Data Analytics and Business Intelligence

Program Overview

Through practical exercises and case studies, learners gain hands-on experience and develop essential skills to gather, analyze, and interpret data effectively. The course covers topics such as predictive modeling, data visualization, and data management, enabling professionals to make informed data-driven decisions that drive business growth.

Program Outline

Degree Overview:

The objective of this course is to provide delegates with a comprehensive understanding of data analytics techniques and their applications in the field of business intelligence. Through this course, delegates will develop the skills and knowledge required to gather, analyse, and interpret data effectively, enabling them to make informed and data-driven decisions. The course will cover topics such as big data analysis, customer demographics and behaviour, market trends, predictive modelling, dashboarding and reporting techniques, data visualisation tools, and best practices for effective data management and governance. Moreover, a number of BI platforms such as SAS and data analytics technologies such relevant Python libraries will be reviewed and explored. The course will also focus on providing practical experience through hands-on exercises and case studies where participants can apply what they have learned in a real-life scenario. This will help them develop critical thinking and problem-solving skills, which are essential for success in any field. By the end of the course, delegates should be able to apply these skills to solve real-world problems and improve their organisations' performance.

Outline:

The short course consists of five full days and its content is as follows:

• Day 1:
• Introduction to concepts of Business Intelligences & Big Data Analytics
• Overview of SAS cloud-version and SAS coding
• A case study from practice
• Day 2:
• Predictive modelling and descriptive analysis
• Linear regression for linear predictive modelling
• Applications of linear regression in SAS
• Day 3:
• Logistic regression for binary predictive modelling
• Applications of logistic regression in SAS
• A second case study from practice
• Day 4:
• Hierarchical Cluster Analysis (HCA) for clustering and classification predictive modelling
• K-means for clustering and classification predictive modelling
• Applications of HCA and K-means in SAS
• Day 5:
• Reviewing two methods of data collection using Python: web scraping and API

Teaching:

• The course is taught by Dr Farjam Eshraghian, a senior lecturer at Westminster Business School (WBS).
• He has been engaged in teaching data analytics to postgraduate students.
• He has supervised a large number of dissertations focusing on Data Analytics and BI.
• He has delivered a talk on the resources of SAS platforms for academics, researchers and students in its SAS Viya Webinar Series.

Careers:

This short course is for:

• Those who intend to enter the field of data science (Aspiring Data Scientists).
• Professionals who work as data analysts, business analysts and market researchers.
• Managers and decision makers who want to make data-driven decision without having technical knowledge.
• Entrepreneurs and founders who want to leverage data analytics and science for market advantages.
• Professionals in fields such as healthcare, marketing, finance, etc.