B.E. (Computer Engineering) Data Warehousing and Mining Syllabus - Mumbai University 2026
The University has moved this degree onto NEP 2020 one year at a time. The first and second years are NEP 2020 syllabi; the third and fourth years are still examined on the REV-2019 'C' Scheme, which is what the University sets for them this year.
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Syllabus for Data Warehousing and Mining
Module 1: Data Warehousing Fundamentals
- Introduction to Data Warehouse, Data warehouse architecture, Data warehouse versus Data Marts, E-R Modeling versus Dimensional Modeling, Information Package Diagram, Data Warehouse Schemas; Star Schema, Snowflake Schema, Factless Fact Table, Fact Constellation Schema. Update to the dimension tables. Major steps in ETL process, OLTP versus OLAP, OLAP operations: Slice, Dice, Rollup, Drilldown and Pivot.
Module 2: Introduction to Data Mining, Data Exploration and Data Pre-processing
- Data Mining Task Primitives, Architecture, KDD process, Issues in Data Mining, Applications of Data Mining, Data Exploration: Types of Attributes, Statistical Description of Data, Data Visualization, Data Preprocessing: Descriptive data summarization, Cleaning, Integration & transformation, Data reduction, Data Discretization and Concept hierarchy generation.
Module 3: Classification
- Basic Concepts, Decision Tree Induction, Naïve Bayesian Classification, Accuracy and Error measures, Evaluating the Accuracy of a Classifier: Holdout & Random Subsampling, Cross Validation, Bootstrap.
Module 4: Clustering
- Types of data in Cluster analysis, Partitioning Methods (k-Means, k-Medoids), Hierarchical Methods (Agglomerative, Divisive).
Module 5: Mining frequent patterns and associations
- Market Basket Analysis, Frequent Item sets, Closed Item sets, and Association Rule, Frequent Pattern Mining, Apriori Algorithm, Association Rule Generation, Improving the Efficiency of Apriori, Mining Frequent Itemsets without candidate generation, Introduction to Mining Multilevel Association Rules and Mining Multidimensional Association Rules.
Module 6: Web Mining
- Introduction, Web Content Mining: Crawlers, Harvest System, Virtual Web View, Personalization, Web Structure Mining: Page Rank, Clever, Web Usage Mining.
Useful Links
- 1 Paulraj Ponniah, “ Data Warehousing: Fundamentals for IT Professionals”, Wiley India.
- 2 Han, Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann 2nd edition.
- 3 M.H. Dunham, “Data Mining Introductory and Advanced Topics” , Pearson Education.
- 1 Reema Theraja, “Data warehousing”, Oxford University Press 2009.
- 2 Pang-Ning Tan, Michael Steinbach and Vipin Kumar, “Introduction to Data Mining”, Pearson Publisher 2nd edition.
- 3 Ian H. Witten, Eibe Frank and Mark A. Hall, “Data Mining”, Morgan Kaufmann 3rd edition.
- 1 https://onlinecourses.nptel.ac.in/noc20_cs12/preview
- 2 https://www.coursera.org/specializations/data-mining
Reproduced from the University of Mumbai syllabus for B.E. (Computer Engineering) under REV-2019 'C' Scheme, in force from the academic year 2021-22. Wording is as printed in that syllabus. Module numbering is as printed there too.
The complete syllabus
This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.