The importance of using evidence based decision making processes has continued to become more popular in a knowledge-driven and information-centric business environment. Using micro, macro and meta data to engage in meta-trend analysis is an arsenal for institutions that are focused on surviving the next wave – whether that wave is social, economic, political or a mix of these. Mastering such skills require some appreciation of how to engage in business research in the first instance. This in turn calls for an appreciation of critical business areas that call for research. The second phase entails the collection and analysis of data to distill business insights. This course aims at strengthening business research and data analytics capacity amongst the participants.
WHO SHOULD ATTEND?
Policy makers (directors, assistant directors), economists, and human resources managers. Those in charge of operations, researchers, Procurement specialists, heads of corporate planning and strategy chief officers and those undertaking postgraduate studies will also benefit.
HOW PARTICIPANTS WILL BENEFIT
At the end of the course, participants will be able to:-
• Learn what data to collect, how to analyze the data and how to interpret it to inform decision and policy making process;
• Use appropriate secondary data to solve business problems;
• Describe the scientific method of research and be able to differentiate between various research methods;
• Structure business and policy research proposals;
• Carry out research process with regard to framing the problem statement, research questions and objectives as well as data collection;
• Analyze and interpret data and use the results to inform decision making and policy;
• Explain the complex nature of business research and data analytics;
• Use statistical software and search engines for predictive analytics, data mining and analytics.
• Business research and data analytics concept;
• Business research framework;
• Framing the business research problem;
• Business research questions and objectives;
• Investigating theoretical and industry practices around the research issue;
• Data mining and search engines;
• Micro, macro and meta data collection;
• Micro, macro and meta data analysis;
• Data analytics;
• Data to decision (DTD) approaches;
• Predictive analytics.