STAT 386
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Data Science Process
Course Description
Principles of data science; web scraping; data wrangling; exploratory data analysis; data visualization; ethics; version control; data communication.
When Taught
Fall and Winter
Min
3
Fixed/Max
3
Fixed
3
Fixed
0
Title
Problem Definition and Data Acquisition
Learning Outcome
Formulate data-driven questions, define objectives, and collect relevant data sources.
Title
Data Wrangling and Cleaning
Learning Outcome
Prepare and clean messy datasets for exploratory analysis, modeling, and visualization.
Title
Exploratory Data Analysis and Feature Engineering
Learning Outcome
Analyze data to identify patterns, detect anomalies, and engineer meaningful features for modeling.
Title
Programming for Data Science
Learning Outcome
Use programming tools to manipulate, analyze, and visualize data efficiently.
Title
Ethics in Data Science
Learning Outcome
Analyze and debate ethical and legal issues, including privacy, data sharing, and algorithmic decision-making.
Title
Version Control and Reproducibility
Learning Outcome
Use version control and best practices to ensure reproducible and well-documented workflows.
Title
Communication and Stakeholder Engagement
Learning Outcome
Communicate findings effectively to both technical and non-technical audiences through reports, presentations, visualizations and/or documentation.
