- LIS 61095-001
- ST: Intro. to Data Mining and Machine Learning
- August 28 - November 5
- 100% online
- Professor: Dr. Emad Khazraee
Course Description
In the recent years, the rate of data production increased dramatically which makes it necessary for information professionals to engage with a new set of tools and methods to deal with data overload and increase our efficiency to extract powerful insights from massive data sets. Data mining and machine learning are two important topics in this regard. Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. Data mining uses the algorithms developed in machine learning and is the science of discovering structure and making predictions in large, complex data sets. This course is designed to provide a conceptual understanding of these methods to the library and information professionals and to introduce students to the basic concepts and techniques of Data Mining. By the end of this course student will develop an understanding of the strengths and limitations of popular data mining techniques and machine learning algorithms and will be able to identify promising applications of data mining. Students will be able to actively manage and participate in data mining projects executed by consultants or specialists in data mining. Students will also develop skills of using recent data mining software for solving practical problems. While the courses aims to introduce the most common algorithms and methods in data mining and machine learning there is no need for prior programming skills or advance math. One useful takeaway from the course will be the ability to perform powerful data analysis in popular data mining tools such as Weka and RapidMiner.
About the Professor
- Dr. Emad Khazraee holds a doctorate in information studies from Drexel University.
- His research looks at cultural differences in new media use and the relationship between social change and digital technologies.
- His research uses socio-technical approaches to social media studies and conceptual frameworks developed in Science Technology Studies (STS) to explore the role of social media in social transformations.