CSE012/CS059 – Data Mining Fall 2025

Home Material Lectures Tutorials Assignments

Lecture Slides For the slides of this course we will use slides and material from other courses and books. Many thanks to: Tan, Steinbach and Kumar, Anand Rajaraman Jeff Ullman, and Jure Leskovec, Evimaria Terzi, Aris Anagnostopoulos for the material from their slides that we have used in this course. Introduction: Logistics (in Greek) (pptx, pdf) Lecture 1: Introduction to Data Mining (pptx, pdf) Chapters 1,2 from the book “Introduction to Data Mining” by Tan Steinbach Kumar. Article: Data Scientist: The Sexiest Job of the 21st Century. Lecture 2: What is data? The data mining pipeline. Preprocessing and postprocessing. Sampling and normalization. (pptx, pdf) Chapter 2 from the book “Introduction to Data Mining” by Tan Steinbach Kumar. Chapter 1 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec. Lecture 3: Data exploration and statistical analysis (pptx, pdf) Chapter 1 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec. Chapters 7-8 (confidence interval, standard error), 11 (hypothesis testing), 16 (independence and correlation tests) from the book All of Statistics byLarry A. Wasserman (the chapter numbers are for the pdf, in the actual book the chapter numbers are -1 of the ones above). Chapters 5,6 from The Data Science Design Manual by Steven S. Skiena Error bars in experimental biology.