IS 467 Fundamentals of Data Science
Summary
An introduction to the Knowledge Discovery Technologies covering all stages of a data mining process: domain understanding, data collection and selection, data cleaning and transformation, dimensionality reduction, pattern discovery, evaluation, and knowledge extraction. The course provides a comprehensive overview of data mining techniques used to realize these stages, including traditional statistical analysis and machine learning techniques. Students will analyze large datasets and develop modeling solutions to support decision making in various domains such as healthcare, finance, security, marketing, customer relationship management (CRM), and multimedia. This course section uses Python (Scikit-learn, Pandas , Matplotlib , Seaborn, NumPy packages) for all assignments, exercises, and final project.
Texts
Data Mining: Concepts and Techniques (Third Edition)
A volume in The Morgan Kaufmann Series in Data Management Systems
Authors: Jiawei Han, Micheline Kamber and Jian Pei
ISBN: 978-0-12-381479-1
Grading
5 Assignments (30%),
5 Exercises (20%),
Midterm Exam(10/4/17)-(25%),
Final Project (25%)
Prerequisites
IT 403 or CSC 423
This syllabus is subject to change as necessary during the quarter. If a change occurs, it will be thoroughly addressed during class, posted under Announcements in D2L and sent via email.
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