Who should attend
The ideal students for this program are individuals who have a strong interest in data analysis for business decision making. Requires a background in intro level statistics; basic programming skills (in any programming language) and the understanding of programming concepts such as variables, functions and loops.
About the course
The amount of data available reached incalculable dimensions with the advancement of the information systems, so it is vital their treatment and analysis to support companies strategic decisions.
This program aims to provide analytics professionals a deeper view and understanding of the concepts and methodologies used in scientific research over large datasets that can be applied and implemented in industry contexts, with direct impact on firms’ performance.
The Business Analytics: Data Science and Big Data program combines theoretical lectures with hands-on sessions. The program is designed to walk participants along the typical phases of a data analytics project, starting with business understanding, followed by data collection and interpreting descriptive statistics, then moving into simple and advanced predictive modeling, to conclude with the design of randomized experiments to try to establish causal effects.
- Understand how to use Big Data Processing Systems (Hadoop, Hive, Spark);
- Understand how to use cloud computing infrastructures, replicable in any business context;
- Implement the latest theories on data analysis, including the predictive and causal deduction methods;
- Use some statistical programming language that allows the company to put into practice the new methods of machine learning and causal modeling (R language);
- Understand how to assess companies data assets value and how to measure the effect of every change.
His research work focuses on how people use technology to consume experience goods and influence others to do so. These are inextricably linked to how firms behave and how public policies affect market structures. Ferreira’s work focuses on the application of robust empirical identification metho...
Miguel Godinho de Matos is Assistant Professor of Information Systems and Management at Católica Lisbon School of Business & Economics. He is also a visiting scholar at the Heinz College from Carnegie Mellon University. Miguel received a Ph.D. in Telecommunications Policy and Management and a...
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Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.