Courses
MySQL for Data Analyst
Our MySQL Course is designed to help you master SQL for data analysis, reporting, and data preparation. You will learn how to query, clean, and transform large datasets, apply business logic, and prepare data for BI tools and ML models.
Available Slots
This course is upcoming. Enrollment will open soon.
Course Description
Understand SQL fundamentals with practical business examples.
Write efficient queries to clean, filter, and aggregate data.
Apply subqueries, joins, and advanced SQL for real analytics.
Work with Window Functions, CTEs, and Stored Procedures.
Prepare data for visualization tools like Power BI, Tableau.
Work on projects that simulate real-world Data Analyst tasks.
Course Duration & Topics
Understanding RDBMS, SQL vs NoSQL for analysts Installing MySQL (Workbench + CLI) Database objects: Tables, Views, Schemas Basic SQL syntax & data types
SELECT statements & Aliases Filtering: WHERE, BETWEEN, LIKE, IN, NULL Sorting & limiting results Aggregate functions (COUNT, SUM, AVG, MIN, MAX) GROUP BY & HAVING with business examples
INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN Self Joins for hierarchical data Handling NULLs in joins Relational constraints: Primary Key, Foreign Key
Subqueries (nested, correlated) Common Table Expressions (CTEs) Window Functions (ROW_NUMBER, RANK, DENSE_RANK, NTILE) Advanced aggregations with PARTITION BY Analytical queries for trend, cohort, and ranking analysis
Dealing with missing & duplicate data String functions (TRIM, SUBSTRING, REPLACE, CONCAT) Date & time functions for reporting (DATEDIFF, DATE_ADD, FORMAT) Conditional logic (CASE, IF, COALESCE) Derived columns for business metrics
Normalization & denormalization for analytics Indexing strategies for query performance Query execution plan analysis Transactions & ACID concepts (from Analyst perspective)
Building KPI reports (Sales, Finance, Marketing) Creating Views & Stored Procedures for reporting Using MySQL with Excel, Power BI & Tableau Exploratory Data Analysis (EDA) in SQL Case studies: Customer segmentation, product performance, churn analysis
Common Table Expressions (CTE) Recursive CTEs Views for reusable queries Role in simplifying analyst workflows
Normalization & denormalization for analytics Indexing strategies for query performance Query execution plan analysis Transactions & ACID concepts (from Analyst perspective)
Who Can Join?
- Freshers who want to enter IT/Analytics field or transition from Non-IT to IT
- Students preparing for job opportunities
- Professionals planning a career transition
- Anyone interested in Data Analytics