• Unit 10: Time Series Analysis

    The last step in this introduction to data science requires us to deal with data derived from time series, such as stock prices as a function of time. All the tools from the earlier units will play a role in performing these analyses. As in the last unit, our goal is to build statistical models that allow for inference and prediction.

    This unit introduces models for analyzing time-series data. The statsmodels module contains various analysis tools, including methods for handling stationary and nonstationary data. This unit will focus on constructing autoregressive, moving average, and autoregressive integrated moving average models. This unit will teach you how to implement Python programs capable of statistical inference and forecasting from time-series data.

    Completing this unit should take you approximately 6 hours.

    • 10.1: The statsmodels Module

    • 10.2: Autoregressive (AR) Models

    • 10.3: Moving Average (MA) Models

    • 10.4: Autoregressive Integrated Moving Average (ARIMA) Models

    • Unit 10 Assessment

      • Receive a grade