Training on SQL for Data Manipulation and Processing

Structured Query Language (SQL) is a foundational tool for managing, processing, and analyzing data in modern organizations. This course equips participants with practical skills to manipulate and extract insights from databases efficiently, ensuring data integrity and supporting informed decision-making.

The training covers essential SQL concepts, including database design principles, table creation, data insertion, and querying. Participants will learn advanced techniques for filtering, joining, aggregating, and transforming datasets, as well as performing data cleaning and validation tasks.

Through hands-on exercises, participants will gain experience in writing complex queries, automating workflows, and generating reports that communicate meaningful insights. Emphasis is placed on best practices for data management, ensuring participants can handle data accurately and efficiently across various business and research environments.

By the end of the course, participants will be proficient in SQL for data manipulation, analysis, and reporting, enabling them to streamline processes, improve data-driven decision-making, and support operational and strategic objectives.

Duration

5 Days

Who Should Attend

• Data analysts and business intelligence professionals
• Database administrators and developers
• IT and data management officers
• Monitoring and Evaluation (M&E) and research professionals managing project data
• Anyone interested in mastering SQL for analytics or reporting

Organizational Impact

• Improves efficiency in data management and reporting processes.

• Enhances accuracy and consistency in organizational data systems.

• Builds internal capacity for data-driven decision-making.

Individual Impact

• Strengthens technical proficiency in SQL and relational databases.

• Enhances analytical and problem-solving skills.

• Equips participants with tools to automate and streamline data workflows.

By the end of the course, participants will be able to:

• Understand the structure and logic of relational databases.
• Write SQL queries for data extraction, transformation, and analysis.
• Manage large datasets efficiently through joins, subqueries, and indexing.
• Apply data cleaning and validation techniques for high-quality reporting.
• Create automated reports and dashboards from SQL queries.

Module 1: Introduction to Databases and SQL Fundamentals

• Overview of relational database systems
• Database concepts: tables, keys, and relationships
• Writing basic SQL queries (SELECT, FROM, WHERE)
• Case Study: Extracting employee data from a corporate HR database

Module 2: Data Retrieval, Filtering, and Aggregation

• Using aggregate functions: SUM, AVG, COUNT, MAX, MIN
• Grouping data with GROUP BY and filtering with HAVING
• Sorting and conditional statements (CASE, IF)
• Case Study: Summarizing monthly sales and regional performance data

Module 3: Working with Multiple Tables and Joins

• Types of joins: INNER, LEFT, RIGHT, and FULL OUTER
• Combining data across different schemas
• Managing many-to-many relationships
• Case Study: Integrating customer and transaction datasets for analysis

Module 4: Subqueries, CTEs, and Views

• Using subqueries in SELECT, FROM, and WHERE clauses
• Common Table Expressions (CTEs) for complex queries
• Creating and managing database views
• Case Study: Building reusable SQL views for project dashboards

Module 5: Data Manipulation and Cleaning

• Inserting, updating, and deleting records
• Handling duplicates, null values, and inconsistencies
• Normalization and denormalization of tables
• Case Study: Cleaning and standardizing program monitoring data

Module 6: Advanced SQL Functions and Data Transformation

• String, date, and numeric manipulation functions
• Using window functions (RANK, ROW_NUMBER, LAG, LEAD)
• Derived columns and calculated fields
• Case Study: Creating time-based reports using sales trend data

Module 7: Query Optimization and Performance Tuning

• Understanding query execution plans
• Indexing strategies for performance
• Reducing redundancy and improving speed
• Case Study: Optimizing a slow-running financial report query

Module 8: Automating Data Workflows and Stored Procedures

• Creating and executing stored procedures
• Automating data imports, updates, and exports
• Error handling and scheduling scripts
• Case Study: Automating monthly data consolidation for management reports

Module 9: SQL for Data Analytics and Reporting

• Building analytical queries for BI dashboards
• Integrating SQL with Power BI, Excel, or Python
• Creating report-ready datasets
• Case Study: Generating KPI-based performance dashboards from SQL queries

Module 10: Data Security, Governance, and Final Project

• User permissions and access control
• Data integrity and compliance best practices
• Final project: End-to-end data processing and reporting exercise
• Case Study: Implementing a secure SQL data pipeline for an organization