Prediction methodsLaajuus (5 cr)

Objective

You learn the key concepts in predictive modeling and forecasting.

You learn to apply statistical and machine learning techniques for prediction tasks.

You learn to evaluate model performance using appropriate metrics.

You learn to work with structured and time-series data to generate accurate forecasts.

You learn to implement and use prediction algorithms using programming tools (e.g., Python, R) and Excel statistics.

You learn to interpret and communicate prediction results effectively.

Content

What are prediction methods and why are they valuable across industries?
How do predictive models differ from descriptive or diagnostic analytics?
What are the fundamental steps in building a prediction system?
Which statistical techniques are most effective for different prediction tasks?
How do regression models form the foundation of predictive analytics?
When should we use parametric vs. non-parametric statistical models?
How does machine learning expand prediction capabilities beyond traditional statistics?
What techniques handle seasonality and trends most effectively in time-series data?
What metrics truly determine a prediction model's success?
How would you design a prediction solution for a specific business challenge?

Evaluation

Students can
a. use professional vocabulary and concepts in an expert way in situations where analytical skills and technics are required.
e. choose appropriate models, methods, software and techniques according to the purpose and justify these choices.

Qualifications

Basic knowledge of statistics is required.