Data Science for Business

Description

Upon completion of this course, the student is able to: Understand and explain what data science and analytics are. Understand and explain how different types of data-driven methods can be used in business to support decision-making and create value. Understand and explain the concepts of descriptive analytics (unsupervised learning), predictive analytics (supervised learning) and prescriptive analytics. Understand and explain the analytics process model. Understand and explain various data-driven methods, such as decision trees and ensemble methods, regression methods and neural networks, clustering methods, etc. Acknowledge the importance of data and suggest and apply recent technologies to analyze data for developing effective data-driven business solutions. Recognize and formulate different data-driven decision support solutions depending on decision problem characteristics. Evaluate the quality of analytical models. Implement, run and evaluate data analytical experiments using a specific toolset (for example, RapidMiner). Evaluate and discuss the application of analytics in real life business settings.