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COMP 4311

Big Data

0.5 credits · Undergraduate

Winter 2024

Topics

  • – 14, Introduction to Big Data
  • – 21, Distributions, Probability, Statistics
  • – 28, Descriptive & Inferential Statistics
  • – ANOVA, Linear/Logistic Regression
  • – 18, Machine Learning
  • – , Classification, Clustering
  • – 10, Clustering, Model Evaluations
  • – 17, Big Data Technologies
  • – 24, Hadoop, HDFS
  • – 31, MapReduce
  • – , Case Study, Review

Readings

  • Required: Moore, D., George P. McCabe, and B. Craig. Introduction to the Practice of Statistics, San Francisco, CA: Freeman, 2012.
  • Required: Mitchell, T. Machine Learning, McGraw-Hill, New York, NY, 1997. P Wes McKinney. Python for Data Analysis, 2nd/3rd Edition, O'Reilly Media,2017/2022.
  • Required: Leskovec, J., Rajaraman A., Ullman, J., Mining of Massive Datasets, 3rd Edition, Cambridge Univ Press Selected research publications and course materials shared by researchers.
Sources