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Updated in [July 25th, 2023]
This course is designed to teach students about time series analysis and forecasting using Power BI. Students will learn about the forecasting models available in Power BI and how to use time series exponential smoothing to manipulate the forecast line for daily, monthly, and yearly predictions of univariate data. Additionally, 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.
By the end of the course, students will be able to visualize time series data in Power BI, apply and manipulate time series exponential smoothing forecasts, 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, and compare actual values versus forecast values.
This course is recommended 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, and business professionals curious about Power BI, forecasting, and time series analysis. Prior experience using Power Query or writing M scripts is useful but not required. Interest in stock trading is appreciated but not required. It is necessary to have Power BI desktop and Microsoft Excel installed.
Course Syllabus
Introduction to Time Series Exponential Smoothing
Time Series Forecasting and Advanced Error Handling