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SQL for Data Analytics
Move beyond SQL basics and learn how analysts use SQL across the full data workflow.
3 connected parts · 3 capstone projects · KPI & dashboard analysis · data engineering · machine learning preparation · Certificate from the Canadian Institute of Artificial Intelligence (CiAI).
Original price was: USD$50.00.USD$15.99Current price is: USD$15.99. /month
Self-paced online · Designed for learners who have completed SQL Basics and SQL Beginner, or have equivalent foundational SQL skills.
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Analyze Business Performance
Build KPI, segmentation, cohort, rolling metric, and dashboard-ready SQL.
Prepare Reliable Data
Use ETL/ELT logic, incremental loads, deduplication, SCD concepts and quality checks.
Prepare Data for ML
Create features, labels, aggregation pipelines and model-ready tables with SQL.
3 Applied Capstone Projects
Complete one capstone after each course part to apply analytics, engineering, and machine-learning preparation skills.
From SQL Queries to Real Data Work
This course assumes you already understand the foundations of SQL. The focus now shifts from learning commands to using SQL as an analytical tool across the same workflow used in data analytics, business intelligence, data engineering, and machine learning preparation.
Why take this after SQL Basics and SQL Beginner?
SQL Basics and SQL Beginner teach you how to retrieve, filter, group, and combine data. SQL Data Analytics shows you what to do with those skills at work: measure performance, build reporting outputs, prepare reliable datasets, and shape data for predictive modeling.
customersprofiles · signup · source
ordersrevenue · status · dates
order_itemsproducts · quantity
eventsbehaviour · timestamps
What you will learn
- Calculate KPIs such as revenue, active customers and average order value
- Segment customers and business activity with GROUP BY and CASE
- Use RANK, LAG and LEAD for analytical comparisons
- Build rolling averages and cohort analyses
- Create dashboard-ready SQL outputs
- Apply ETL/ELT and incremental-load thinking
- Deduplicate records with ROW_NUMBER
- Understand SCD Type 1 and Type 2 concepts
- Validate data quality before analysis
- Create features and labels for machine learning
- Build clean, model-ready datasets
Who this course is for
- Learners who completed SQL Basics and SQL Beginner
- Aspiring data analysts and BI analysts
- Business professionals who need stronger analytical SQL
- Learners preparing for data engineering or machine learning workflows
- Non-technical and social-science professionals moving into data-focused work
Three Connected Parts — One Data Workflow
The course mirrors how data moves through a real organization: first you analyze it, then you prepare and govern it, then you shape it for machine learning.
Turn raw business records into metrics, trends and dashboard-ready analytical outputs.
- KPI queries
- Data segmentation & group-based analysis
- Window functions: RANK, LAG, LEAD
- Rolling averages
- Cohort analysis
- Dashboard-ready queries
Prepare and maintain clean data so downstream reports, dashboards and models can be trusted.
- ETL and ELT patterns
- Incremental loads
- Deduplication with ROW_NUMBER
- Slowly Changing Dimensions
- SQL in pipelines: Airflow/dbt examples
- Data validation & quality checks
Use SQL to convert operational data into features, labels and structured datasets that support predictive modeling.
- Feature engineering
- Time-series transformations
- Aggregation pipelines
- Label creation
- Train-test dataset preparation
- Feature selection & model-ready tables
See the Data Workflow End to End
1. AnalyzeKPIs, cohorts, segmentation, trends and dashboard queries.
→
2. PrepareClean, deduplicate, validate and transform data for reliable use.
→
3. Model-ReadyCreate features, labels and one-row-per-observation datasets for ML.
Part 1 · Business Intelligence
Example: Rank customers by total spending
This query combines aggregation with a window function so analysts can summarize customer value and rank customers without losing the grouped result.
SELECT
customer_id,
SUM(amount) AS total_spent,
RANK() OVER (ORDER BY SUM(amount) DESC) AS spend_rank
FROM orders
WHERE order_status = 'Completed'
GROUP BY customer_id;
-- One analytical result: total spend + customer rank
The course then carries the same data forward into engineering and machine learning tasks instead of treating each SQL concept as an isolated command.
Recommended SQL Learning Path After This Course
Assuming you have completed SQL Basics and SQL Beginner, the following courses are recommended to continue building stronger SQL skills and move toward more advanced, career-focused applications.
Next 1
SQL Joins
Deepen relational analysis, reconciliation, missing-match detection and multi-table reporting.
Explore SQL Joins →Next 2
SQL Intermediate
Add CTEs, subqueries, advanced filtering, NULL-safe logic and SQL-based data cleaning.
Explore SQL Intermediate →Next 3
SQL Advance
Move into performance, advanced set logic, window functions, recursive CTEs and BI-oriented SQL.
Explore SQL Advance →Next 4
SQL AI-Assisted
Use AI to generate, inspect, debug and verify SQL while keeping human judgment in control.
Explore SQL AI-Assisted →Course Details
Format100% self-paced, online
LevelPost-beginner / applied SQL
Structure3 connected parts
Core datasetcustomers, orders, order_items, events
PracticeHands-on SQL labs
AI TutorIncluded — course-specific guidance
Capstone Projects3 — one after each course part
CertificateIssued by the Canadian Institute of Artificial Intelligence (CiAI)
Recommended preparationSQL Basics + SQL Beginner
Frequently Asked Questions
It is not intended to be the learner's first SQL course. This page assumes learners have already completed SQL Basics and SQL Beginner, or have equivalent foundational skills with SELECT, WHERE, GROUP BY, CASE and JOINs.
Part 1 focuses on SQL for Data Analysis and Business Intelligence: KPI queries, segmentation, RANK/LAG/LEAD, rolling averages, cohort analysis and dashboard-ready queries.
Part 2 focuses on SQL for Data Engineering: ETL/ELT, incremental loads, deduplication, slowly changing dimensions, pipeline examples with Airflow/dbt and data-quality validation.
Part 3 focuses on SQL for Machine Learning: feature engineering, time-series transformations, aggregation pipelines, label creation, train-test dataset preparation and model-ready tables.
The course uses four core tables throughout the workflow: customers, orders, order_items and events.
A strong sequence is SQL Joins for relational depth, SQL Intermediate for advanced query logic and data cleaning, SQL Advance for higher-level analytical SQL, and SQL AI-Assisted for responsible AI-supported query development. You can then choose a domain specialization from the CodeCore catalog.
Yes. The course assistant can explain KPIs, window functions, cohorts, ETL/ELT, deduplication, SCDs, data quality, feature engineering, labels and model-ready tables.
The course includes 3 capstone projects — one after each of the three course parts.
The certificate of completion is issued by the Canadian Institute of Artificial Intelligence (CiAI).
Meet the Creator of the Course
Learn from an educator and machine learning engineer working across analytics, AI, data science and applied business problems.
Dr. Mark Lokanan
Professor · Senior Machine Learning Engineer · Data Scientist · AI Specialist
Dr. Mark Lokanan is a professor at Royal Roads University and a Senior Machine Learning Engineer at Vedia. His work spans data science, machine learning, artificial intelligence, analytics, financial crime, and applied research. Drawing on both academic and industry experience, he designed SQL Data Analytics to help learners connect SQL with the full data workflow — business analysis, data engineering, and machine learning preparation — so they can move from writing queries to solving practical data problems.
SQL Data Analytics Assistant
Ask me about the 3 course parts, 3 capstone projects, KPIs, window functions, rolling averages, cohorts, ETL/ELT, incremental loads, deduplication, SCDs, data quality, feature engineering, labels, model-ready tables, prerequisites, the CiAI certificate, or recommended next courses.