Series of lectures of my course An Introduction into Relational Databases

Course is based on next Books and my working experience for Production Systems

  • An Introduction to Database Systems (by C. J. Date)
  • Database Systems: The Complete Book (by Jeffrey D. Ullman, Hector Garcia-Molina, Jennifer Widom)
  • SQL and Relational Theory. How to write Accurate SQL Code (by C.J.Date)
  • SQL Fundamental course. Educational and practical guide (by E.P. Morgunov)
  • Readings in Database Systems (by Peter Bailis, Joseph M. Hellerstein, Michael Stonebraker)

Series of lectures of my course Data Warehouse Management Systems

  • Lecture #1 (Types of Data)

Course is based on the next Books and my working experience for Production Systems

  • DAMA-DMBOKData Management Body of Knowledge (by Deborah Henderson, Susan Earley, Laura Sebastian-Coleman)
  • Data Mesh.Delivering Data-Driven Value at Scale (by Zhamak Dehghani)
  • Data Model Scorecard (by Steve Hoberman)
  • Turning TEXT into GOLDTaxonomies and Textual Analytics (by Bill Inmon)
  • Building a Data Warehouse (by Vincent Rainardi)
  • Data Architecture: A primer for the Data Scientist (by W.H.Inmon, Daniel Linstedt)
  • Building a scalable Data Warehouse with Data Vault 2.0 (by Daniel Linstedt, Michael Olschimke)
  • Data Lake for Enterprises (by Thomas Benjamin)
  • Data Engineering Teams (by Jesse Anderson)
  • Building The Data Warehouse (by Bill Inmon)
  • The Data Warehouse Toolkit (by Ralph Kimball, Margy Ross)

Series of lectures of my course Advanced Relational Databases (PostgreSQL)

  • Lecture #1 (Heap Table)
  • Lecture #2 (Database Versioning and Benchmarks)
  • Lecture #3 (Database Schemas and Tablespaces)
  • Lecture #4 (Other Types of Database Tables)
  • Lecture #5 (VACUUM)
  • Lecture #6 (Tool and techniques)
  • Lecture #7 (Dynamic SQL and Triggers)
  • Lecture #8 (OLAP techniques)
  • Lecture #9 (Cluster Structure and Security)
  • Lecture #10 (Indexes)
  • Lecture #11 (Database Statistics / EXPLAIN)
  • Lecture #12 (Indexes. Part II)
  • Lecture #13 (Transactions and Isolation Levels)
  • Lecture #14 (Procedural languages)
  • Lecture #15 (Memory Structures)
  • Lecture #16 (Replication)

Course is based on next Books and my working experience for Production Systems

  • PostgreSQL 9.6 High Performance (by Ibrar Ahmed, Gregory Smith)
  • Understanding EXPLAIN (by Andy Withers)
  • PostgreSQL 11 Administration Cookbook (by Simon Riggs, Gianni Cioli, Sudheer Kumar Meesala)

Series of lectures of my course Introduction into Data Mining

  • Lecture #1 (Intro)
  • Lecture #2 (Data Preparation)
  • Lecture #3 (Similarity and Distances)
  • Lecture #4 (Link Analysis)
  • Lecture #5 (Advertising on the Web)
  • Lecture #6 (Frequent Itemsets)
  • Lecture #7 (Mining Data Streams)
  • Lecture #8 (Clustering)

Course is based on next Books and my working experience

  • Data Mining (by Charu C. Aggarwal)
  • Outlier Analysis (by Charu C. Aggarwal)
  • Social Network Data Analysis (by Charu C. Aggarwal)
  • Mining of Massive Datasets (by Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman)
  • Google’s PageRank and Beyond: The Science of Search Engine Rankings (by Amy N. Langville, Carl D. Meyer)
  • Data Mining Concepts and Techniques (by Jiawei Han, Micheline Kamber, Jian Pei)