SQL

Proficiency Level: Intermediate

As a final-year BCom Actuarial Science student, I have developed a working knowledge of SQL through academic work, projects, and self-study. I use SQL to query, clean, and explore datasets, and I continue to build skills in database design and integration.

Core SQL Skills

Querying:

• Writing SELECT statements with JOINs and filtering conditions
• Using aggregate functions with GROUP BY and HAVING
• Constructing subqueries and Common Table Expressions (CTEs)
• Applying window functions for simple ranking and cumulative calculations
• Using CASE statements for conditional logic

Data Manipulation:

• Filtering, sorting, and transforming datasets
• Basic pivoting using CASE and aggregation
• String functions and date/time manipulation

Databases Used

PostgreSQL and MySQL:

• Writing queries on structured insurance or financial datasets
• Exploring use of indexes, primary keys, and constraints
• Working with JSON fields and basic stored procedures

SQLite:

• Used as a lightweight solution for prototyping and small-scale projects
• Integrated with Python for exploratory data analysis

Database Design

Schema Design Fundamentals:

• Understanding normalization principles
• Designing tables with appropriate data types
• Using foreign keys to enforce relationships

Project Work

Claims Data Analysis

• Queried mock claims data using window functions
• Performed cohort analysis and summary reporting

Data Cleaning and Transformation

• Wrote scripts to clean inconsistent or missing values
• Used joins and filters to prepare datasets for modeling

Performance and Optimization

• Exposure to EXPLAIN plans for understanding query performance
• Practice with writing efficient joins and filtering
• Understanding the impact of indexes and constraints

Integration and Tools

Used With:

• Python (sqlite3, psycopg2, SQLAlchemy) for data pipelines
• R (DBI, RPostgres) for connecting to databases during analysis
• SQL tools like DBeaver and pgAdmin for query writing and exploration

Ongoing Learning

I’m actively developing my SQL proficiency by:

• Practicing more advanced queries and performance tuning
• Learning ETL patterns and data pipeline design
• Studying schema design principles and data warehousing
• Reviewing open-source SQL projects and tutorials

SQL has become an essential part of my data toolkit, helping me manage, explore, and prepare data efficiently for analysis and modeling tasks in academic and personal projects.