UGBS @ 60 - THE FUTURE OF BUSINESS EDUCATION IN GHANA: REFLECTING ON THE 60-YEAR EXPERIENCE OF THE UNIVERSITY OF GHANA BUSINESS SCHOOL

Data Analytics and Machine Learning Applications to Business: An OMIS And Finance Department Joint Seminar

Data Analytics and Machine Learning Applications to Business: An OMIS And Finance Department Joint Seminar
Aug 02, 2022

On 19th July, 2022 the Department of Operations and Management Information Systems (OMIS) collaborated with the Department of Finance for a two-session seminar on the theme: "Data Analytics and Machine Learning Applications to Business". The seminar was organised for postgraduate and undergraduate students at the UGBS Graduate Campus and R.S. Amegashie Auditorium respectively.  The sessions were moderated by Dr. Emmanuel Kolog Awuni, a Senior Lecturer at the Department of OMIS, and had in attendance Prof. Elikplimi Agbloyor, Department of Finance, Prof. John Effah, Department of OMIS, and the guest speaker Prof. James Malm, Associate Professor of Finance, College of Charleston School of Business, South Carolina, United States of America.

The Guest Speaker, Prof. James Malm took the students through an understanding of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL) and Data Science. He further mentioned how machine learning is applied to industries such as finance, accounting, health, marketing, and supply chain management, and gave a few examples. In the field of supply chain management, machine learning is used in anomaly detection, demand forecasting, delivery prediction, route optimization and for marketing, it is used for predictive targeting, prediction lead scoring, customer lifetime value, churn prediction, etc. The accounting and finance industries also use machine learning for asset pricing, auditing, risk management, tax planning, fraud detection, etc.

Prof. Malm further explained the different types of machine learning, which are supervised learning, unsupervised learning and reinforcement learning. He noted Python and R as some tools used for machine learning. Prof. Malm also took the students through a practical model development process to predict whether a given client will default or not using data about credit card clients.

In conclusion, Prof. Malm mentioned machine learning engineers, data scientists, natural language scientists and business intelligence developers as a few of the machine learning career paths. He explained what these career paths entail and advised students to choose the one they can excel in.

 

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