Data Mining, Discovery, and Exploration
Harvard Summer School
CSCI S-108
Section 1
CRN 35576
Extracting actionable insights and relationships from massive complex data sets is the domain of data mining. Data mining has wide-ranging applications in science and technology. These include web search, interactions in social networks, recommender systems, processing signals in large internet-of-things (IoT) sensor networks, image search, genetic analysis, and discovery of interactions between drugs. This course surveys a range of unsupervised learning algorithms for data mining. The emphasis is on graph algorithms and scaling for massive datasets. The course comprises readings and lectures on theory along with hands-on exercises and projects where students apply the theory through Python coding. For the hands-on component of the course a variety of libraries in the Python language, including possibly Scikit-Learn, NetworkX, Neo4J, Scikit-Learn-Extra, Mlextend, and Surprise are used. Students enrolled for graduate credit are required to perform, present, and report on an independent project. This project must demonstrate a mastery of methods covered in the course as applied to a suitable real-world data set.
Registration Closes: June 20, 2024
Credits: 4
View Tuition Information Term
Summer Term 2024
Part of Term
Full Term
Format
Flexible Attendance Web Conference
Credit Status
Graduate, Undergraduate
Section Status
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