In this course, you will :
- A working knowledge of time series data analysis concepts like stationarity, autocorrelation, and seasonality.
- Working knowledge of Python time series data analysis libraries such as Pandas and NumPy.
- Python experience analysing and forecasting time series data.
- Capability to forecast time series data using statistical modelling approaches such as ARIMA.
- Advanced methodologies for time series data processing, such as machine learning algorithms and neural networks, are required.
- Introduction to Time Series
- Python Basics for Time Series
- Time Series Analysis
- Basic Time Series Forecasting
- Advanced Time Series Forecasting
- Forecast Evaluation
- Time Series Analysis and Forecasting