In this course, students will learn about the forecasting models available in Power BI. By understanding how time series exponential smoothing works, students will be able to manipulate the forecast line efficiently for daily, monthly, and yearly predictions of univariate data.

As part of the course, students will gain hands-on experience in advanced error handling techniques in Power Query and be able to tune parameters efficiently for cyclical and seasonal datasets.

What you’ll learn

• Visualise time series data in Power BI

• Apply and manipulate time series exponential smoothing forecast

• Transform unstructured data into time series data

• Understand time series theory, and the concepts of seasonal and cyclical data

• Handle time series forecasting errors using advanced techniques in Power Query

• Compare actual values versus forecast values

Are there any course requirements or prerequisites?

• A prior, basic understanding and usage of Power BI is recommended

• Prior experience using Power Query or writing M scripts is useful but not required

• Interest in stock trading is appreciated but not required

• Have Power BI desktop and Microsoft Excel installed

Who this course is for:

• Business analysts interested in time series analysis

• Finance professionals curious about Power BI and trend analysis

• Python developers curious about Power BI and trend analysis

• Power BI and Excel users interested in trend analysis

• Business professionals curious about Power BI, forecasting, and time series analysis