SQL Associate Certification

Skills Gained

Core SQL Competencies

Comprehensive SQL skills demonstrated through hands-on exercises, real-world datasets, and practical business scenarios across multiple domains.

📊

Data Extraction & Filtering

Advanced SELECT queries for targeted data insights
Complex filtering with WHERE, LIKE, BETWEEN, IN, IS NULL
Result optimization using ORDER BY and LIMIT
Pattern matching and text search operations

🧮

Aggregation & Grouping

Data summarization with GROUP BY and HAVING clauses
Aggregate functions: SUM(), AVG(), COUNT(), MAX(), MIN()
KPI calculation and performance tracking reports
Multi-level grouping for dimensional analysis

🔗

Working with Multiple Tables

Multi-table joins: INNER, LEFT, RIGHT, FULL OUTER
Table aliases and referencing for complex queries
Cross-functional data merging (sales, inventory, HR)
Relationship management between normalized tables

🔄

Subqueries & CTEs

Subqueries in SELECT, FROM, and WHERE clauses
Common Table Expressions (WITH queries) for clarity
Layered logic construction for complex analysis
Modular query design for maintainability

📐

Window Functions

OVER() clauses with PARTITION BY and ORDER BY
Ranking functions: ROW_NUMBER(), RANK(), DENSE_RANK()
Lead/lag analysis for time-based insights
Moving averages and running totals for trends

🧼

Data Cleaning & Transformation

String manipulation: TRIM, SUBSTRING, CONCAT
Date/time parsing and formatting with DATE_PART
Conditional logic implementation with CASE WHEN
Data standardization and inconsistency resolution

Dataset

LuxurStay Hotels Database Tables

Relational database structure for customer service analysis including branch information, service types, and request performance data

Branch Table

100 Hotel Branches

7 Columns

4 Regions

Loading branch data...

Service Table

4 Service Types

3 Columns

Loading service data...

Request Table

17,682 Service Requests

7 Columns

1-5 Rating Scale

Loading request data...

SQL Analysis

LuxurStay Hotels Analysis

Developed advanced SQL queries to evaluate customer service metrics and uncover areas for operational enhancement

Data Validation & Cleanup

Standardize branch data and handle inconsistencies before analysis. This query ensures data integrity by applying business rules to missing values and standardizing branch information.

SELECT id ,COALESCE(location, 'Unknown') AS location ,CASE WHEN total_rooms BETWEEN 1 AND 400 THEN total_rooms ELSE 100 END AS total_rooms ,CASE WHEN staff_count IS NOT NULL THEN staff_count ELSE total_rooms * 1.5 END AS staff_count ,CASE WHEN opening_date = '-' THEN '2023' WHEN opening_date BETWEEN '2000' AND '2023' THEN opening_date ELSE '2023' END AS opening_date ,CASE WHEN target_guests IS NULL THEN 'Leisure' WHEN target_guests LIKE 'B%' THEN 'Business' ELSE target_guests END AS target_guests FROM Branch;

Sample Results (First 10 Records):

ID	Location	Total Rooms	Staff Count	Opening Date	Target Guests
1	LATAM	168	178	2017	Business
2	APAC	154	82	2010	Leisure
3	APAC	212	467	2003	Leisure
4	APAC	230	387	2023	Business
5	APAC	292	293	2002	Business
6	NA	260	590	2022	Leisure
7	EMEA	259	442	2018	Business
8	NA	259	285	2023	Business

Service Response Time Analysis

Calculate performance metrics to identify service bottlenecks. This query aggregates response times by service and branch to pinpoint areas requiring operational improvements.

SELECT service_id ,branch_id ,ROUND(AVG(time_taken), 2) AS avg_time_taken ,MAX(time_taken) AS max_time_taken FROM Request GROUP BY service_id ,branch_id;

Top 10 Service Performance Results:

Service ID	Branch ID	Avg Time Taken (min)	Max Time Taken (min)
2	46	13.09	16
4	99	9.13	13
1	8	2.56	10
2	13	13.53	17
1	46	2.08	4
3	15	6.73	7
2	35	13.17	16
1	1	2.44	12
3	13	6.80	8

Total of 385 service-branch combinations analyzed for performance metrics.

Regional Service Analysis

Focus on Meal and Laundry services in EMEA and LATAM regions. This multi-table join analysis provides detailed insights into specific service performance in key international markets.

SELECT s.description AS description ,b.id AS id ,b.location AS location ,r.id AS request_id ,r.rating AS rating FROM Service AS s INNER JOIN Request AS r ON s.id = r.service_id INNER JOIN Branch AS b ON r.branch_id = b.id WHERE s.description IN ( 'Meal' ,'Laundry' ) AND b.location IN ( 'EMEA' ,'LATAM' );

Sample Regional Analysis Results:

Service Description	Branch ID	Location	Request ID	Rating
Laundry	63	EMEA	3	4
Laundry	69	LATAM	6	5
Meal	44	EMEA	18	4
Laundry	57	LATAM	19	3
Meal	1	LATAM	21	4
Meal	26	LATAM	26	5
Laundry	34	EMEA	27	4
Laundry	60	LATAM	35	4
Meal	21	EMEA	37	4

Total of 5,047 meal and laundry service records analyzed across EMEA and LATAM regions.

Underperforming Branch Identification

Identify branches with ratings below the 4.5 target threshold. This query uses GROUP BY and HAVING clauses to flag problem areas requiring immediate intervention.

SELECT service_id ,branch_id ,ROUND(AVG(rating), 2) AS avg_rating FROM Request GROUP BY service_id ,branch_id HAVING AVG(rating) < 4.5;

Underperforming Service-Branch Combinations:

Service ID	Branch ID	Average Rating
2	46	3.78
4	99	3.83
1	8	3.64
1	46	3.81
3	15	4.00
2	35	3.76
1	1	3.66
1	57	3.64
1	41	3.77

Total of 215 service-branch combinations identified with ratings below the 4.5 target threshold.

Technical Proficiency

Tools & Professional Impact

Technical environment and real-world application of SQL skills in business contexts.

Technical Tools Used

PostgreSQL - Primary SQL dialect for all exercises
DataCamp Online SQL Editor - Interactive coding environment
Real-world Business Datasets - Retail, HR, marketing scenarios
Database Design Principles - Normalized table structures

Professional Impact

Strengthened ability to extract actionable insights using SQL for data-driven decision making
Gained confidence working with relational databases and normalized data structures
Developed experience preparing clean, stakeholder-ready datasets for business reporting
Positioned for entry-level roles in data analytics, business intelligence, and reporting
Enhanced problem-solving skills through complex query optimization and performance tuning