Advanced SQL for Data Scientists
4.5 /5- Offered byLinkedin Learning
Advanced SQL for Data Scientists at Linkedin Learning Overview
Duration | 3 hours |
Mode of learning | Online |
Difficulty level | Advanced |
Credential | Certificate |
Advanced SQL for Data Scientists at Linkedin Learning Highlights
- Earn a sharable certificate
- 1 exercise file
- 5 quizzes
- Access on tablet and phone
Advanced SQL for Data Scientists at Linkedin Learning Course details
- This course provides a more sophisticated approach to designing data models and optimizing queries in SQL
- Instructor Dan Sullivan begins with the logical and physical design of tables?with particular focus on very large databases?and then presents a deep dive review of indexes, including specialized indexes and when to use them
- The next section introduces query optimization and shows how to optimize basic, multi-join, and more complex queries
- The course also covers SQL extensions, including user-defined functions and specialized data types
Advanced SQL for Data Scientists at Linkedin Learning Curriculum
Introduction
Advanced SQL techniques for data science
What you should know
Data Modeling: Tables
Rules of normalization
Denormalization
Partitioning data
Materialized views
Read replicas
Challenge: Design a data model for analytics
Solution: Design a data model for analytics
Data Modeling: Indexes
B-tree indexes
Bitmap indexes
Hash indexes
GiST and SP-GiST indexes
GIN and BRIN indexes
Challenge: Choosing an optimal indexing strategy
Solution: Choosing an optimal indexing strategy
Query Optimization
EXPLAIN and ANALYZE commands
Generating data with generate_sequence
Generating time series data
Analyzing a query with WHERE clauses and indexes
Analyzing a query with a join
Challenge: Optimize a query using an explain plan
Solution: Optimize a query using an explain plan
User-Defined Functions
Extending SQL with user-defined functions
SQL query functions
Function overloading
Function volatility
PL/Python functions
Challenge: Write a user-defined function
Solution: Write a user-defined function
Special-Purpose Functionality
Federated queries
Bloom filters
Hstore for key-value pairs
JSON for semi-structured data
Hierarchical data and ltrees
Challenge: Design a table to support unstructured data
Solution: Design a table to support unstructured data
Conclusion
Next steps
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