This course builds on DATA 3200 to provide students with a more robust understanding of the tools of a Data Scientist. Data Analytics combines data, computation and inferential thinking to solve challenging problems to thereby better understand the world. This class explores key principles and techniques of data science, including quantitative critical thinking and algorithms for machine learning methods. It will also introduce students to the ways in which data analytics is deployed in healthcare, marketing, political science, criminal justice, and other fields. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.
Prerequisite(s): DATA 3200 Principles and Techniques of Data Analytics I and MATH 1600 Calculus I.
DATA 3200 Principles and Techniques of Data Analytics I (3 hours)
This course is based heavily on UC Berkeley's Data 100 class. Data Analytics combines data, computation and inferential thinking to solve challenging problems and understand their intricacies. This class explores key principles and techniques of data science, and teaches students how to create informative data visualizations. It also explores particular concepts of Linear Algebra which are central to Data Science. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.
Prerequisite(s): CMPSC 2100 Python Programming II and DATA 1400 Foundations of Data Analytics II.
MATH 1600 Calculus I (5 hours)
An introduction to calculus of a single variable. Topics include limits, continuity, differentiation, and beginning integration with applications. Assignments are given that help build proficiency in the use of a computer algebra system.
Prerequisite(s): Math ACT score of at least 27, or a grade of "C" or better in MATH 1470 Trigonometry or MATH 1400 Pre-Calculus, or permission of the instructor.
(Normally offered each semester.)
Archway Curriculum: Foundational Literacies: Mathematical Problem Solving
