Whether it’s quarterly sales data, stock prices, automated inventory trading, or even weather data, time series data is all around us. And working with time series data is a crucial skillset for data practitioners. Ideal for those aiming to refine inventory forecasts, predict market trends, or understand user behaviour, this course offers a direct route to mastering sophisticated time series analysis techniques with Python.

You'll begin the course by learning the foundations, including preprocessing steps for time series data in Python and Pandas, and thinking about time series data as consisting of a trend component, a seasonal component, and observational noise. From there, you'll explore various core modelling and forecasting techniques you can leverage including ARMA, SARIMAX, Exponential smoothing, and the approach taken in the Python library, Prophet.

With a hands-on approach, you'll learn to navigate the complexities of financial markets, supply chain dynamics, consumer behaviour, and product usage trends, enhancing your strategic decision-making and contributing to your organisation's success.