Course Information:

  • IDS 561 - Analytics for Big Data
  • Spring 2023
  • DH 320 Tue. 3:30pm - 6pm (46596) / Tue. 6:30pm - 9pm (45604)
  • yuhenghu at uic dot edu
  • Office hour: Email


The “big data” paradigm has drawn a significant amount of attention in recent years as costs of acquiring and storing data have plummeted. Instead, bottlenecks have been shifted to fast and in-depth analysis. However, this shift has created its own set of problems, the most obvious one is that large datasets are often computationally expensive to process. Algorithms that are efficiently capable of processing data that fit in memory may become prohibitively expensive to use on larger datasets. Consequently, it can be difficult to gain insights from the underlying data.

This course is an introductory course for big data analytics and data science. It has three main goals. First, it is intended to provide the student with an appreciation for the issues involved in doing data science to work on datasets that do not fit in main memory. Second, it is intended to provide a working knowledge of and experience with some of the current distributed frameworks (e.g. Hadoop). Third, the course is intended to provide students with hands-on opportunities to implement solutions using real-world datasets.

Academic Integrity

You are expected to adhere to the highest standards of academic honesty. Unless otherwise specified, collaboration on assignments is not allowed. Use of published materials is allowed, but the sources should be explicitly stated in your solutions. Violations will be reviewed and sanctioned according to the University Policy on Academic Integrity. Collaborations among team members are only allowed for the final term projects that are selected. "Academic integrity is the pursuit of scholarly activity free from fraud and deception and is an educational objective of this institution. Academic dishonesty includes, but is not limited to, cheating, plagiarizing, fabricating of information or citations, facilitating acts of academic dishonesty by others, having unauthorized possession of examinations, submitting work for another person or work previously used without informing the instructor, or tampering with the academic work of other students." For more information about violations of academic integrity and their consequences, consult


IDS 400/401, and IDS 572

Recommended textbooks

In-class survey

Weekly Schedule

Classs Date Topic Assignment Note
Jan 10 Introduction of Big data analytics HW #0 Environment setup and testing
Jan 17 Big data with MapReduce I Lab #1 on Jan 23
Jan 24 Big data with MapReduce II HW #1 MapReduce using Python
Jan 31 Big data with Spark I Quiz #1
Feb 7 Big data with Spark II HW #1 due, Lab #2 on Feb 13
Feb 14 Real-time Streaming Computing Models HW #2 Programming with Spark Project proposal due
Feb 21 Query Processing on Big Data Platforms I
Feb 28 Query Processing on Big Data Platforms II Quiz #2
Mar 7 Data Frame on Spark Lab #3 on Mar 13
Mar 14 CAP theorem and NoSQL databases HW #3 Data Processing using Spark HW #2 due
Mar 21 No class; Spring break
Mar 28 Machine Learning for Big Data: Recommender Systems Quiz #3, HW #3 due, Mid-term project progress report due
Apr 4 Tentative; Instructor out of town
Apr 11 Machine Learning for Big Data: Clustering HW #4 Recommender system in Spark Lab #4 on Apr 17
Apr 18 Machine Learning for Big Data: Graph analysis + Frequent Pattern Mining HW #4 due, Quiz #4
Apr 25 Final Presentation