Data Mining, Discovery, and Exploration
Harvard Extension School
CSCI E-108
Section 1
CRN 17304
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, including web search, understanding interactions in social networks, recommender systems, analyzing data from 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 and interpretation of results. The hands-on component of the course uses a variety of libraries in the Python language, Scikit-Learn, NetworkX, Scikit-Learn-Extra, Mlextend, Surprise, and TensorFlow. Students may not take both CSCI E-96 and CSCI E-108 for degree or certificate credit.
Registration Closes: August 28, 2025
Credits: 4
View Tuition Information Term
Fall Term 2025
Part of Term
Full Term
Format
Flexible Attendance Web Conference
Credit Status
Graduate, Noncredit, Undergraduate
Section Status
Open