Applied Enterprise Analytics
Description
Successful companies realised the power of data driven decision making a few decades back when analytics became a lever to succeed. Over the years the landscape evolved and became much more complex, with large volumes of complex data streaming in and being stored, waiting to be analysed. In order to analyse this new era data, companies need newer technologies and algorithms in order to extract the insights needed to make business impact. This is where Machine Learning comes in handy. Machine Leaning helps to solve business challenges with the help of data. Today's business challenges start with large volumes of complex data. Effective decision-making requires state-of-the-art techniques for predictive modeling. In this course, you learn about the three main requirements for moving rapidly from data to decisions: 1) state-of-the-art techniques for predictive modeling: machine learning; 2) powerful and easy-to-use software that can help you wrangle your data into shape and quickly create many accurate predictive models: SAS Viya and related tools; 3) and an integrated process to manage your analytical models for optimal performance throughout their lifespan.
Requisites
Prerequisites: IS454/ ANLY104/IS217/MGMT108 - Pre-req
Co-requisites: None
Anti-requisites: None
Attributes
Department: SCIS
Course Level: Undergraduate
Tracks: IS/T4BS: Business Analytics Track
Areas: Advanced Business Technology Major Business Options Business-Oriented Electives Econ Major Rel/Econ Options IS Depth Electives Social Sciences/PLE Major-related
Learning Outcomes
Graduate Learning Outcomes
Disciplinary Knowledge, Multidisciplinary Knowledge, Critical thinking & problem solving
Competencies
Data Analytics, Enterprise Architecture, Computational Modelling, Pattern Recognition Systems