Data Driven Decision Making
Data-driven decisions perform significantly better according to a study led by the MIT Centre for Digital Business. One barrier to data-driven decision making is the shortage of experienced talent in analytics.
Data modelling and engineering is the role of data scientists or data analysts, business leaders and managers have a critical role to play at the beginning and at end of the process, framing the problems and analysing the results to provide the recommendations for sound decision making. These managers’ equally need to know and understand data science, combining it with their knowledge of the business and the business needs, to address challenges faced by the organisation.
When presented with analytics output, business decision makers commonly have the following questions and uncertainties. It is necessary for the end consumers of data analytics to know how to discern good analytics work from a poor one.
At the end of the course, participants will be able to:
- Ask the right questions to evaluate model goodness and validity
- Interpret common model outcomes and applications
- Understand the benefits and risks of applying particular analytical outcomes
- Understand the difference between various discipline areas such as statistics, machine learning, and operations research
- Understand competencies required of a data driven project
- Do a quick evaluation of a project given the organisation data and analytics maturity level
- Make recommendations for a project to succeed in the above context
What Will Be Covered
- An overview on data and analytics process and life cycle
- Reliability and validity check for data analytics model
- Common model results interpretation and application
- Different schools of modelling techniques
- Strategy for data-driven projects
Who should attend
Any working professional who is interested in using data and analytics to address business issues; to be able to frame the problems and analyse the results of an analytics project to ensure sound decision making.