This course is designed for data scientists and analysts who wish to harness the power of PostgreSQL in their data science projects. It covers PostgreSQL’s data storage, retrieval, and analysis capabilities, emphasizing statistical functions, data aggregation, and integration with popular data science tools. Through practical examples and hands-on exercises, participants will learn how to efficiently use PostgreSQL for data exploration, analysis, and predictive modeling.

Target Audience:

• Data Scientists and Analysts
• Researchers in need of robust data management and analysis tools
• IT professionals transitioning to data science roles

Prerequisites:

• Basic understanding of SQL and relational database concepts
• Familiarity with data analysis techniques and principles
• Experience with a programming language (Python/R) is beneficial

Course Duration:

• Total Duration: 30 Hours (3 days, full-time)
• Mode of Delivery: Online / In-Person / Part-Time

Module 1: Introduction to PostgreSQL for Data Science

• Overview of PostgreSQL features relevant to data science
• Setting up a PostgreSQL database environment for data analysis

Module 2: Advanced Data Types and Operations

• Working with various data types for data science (arrays, JSONB, geometric, and custom types)
• Advanced operations for data manipulation and analysis

Module 3: Data Import, Export, and Manipulation

• Efficient data import and export techniques with PostgreSQL
• Data cleaning and preprocessing using SQL queries

Module 4: Exploratory Data Analysis (EDA) with PostgreSQL

• Utilizing SQL for data summarization, aggregation, and visualization preparation
• Advanced statistical functions available in PostgreSQL

Module 5: Advanced Analytical Techniques

• Using Window Functions for complex analytical tasks
• Implementing Common Table Expressions (CTEs) for recursive and complex queries

Module 6: Integration with Data Science Tools

• Connecting PostgreSQL with Python/R for data analysis
• Using PostgreSQL as a data source for machine learning models

Module 7: Performance Tuning for Data Analysis

• Indexing strategies for analytical queries
• Optimizing query performance for large datasets

Module 8: Predictive Modeling with PostgreSQL

• Introduction to PL/Python and PL/R for in-database analytics
• Building and deploying simple predictive models within PostgreSQL

Module 9: Visualization and Reporting

• Generating insights from data using PostgreSQL
• Tools and techniques for visualizing query results

Course Conclusion:

• Recap of key concepts and techniques
• Best practices for using PostgreSQL in data science projects
• Resources for further learning and development

Learning Outcomes:

Participants will gain practical skills in using PostgreSQL for data science, including data manipulation, analysis, and modeling. They will learn how to integrate PostgreSQL with Python and R for advanced analytics and how to optimize PostgreSQL's performance for data science applications. This course will enable them to leverage PostgreSQL's full potential in their data science projects, from exploratory data analysis to predictive modeling.

This course is designed to be hands-on, with a blend of instructional content, practical exercises, and real-world case studies to ensure participants can effectively apply their learning in their data science roles.