for decision-making begins with acquiring the raw information and ends with communicating the results of analysis. This course covers the data science skills necessary at every stage of the value chain, including data transformation; descriptive, explanatory and predictive analyses; and professional communication.

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Athena Title

Intro to Data Science for Busn

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Equivalent Courses

Not open to students with credit in BUSN 5000

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Non-Traditional Format

This course will be taught 95% or more online.

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Prerequisite

BUSN 3000 or BUSN 3000E or BUSN 3000H

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Grading System

A - F (Traditional)

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Student Learning Outcomes

• Students will acquire and prepare data for analysis.
• Students will design reproducible data analyses.
• Students will map business problems and policy questions to hypotheses about relationships in data.
• Students will describe data and perform basic descriptive analysis.
• Students will implement and interpret basic causal-inference research designs.
• Students will implement and interpret basic machine-learning algorithms.
• Students will communicate the results from descriptive, causal and predictive analyses.

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Topical Outline

• Data fundamentals and beginning to learn
• Models for exploration
• Making inferences
• Measurement error, sample selection, and confounding
• Bayesian approach to learning from data
• Regression fundamentals
• Potential outcomes and causal inference
• Regression discontinuity
• Difference in differences
• Introduction to machine learning

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Institutional Competencies

Analytical Thinking

The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.

Critical Thinking

The ability to pursue and comprehensively evaluate information before accepting or establishing a conclusion, decision, or action.

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