Purdue CS440: Large-scale Data Analytics
(Spring 2026)

Course Description

"Big data" has been a buzzword for a long time. Many disruptive techniques have been developed to address various aspects of big data. This course will cover the key concepts, design principles, and systems to analyze large-scale data in order to extract novel and transformative insights. Tentative topics include database fundamentals, big data storage (e.g., HDFS), big data computing frameworks (e.g., Hadoop and Spark), data warehouses, data lakes, graph analytics (e.g., Spark Graph), data streaming (e.g., Spark Streaming), large-scale machine learning (e.g., Spark MLlib), vector databases, and cloud-native data analytics.

Instructor

• Jianguo Wang
• Email: csjgwang@purdue.edu (note: must include "[CS440]" in the subject)

Teaching Assistants

• Shige Liu (liu3529@purdue.edu)
• Yunan Zhang (zhan4404@purdue.edu)
• Jiayi Liu (liu4127@purdue.edu)

Logistics

• When: MW 3:30p-4:20p
• Where: Wilmeth Active Learning Center 3087
• Office hour: after class or make appointment
• Pre-requisites: CS242, CS251, and CS373

Labs and PSOs

• L01: Tuesday 1:30pm-3:20pm (LWSN B131)
• L02: Friday 9:30am-11:20am (LWSN B131)
• L03: Wednesday 9:30am-11:20am (LWSN B131)
• L04: Wednesday 11:30am-1:20pm (LWSN B131)

Online communications

• We'll use Piazza, e.g., announcements, discussions, and Q&A.
• We'll NOT use Brightspace except for sending emails occasionally.
• We'll use Gradescope for submitting and grading homeworks.

Textbooks (Optional)

• Spark: The Definitive Guide. By Bill Chambers and Matei Zaharia.
• Database System Concepts (7th edition). By Avi Silberschatz, Henry F. Korth, and S. Sudarshan.
• Mining of Massive Datasets. By Jure Leskovec, Anand Rajaraman, and Jeff Ullman.

Grading

• Homeworks: 20% (2 * 10%)
• Midterm exam: 25%
• Final exam: 35%
• Project: 20% (2 * 10%)
• Projects are related to the labs and will be explained in the labs.
• Extra credits: 5%

Academic Integrity and More

• Read here

Schedule

Lecture

Topic