*2026 - March - Intermediate Data Analytics on Microsoft Excel

Course Description

In today’s data-driven landscape, businesses and organizations rely heavily on data analytics to inform strategic, operational, and financial decisions. Excel, as a robust and widely adopted analytics tool, empowers users to extract value from data, uncover trends, and generate actionable insights. This intermediate-level course is meticulously designed to equip learners with the practical skills necessary to perform advanced data analytics using Microsoft (MS) Excel, enhancing their ability to support data-driven decision-making and effectively communicate insights.

Learners will gain hands-on experience with advanced tools and techniques, including connecting to external data sources, cleaning and transforming data using Power Query, building relational data models with Power Pivot, and applying statistical and predictive methods such as regression and hypothesis testing. In addition, participants will explore prescriptive analytics using What-If Analysis, Goal Seek, and Solver, as well as perform basic sentiment analysis through Azure Machine Learning Excel add-in. Real-world datasets and scenarios are used throughout to provide practical context and deepen understanding. Spanning six comprehensive modules, this course enables learners to develop a holistic, hands-on command of Excel as a data analytics application and prepares them to deliver impactful business insights.

Learning Outcomes

On successful completion of this course, learners will be able to:

1. Retrieve and structure data from diverse external sources for analysis in Excel.

2. Clean, transform and prepare datasets for analysis using both built-in tools and M scripting in Power Query

3. Build efficient data models for large datasets.

4. Use DAX (Data Analysis Expressions) for enhanced data models.

5. Apply basic forecasting and regression techniques to predict trends and outcomes using historical data.

6. Use Excel to recommend decision options through scenario modeling, goal seeking, and constraint-based optimization.

7. Conduct hypothesis testing to support data-driven decision-making and validate assumptions using statistical methods.

Course Format:

• Duration: 5 weeks (25 hours)
• Schedule: 2 sessions per week (Tuesdays and Thursdays, 6-8:30pm)
• Delivery: Fully online
• Time Commitment: Approx. 5 hours/week + optional assignments

The course includes assignments, in-class discussions, and a capstone project to help you apply what you’ve learned in a real-world context.

Who Should Apply:

This course is ideal for:

• Jobseekers looking to build in-demand analytical skills
• Newcomers interested in data analytics
• Small business owners and solopreneurs
• Anyone looking to strengthen their analytical skills

Eligibility:

• 18+
• Living in Canada
• Comfortable using a computer with a stable internet connection
• English language level CLB 6 or higher

Topics Covered Include:

Module 1: Connecting to External Sources

Module 2: Data Munging with Power Query

Module 3: Data Modelling with Power Pivot

Module 4: Predictive Analytics

Evaluation Modalities

1. Individual in-class assignment

2. Weekly Quizzes

3. Home assignments

📝 Registration is now open. Space is limited—sign up today to reserve your spot!