Advanced Sql And Nosql For Data Science

This section covers advanced SQL techniques and introduces NoSQL databases for effective data management in data science.

Advanced Sql Concepts

This section covers advanced SQL techniques such as subqueries, common table expressions, window functions, pivoting, and advanced joins.

Subqueries And Nested Queries

Subqueries are queries nested within other queries, enhancing SQL's ability to perform complex data manipulations and filtering.

Common Table Expressions (Ctes)

Common Table Expressions (CTEs) improve SQL query readability and allow recursion.

Window Functions

Window functions allow for calculations across a set of table rows that are related to the current row, enhancing analytical capabilities in SQL.

Pivoting And Unpivoting Data

This section introduces pivoting and unpivoting data techniques in SQL, allowing for the transformation of row data into column data and vice versa.

Advanced Joins And Set Operations

This section covers advanced SQL techniques such as various types of joins and set operations to address complex data queries.

Sql Optimization Techniques

This section covers key SQL optimization techniques that enhance database performance.

Indexing

Indexing is a technique used to enhance data retrieval performance in databases by creating and managing different types of indexes.

Query Execution Plan Analysis

This section covers the use of query execution plans to identify performance bottlenecks in SQL queries, highlighting tools like EXPLAIN and EXPLAIN ANALYZE.

Materialized Views

Materialized views store the results of database queries for faster access.

Partitioning And Sharding

Partitioning and Sharding are techniques used to enhance database performance by facilitating efficient data distribution across systems.

Introduction To Nosql Databases

NoSQL databases provide flexible data models and scalability for unstructured and semi-structured data, diversifying options for data storage and retrieval beyond traditional relational databases.

Why Nosql?

NoSQL databases provide flexibility and scalability, making them ideal for handling unstructured and semi-structured data.

Document Databases

This section introduces document databases, emphasizing their structure and usage in handling semi-structured data.

Key-Value Stores

Key-value stores are the simplest NoSQL database structures, known for high performance and low latency.

Column-Family Stores

Column-family stores are a type of NoSQL database optimized for large-scale data writing and retrieval, using rows with variable columns grouped into families.

Graph Databases

Graph databases utilize structured graph data models to efficiently represent and query relationships.

Working With Mongodb For Data Science

This section covers the core functionalities of MongoDB including CRUD operations, aggregation pipelines, indexing, and geospatial and text search.

Crud Operations

This section introduces the fundamental CRUD operations in MongoDB, which consist of create, read, update, and delete functions essential for data manipulation.

Aggregation Pipeline

The aggregation pipeline in MongoDB facilitates processing and transforming data similar to SQL's GROUP BY operation.

Indexing In Mongodb

Indexing in MongoDB significantly improves the read performance of query operations.

Geospatial And Text Search

This section introduces how geospatial and text search functionalities can enhance data retrieval in MongoDB.

Choosing Between Sql And Nosql

This section discusses the characteristics and use cases of SQL and NoSQL databases to help data scientists make informed decisions about their data storage choices.

Using Sql And Nosql Together

This section discusses the advantages of using both SQL and NoSQL databases in conjunction, emphasizing how data scientists can leverage the strengths of each for improved data processing.