IS454
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Applied Enterprise Analytics
Course (UG/PG)
Undergraduate
Offering Unit/Department
Course 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.
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.
Course Learning Outcomes
1. Demonstrate the business value of data analytics and machine learning
2. Formulate business problems from the given dataset
3. Develop data features to benefit business decisions
4. Prepare data and manipulate it to improve outcomes
5. Structure machine learning pipelines and evaluate them for effectiveness
6. Summarize unstructured textual data and use it enhance the predictive power of models
7. Gain expertise with supervised machine learning models by learning about their nuances
8. Build and compare machine learning pipelines based on various techniques
9. Assess the results of various pipelines and compare performance
10. Deploy and manage machine learning models in the business environment
Discipline-Specific Competencies
Data Analytics, Enterprise Architecture, Computational Modelling, Pattern Recognition Systems
SMU Graduate Learning Outcomes
Disciplinary Knowledge, Multidisciplinary Knowledge, Critical thinking & problem solving
Grading Basis
GRD - Graded
Course Units
1