4 credits
Required Prerequisites: High school algebra
Satisfies requirements for: BS, MSA, QR/1, PitE Practical Experience; Quantitative Analysis Sequence for Biology, BHS, EEB, MCDB, and Microbiology majors
Meets: Monday, Tuesday, Wednesday
Instructor: Mark Fredrickson
Course Description:
Decode the stories hidden in data. Turn bird songs into stunning visualizations. Translate soil samples into ecological insights with statistical modeling. Train machine learning models to spot trends in climate and habitat data. In DATASCI 101, students will unlock the power of data using computer programming, statistical inference, and ethical reasoning. The course emphasises an hands-on approach to data analysis through Python programming, and students will have frequent opportunities to analyze real-world datasets, including many collected at UMBS.
