Machine Learning: Using Data for Decisions
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Organisations are increasingly making business decisions using (big) data. This course helps the modern-day manager become proficient in using data for decisions, and gain technical mastery of the new paradigm. The course covers quantitative techniques and algorithms that are based on big and small data (numerical and textual) mostly with machine learning techniques. We will also analyse models of big systems for prediction and optimisation that are currently being used widely in business. It introduces topics that are often qualitative, but now amenable to quantitative treatment. The course will prepare participants fora more rigorous analysis of large d ata sets, as well as introduce machine learning models and data analytics for business intelligence.
Focussing on widely used quantitative models, the classes will examine some applications of the same in practice, and then reinforce this learning hands-on. The main goal is to foster an ability to deal with the concepts and mechanics of data analytics and modelling. This is a course of concepts and tools, where various machine learning techniques will be covered. Applications will range across various area of economics and business.
- Business: Where and how analytics plays a role in modern business
- Conceptual: Learn, understand, and be able to apply the ideas and intuition for a range of models that are widely used in business analytics today
- Technical: Be able to use tools for quantitative modelling across business disciplines using mathematics and software, such that one may implement the chosen conceptual framework
- Programming: Participants will be fluent in the R Programming language at the end of the course
- Judgmental: Know when it is best to apply the appropriate conceptual framework and tools to different datand problems
Who should attend
Middle Managers interested in the latest business analytics paradigms, Managers who wish to learn about using data to make business decisions, and data analytics professionals.