Introduction to data science for business

This is an introductory course to data science for business. It explains how useful information and knowledge can be extracted from large volumes of data and represented as various models to improve business decision making. It emphasizes in the qualitative and graphical understanding of the core concepts in data science from the perspective of business users.

This course covers a wide array of principles and techniques in data science. Upon the completion of this course, you will be able to: 1) Explain the data science principles and techniques to business users. The core concepts include Entropy, Information Gain, Classification with Decision Tree, Class Probability Estimation with Logistic Regression, Model Performance Evaluation with various metrics such as confusion matrix and Profit curve and ROC curve, Expected Value based Decision Analytic Thinking, Similarity Measure, Clustering Analysis, Co-occurrence, Association Rules Mining, Text Mining. 2) Communicate business needs in precise data science terminology to data science team for model building, and provide ongoing supports and feedbacks for model implementation and model performance tracking.

The textbook adapted in this course is Data Science for Business: What you need to know about data mining and data-analytic thinking, 1st edition, (2013) Foster Provost and Tom Fawcett, O’Reilly, ISBN-13: 9781449361327.