Data still lives inside relational databases, and every analytics or AI workflow eventually depends on a clean SQL query. This program walks you through practical database fundamentals, business-focused data analysis, and query optimization, using live datasets instead of theory-only lessons. You’ll also see how SQL fits into today’s AI-assisted analytics stack, including natural-language querying, cloud warehouses, and BI dashboards, so the skills you build stay relevant on the job, not just in a classroom.
Prerequisites
- Basic computer literacy and comfort navigating software applications
- No prior programming or coding background required
- A general interest in numbers, logic, or data-driven thinking is helpful
Course Objectives
- Build a strong command of relational database concepts and structure
- Write efficient SELECT, filtering, and sorting queries from scratch
- Apply joins, subqueries, and set operations to solve multi-table problems
- Clean, transform, and validate raw data before analysis
- Use aggregate functions and window functions for business reporting
- Tune slow queries using indexing and execution plan analysis
- Connect SQL output to Python notebooks and BI dashboards
- Understand how AI-assisted SQL and natural-language querying are reshaping analytics work
What You Will Learn
In this program, you will learn essential skills that are required to build strong foundation.
- Database fundamentals: tables, schemas, keys, and data types
- Writing and structuring SELECT, WHERE, and ORDER BY statements
- Grouping and summarizing data with GROUP BY, HAVING, and aggregate functions
- INNER, LEFT, RIGHT, FULL, and SELF joins across multiple tables
- Nested and correlated subqueries, plus Common Table Expressions (CTEs)
- Window functions for running totals, rankings, and trend analysis
- Data cleaning techniques: handling nulls, duplicates, and inconsistent formats
- Query optimization, indexing strategies, and reading execution plans
- Database design basics, normalization, and relationship mapping
- Working with large and semi-structured datasets in cloud data warehouses
- Integrating SQL with Python (Pandas) for extended analysis
- Feeding SQL output into BI tools for dashboards and visual reporting
- Data governance, access control, and security fundamentals
- An introduction to AI-assisted and natural-language SQL querying tools
Who Is This Course For?
This course fits anyone who works with data or wants to, regardless of technical background.
- Freshers and career-changers entering data analytics or data science
- Aspiring data analysts and data scientists
- Business analysts and reporting professionals who rely on data
- Software developers looking to strengthen database skills
- Working professionals aiming to upskill or switch to a data-focused role
Tools and Technologies Covered
- MySQL
- PostgreSQL / SQL Workbench
- Python (Pandas, Jupyter Notebook for SQL integration)
- Cloud data warehouses (Snowflake / BigQuery concepts)
- Power BI / Tableau (conceptual dashboard integration)
- Excel for data validation and cross-checking
- Git basics for version-controlled query scripts
Career Outcomes
Completing this course opens the door to a range of data-focused roles.
- Data Analyst
- Junior Data Scientist
- Business Intelligence (BI) Analyst
- Database Analyst / Query Developer
- Reporting Analyst
- Data Science Trainee / Associate roles in analytics teams
Why Choose kodestree for This Training?
kodestree brings structured, mentor-led learning built around real industry needs.
- Live, instructor-led sessions with practicing data professionals
- Hands-on practice using real, business-style datasets
- Curriculum updated to reflect current SQL and AI-analytics practices
- Flexible batches for working professionals and beginners alike
- Post-course support, doubt-clearing sessions, and access to recordings
- Resume and interview preparation guidance
- Verifiable course completion certificate
