Course Schedule, Part-time Online Program

Please note that course schedules may be amended due to low enrollment, faculty availability, and/or other factors.

Online Sync Sessions are an integral part of the online learning experience. Additional information about learning concepts and assignments may be discussed and sync sessions offer valuable opportunities for students to interact with their faculty and peers during the term. We encourage all students to attend live, but if they are unable to, sync sessions will be recorded and posted within Canvas to allow for an asynchronous model of success as well.

MSDS 413-DL : Time Series Analysis and Forecasting


Description

This course covers analytical methods for time series analysis and forecasting. Specific topics include the role of forecasting in organizations, exploratory data analysis, stationary and non-stationary time series, autocorrelation and partial autocorrelation functions, univariate autoregressive integrated moving average (ARIMA) models, seasonal models, Box-Jenkins methodology, regression models with ARIMA errors, multivariate time series analysis, and non-linear time series modeling including exponential smoothing methods, random forest analysis, and hidden Markov modeling.

Recommended prior course: MSDS 410-DL Supervised Learning Methods and MSDS 411-DL Unsupervised Learning Methods

Prerequisites: (1) MSDS 420-DL Database Systems and Data Preparation or CIS 417 Database Systems Design and Implementation and (2) MSDS 422-DL Practical Machine Learning or CIS 435 Practical Data Science Using Machine Learning.


Fall 2022
Start/End DatesDay(s)TimeBuildingSection
09/20/22 - 12/10/22Sync Session W
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Riggs, Jamie
Online
Open

Spring 2023
Start/End DatesDay(s)TimeBuildingSection
03/27/23 - 06/10/23Sync Session W
7 – 9:30 p.m. 55
InstructorCourse LocationStatusCAESAR Course ID
Riggs, Jamie
Online
Open
^ Back to top ^