This course aims to provide you with a robust understanding of forecasting and predictive analytics. You will gain the knowledge and skills necessary to apply these techniques in real-world scenarios, while identifying the challenges of changing processes and supporting stakeholders during transitions. You’ll also be guided on the methods of estimating future business outputs based on historical data and unpack ways that businesses can use forecasting techniques to gain insight into future demand. You will discover how to use time-series forecasting to better anticipate future trends. You will develop a holistic approach to using predictive analytics in business, considering the different stages of a data analytics pipeline, and learn how to use predictive models to support strategic decision-making. The factors for successful deployment of a predictive model, data quality, governance, and stakeholder buy-in will also be analyzed.
This course aims to provide you with a robust understanding of forecasting and predictive analytics. You will gain the knowledge and skills necessary to apply these techniques in real-world scenarios, while identifying the challenges of changing processes and supporting stakeholders during transitions. You’ll also be guided on the methods of estimating future business outputs based on historical data and unpack ways that businesses can use forecasting techniques to gain insight into future demand. You will discover how to use time-series forecasting to better anticipate future trends. You will develop a holistic approach to using predictive analytics in business, considering the different stages of a data analytics pipeline, and learn how to use predictive models to support strategic decision-making. The factors for successful deployment of a predictive model, data quality, governance, and stakeholder buy-in will also be analyzed.
Evaluate projected demands to optimize supply chain operations.
Choose appropriate forecasting techniques based on business objectives.
Identify key considerations when adding new predictive elements to a workflow, such as data quality, data integration, and data governance.
Apply change management strategies to effectively deploy data-driven processes in organizations.
Evaluate the practical applications, strengths, and limitations of predictive analytics.
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