BUSI 650: Business analytics

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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. Introduction (~500)
   1. Introduction
2. Discuss the background of the analysis/research
3. Explain the relevance to the industry or company used in the analysis in conjunction with the current business environment
4. In a paragraph explain the purpose of the research, the relevance of the problem to the current business environment
   1. Scope and Objectives (deliverables)
5. Scope: Clarify the scope of the analysis or research
6. Objectives: clearly state the objective (similar to a research statement)
7. Background Research/Literature Review (~800)
   1. Business
8. Use more than peer-reviewed papers or/and reputable media articles/opinions
9. The papers/articles/opinions must be associated with the area of analysis/research and should be cohesively analyzed
10. Methodology (~700)
    1. Data
11. Discuss the data** collected
12. There must be at least 1,000 observations
13. Explain how does it relate to the research/Analysis
14. Mention the time frames and sources of the data
    1. Tools/Analytics
15. Mention the tools and techniques used to analyze the data, including
    1. Data Visualization using Tableau (required)
    1. Clustering/Unsupervised Learning using Excel/R/Python (required)
    1. Classification/Supervised Learning using Excel/R/Python (optional)
    1. Neural Networks/Deep Learning using Excel/R/Python (option)
16. Explain how it addresses the analysis or research
17. Analysis (~2500)
18. Align this section with 3.2 and explain how the data was analyzed
19. Note changes in tools or methodology (if any)
    1. Analysis
20. Elaborate the results found in 4.1 and explain how it addresses the objectives
21. Explain how the results are in conjunction to section 2.0
22. Graphs and explanation
23. Conclusion (~500)
24. Use sections 2, 3, and 4 and provide conclusive remarks
25. 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 TableauClustering/UPL using Excel/R/PythonClassification/SPL 1 using E/R/PClassification/SPL 2 using E/R/P	35 15 10   5   5
Team Presentation well organized presentationimpressive democonvincing 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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