SQL
- SQL fundamentals
- Filtering and aggregation
- Joins
- Data cleaning
- Window functions
- Microsoft SQL Server
- AdventureWorks
- T-SQL
- Database querying
Teaching
Adjunct Instructor, Data Science and Analytics at SAIT.
I teach data analytics and data science at SAIT. Students work with SQL, Python, statistics, and BI on realistic datasets.
Curriculum
Topics I teach
Python
- pandas
- NumPy
- Exploratory data analysis
- Joins and merges
- Data cleaning
- Visualization
- Machine learning
R
- Data analysis
- Quarto
- Association rule mining
- Statistical analysis
- Database connectivity
Machine Learning
- Linear regression
- Multiple regression
- K-nearest neighbours
- K-means
- XGBoost
- Forecasting
- Model evaluation
Business Intelligence
- Power BI
- Tableau
- Looker concepts
- Data visualization
- Dashboard design
- Data storytelling
Data Engineering
- Dimensional modeling
- ELT
- dbt
- Layered analytics architecture
- Data quality
- Warehousing
Teaching
How I teach
I teach SQL, Python, visualization, and machine learning as parts of one workflow. Students see how those tools fit together on a real question.
Classes use realistic datasets, databases, and projects. The goal is that students can investigate a question on their own, not only copy an example.
I have also taught chemistry, physics, and science. I still start with evidence, then method, then interpretation.