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.
CMPSC 2100 Python Programming II (4 hours)
A project-based continuation of the techniques developed in CMPSC 1100 Python Programming I. Topics include object-oriented programming, algorithm design and analysis, data structures, and general problem-solving techniques (such as recursion) while following industry-standard software development principles.
Prerequisite(s): Grade of "C" or better in CMPSC 1100 Python Programming I or permission of instructor.
DATA 1400 Foundations of Data Analytics II (3 hours)
This course is intended as a continuation of Foundations of Data Analytics I. In this course, you'll learn how Data Analytics are applied within the workforce. Particular attention will be paid to the role of the Data Scientist or Analyst, machine learning and the applications of Big Data. By the end of the term, you will be able to design and execute a range of data-driven experiments. This course is an online class offered through the Lower Cost Models Consortium. The class has optional live sessions.
Prerequisite(s): DATA 1300 Foundations of Data Analytics I.
