Series of lectures of my course “An Introduction into Relational Databases“
- Lecture #1 (Introduction into RDBMS)
- Lecture #2 (ER diagrams and Data Analytics)
- Lecture #3 (Relational Model)
- Lecture #4 (ER diagrams)
- Lecture #5 (Data Access)
- Lecture #6 (Database Design)
- Lecture #7 (Relational Algebra)
- Lecture #8 (Transactions)
- Lecture #9 (SQL)
- Lecture #10 (Optimization)
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-DMBOK. Data 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 GOLD. Taxonomies 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)