Undergraduate Certificate in Statistics for Time Series
-- ViewingNowThe Undergraduate Certificate in Statistics for Time Series is a compact course designed to equip learners with essential skills in time series analysis, a highly sought-after skill in various industries. This course covers key statistical concepts, time series modeling, forecasting, and data visualization using industry-standard tools like R and Python.
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โข Time Series Basics: An introduction to time series analysis, including concepts such as stationarity, seasonality, and trend.
โข Data Exploration and Visualization: Techniques for exploring and visualizing time series data, including autocorrelation and partial autocorrelation plots, seasonal plots, and time series decomposition.
โข Classical Decomposition Models: An in-depth look at classical decomposition models, such as additive and multiplicative models, and their applications.
โข Autoregressive (AR) and Moving Average (MA) Models: The theory and practice of AR and MA models, including model identification, estimation, and diagnostic checking.
โข Autoregressive Integrated Moving Average (ARIMA) Models: The development and application of ARIMA models, including model identification, estimation, and diagnostic checking.
โข Seasonal ARIMA (SARIMA) Models: An examination of SARIMA models, including model identification, estimation, and diagnostic checking.
โข State Space Models: An introduction to state space models, including the Kalman filter and smoother, and their applications.
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