Python Regression Accelerator: 1 Day Workshop in Edmonton

Group Discounts:

• Save 10% when registering 3 or more participants
• Save 15% when registering 10 or more participants

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About This Course

• Duration: 1 Full Day (8 Hours)
• Delivery Mode: Classroom (In-Person)
• Language: English
• Credits: 8 PDUs / Training Hours
• Certification: Course Completion Certificate
• Refreshments: Lunch, snacks, and beverages included

Course Overview

This 1 Day intensive program introduces you to the essential concepts of regression analysis using Python. You will explore simple and multiple linear regression, diagnostics, transformations, qualitative predictors, collinearity, and real-world modelling challenges.
The course focuses on practical understanding, helping you build, interpret, evaluate, and troubleshoot regression models efficiently.
Using a blend of theory, examples, and structured activities, you will learn how to apply regression to real datasets, assess model validity, and improve model performance in various scenarios.

Learning Objectives

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

1. Understand and apply simple and multiple linear regression.
2. Interpret regression output and evaluate model performance.
3. Diagnose model problems using residual analysis and assumptions.
4. Use indicator variables and interactions effectively.
5. Apply transformations to improve model fit and reliability.
6. Identify and manage collinearity issues.
7. Build regression models using Python with confidence.

Target Audience

This course is ideal for:

• Data science beginners with basic Python knowledge
• Students and professionals exploring statistical modelling
• Business analysts and researchers working with data
• Anyone looking to strengthen analytical and regression skills

Why Choose This Course?

This program simplifies complex regression topics into a clear, practical 1-day format.
The trainer brings real-world data science experience, ensuring examples and explanations remain practical, relevant, and easy to understand.
You gain structured exposure to essential and intermediate-level regression concepts without being overwhelmed by advanced mathematics.

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Want to train your entire team?
We provide customized in-house sessions tailored to your industry datasets, modelling goals, and team skill levels.
The content can be adapted to finance, marketing, healthcare, operations, or research environments.
Ideal for teams aiming to improve analytics capability in one focused day.

📧 Contact us today to schedule a customized in-house, face-to-face session:

Info: • Understand what regression analysis is and why it is used.

• Explore publicly available datasets for modelling.

• Learn key steps in performing regression analysis.

• Icebreaker

Info: • Learn covariance, correlation, and basic model structure.

• Explore parameter estimation and hypothesis testing.

• Understand predictions and evaluating model fit.

• Case Study

Info: • Learn interpretation of regression coefficients.

• Understand centring, scaling, and estimator properties.

• Explore inference and prediction in multiple regression.

• Simulation

Info: • Understand standard regression assumptions.

• Learn residual types, normality, linearity, and influence.

• Identify outliers and assess model violations.

• Role Play

Info: • Learn how to include categorical predictors in regression.

• Understand interaction variables and seasonal indicators.

• Explore multi-equation regression systems.

• Case Study

Info: • Learn how to include categorical predictors in regression.

• Understand interaction variables and seasonal indicators.

• Explore multi-equation regression systems.

• Case Study

Info: • Apply transformations for linearity and variance stabilization.

• Detect heteroscedasticity and apply weighted least squares.

• Use log and power transformations to enhance model accuracy.

• Activity

Info: • Understand autocorrelation and its impact on regression models.

• Learn the Durbin–Watson statistic and its limitations.

• Explore transformations and indicator variables to correct correlation.

• Simulation

Info: • Understand principal components and constrained estimation.

• Explore principal component regression and biased coefficients.

• Learn ridge regression basics and model stabilization strategies.

• Group Discussion

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