| Course ID: | BUSN 5000. 3 hours. |
| Course Title: | Introduction to Data Science for Business and Economics |
Course
Description: | The modern world is awash in a seemingly unlimited amount of data. Harnessing this data for decisions starts with acquiring the raw information and ends with a report describing the outcome of some analysis. At each step, the analyst combines data with some ideas about how the world works to produce an output. A hands-on approach, with a focus on techniques of data preparation; descriptive, explanatory, and predictive analyses; and scientific communication. |
| Oasis Title: | Intro to Data Science for Busn |
| Prerequisite: | BUSN 3000 or BUSN 3000E or BUSN 3000H |
| Grading System: | A-F (Traditional) |
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| Course Objectives: | After completing this course, the student should understand:
1. How social, economic, and business data are produced from different information.
2. How to identify and select data appropriate to an inquiry.
3. How to produce replicable, properly curated research results based on confidential and public-use data files.
4. How to map economic models, business problems, and policy questions to hypotheses about relationships in data.
5. The basic concepts of causal inference and how to implement basic research designs to facilitate causal inference.
6. The basic concepts of statistical learning and how to implement them to make predictions about economic, business, or policy outcomes.
7. How to obtain data and prepare it for analysis.
8. How to describe data and perform basic descriptive, explanatory, and predictive analyses.
9. How to communicate the results from descriptive, explanatory, and predictive analyses. |
| Topical Outline: | 1. Data: theory and measurement [3 weeks]
a. Finding data for analysis
b. Ethical issues in data collection and analysis
c. Concept Validity: What are we measuring?
d. External Validity: Who are we measuring?
2. Description: Communicating with and about data [3 weeks]
a. Summarizing and exploring data
b. How to present empirical results
c. The principles of replicable research
3. Decisions: prediction and causation [9 weeks]
a. Using data to predict:
i. Statistical learning
ii. Model selection
iii. Dealing with high dimensional data
iv. Cross-validation
v. Classification
b. Using data to explain: causal research primer
i. Counterfactual thinking
ii. Research designs for causal inference |
| Honor Code Reference: | All students are responsible for maintaining the highest
standards of honesty and integrity in every phase of their
academic careers. The penalties for academic dishonesty are
severe, and ignorance is not an acceptable defense.
Academic honesty means performing all academic work without
plagiarizing, cheating, lying, tampering, stealing, receiving
assistance from any other person, or using any source of
information that is not common knowledge. |