Posted: September 6th, 2022

BUSI 650: BUSINESS ANALYTICS:
IDEA

BUSI 650: BUSINESS ANALYTICS:
IDEA
The idea of this project familiarizes MBA candidates with the industry and area of expertise. The project allows students to create their own project in any domain to tackle business problems or opportunities in various facets of industry domains as well as fields of interest (Finance, Marketing, Operations, HR, etc.).
EXPECTATIONS
students will be expected to use data to identify the problems or trends and patterns in their area of interest. The process of identifying patterns or trends would be primarily based on the use and analytics of data using various tools and techniques discussed in the class. The candidates should be able to explain the significant changes in their data.
1.0 Introduction (~500)
1.1 Introduction
• Discuss the background of the analysis/research
• Explain the relevance to the industry or company used in the analysis in conjunction with the current business environment
• In a paragraph explain the purpose of the research, the relevance of the problem to the current business environment
1.2 Scope and Objectives (deliverables)
• Scope: Clarify the scope of the analysis or research
• Objectives: clearly state the objective (similar to a research statement)
2.0 Background Research/Literature Review (~800)
2.1 Business
• Use more than peer-reviewed papers or/and reputable media articles/opinions
• The papers/articles/opinions must be associated with the area of analysis/research and should be cohesively analyzed
3.0 Methodology (~700)
3.1 Data
• Discuss the data** collected
• There must be at least 1,000 observations
• Explain how does it relate to the research/Analysis
• Mention the time frames and sources of the data
3.2 Tools/Analytics
• Mention the tools and techniques used to analyze the data, including
o Data Visualization using Tableau (required)
o Clustering/Unsupervised Learning using Excel/R/Python (required)
o Classification/Supervised Learning using Excel/R/Python (optional)
o Neural Networks/Deep Learning using Excel/R/Python (option)
• Explain how it addresses the analysis or research
4.0 Analysis (~2500)
• Align this section with 3.2 and explain how the data was analyzed
• Note changes in tools or methodology (if any)
4.1 Analysis
• Elaborate the results found in 4.1 and explain how it addresses the objectives
• Explain how the results are in conjunction to section 2.0
• Graphs and explanation
5.0 Conclusion (~500)
• Use sections 2, 3, and 4 and provide conclusive remarks
• Provide solution to the problem along with some empirical evidence
GRADING RUBRIC
Key Points Grade allocation
(%)
Format (font type, size, tables, charts) 20
Data Analysis
– Data Visualization using Tableau
– Clustering/UPL using Excel/R/Python
– Classification/SPL 1 using E/R/P
– Classification/SPL 2 using E/R/P 35
15
10
5
5
Team Presentation
– well organized presentation
– impressive demo
– convincing results/solution 25
10
10
5

Creativity in presenting the data 10
APA (in text citations, reference page) ** 10
N.B. Failure to comply with the above would result in low grades.

WORD LIMIT
5,000 words (excluding tables, graphs, bibliography and appendix) (± 10%)
APA style paper and referencing
**No later than 2011 i.e. last 10 years only

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