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Statistics & Analysis
(5/5)

Using the data provided, analyse the premium prices charged to customers.

INSTRUCTIONS TO CANDIDATES
ANSWER ALL QUESTIONS

Background Info: 

The insurance industry is driven by the simple fact that the capital spent by the insurance company in response to beneficiary claims should not exceed customer premium. The higher the difference between the approved customer claims and total premium received, equates to higher profits. Generally, the more data we have on a customer, the better we are able to understand their needs, as well as assess the risks involved in insuring them. 

Task: Using the data provided, analyse the premium prices charged to customers. 

Some helper questions: 

● Who is charged a higher premium?

 ● Who is charged a lower premium? 

● Who are the main customers? You may choose to supplement the existing data with additional data sources where applicable.

For your CAPSTONE project you should,

● Prepare a 12-15page slide deck in response to the case study and show how you address them through your data analytics techniques. 

● Your slide deck should cover the following: 

1. Define your business objective or problem statement and identify relevant metrics 

2. Identify clearly the sources of data and steps taken extract the data from the sources 

3. Collect, consolidate and organise the data 

4. Exploratory data analysis to ensure the data integrity and data quality is sufficient to conduct analysis 

5. Working Tableau Dashboard with a minimum of 4 different chart types that help address the problem statement 

6. Show proper data analysis to approach the chosen case study 

7. Using the dashboard, demonstrate how you can derive data driven decisions 

● Design a dashboard that can visualize your data. The working TABLEAU dashboard should show at least two different types of charts and a TOTAL OF 4 CHARTS (line charts, maps, bar charts, etc.) and identify 2 recommendations or 2 insights based on your defined problem statement. Focus on the organization of the dashboard, the use of colours, the labelling of the axes, to ensure it looks professional. Dashboard link and screenshots needs to be pasted/attached in the presentation. Descriptions/write-ups need to be accompanied with the charts. 

 

Capstone Project, PowerPoint Presentation Requirements

 

Each student to submit 1 slide deck with Minimum of 12-15 slides.

1. Data Extraction needs to be done in Python.

2. Data Cleaning and Exploratory Data Analysis needs to be done in Python.

3. Data Visualization needs to be done in Tableau(Dashboard). 

 

Presentation Template- Suggested flow of presentation

Case Study X

Introduction to chosen case study, and define your problem statement clearly

See if you can very succinctly: (suggestions)

Summarize their value proposition

Explain the business problem

Data 

Identify clearly the sources of data and steps taken extract the data from the sources

Steps taken to collect and consolidate the data

Methods taken to organise the data to use it for analysis

 

Exploratory Data Analysis (1/2)

What steps did you take in cleaning the data?

How was it done?

List all the data cleaning steps taken to ensure the data integrity and data quality is sufficient to conduct analysis

Exploratory Data Analysis (2/2)

What are the initial insights on the data?

Which are the relevant fields to approach the problem statement

 

 

 

 

 

Dashboard

Insert Dashboard Link here

Design a dashboard that can visualize your data. The dashboard should show at least four different charts (line charts, maps, bar charts, etc.) and identify 2 recommendations or 2 insights based on your defined problem statement. Focus on the organization of the dashboard, the use of colors, the labeling of the axes, to ensure it looks professional. 

 

Strategy: Key Recommendations

Discuss in a bit more detail what you would recommend based on your insights

Use data to support your recommendations

 

Conclusion

Forecast the impact of your recommendations.

Any other considerations when executing these recommendations? What resources might you need?

If recommendations work well, any next steps?

Individual reflections: what have you learnt? What were the main challenges in applying data analytics skills to this project?

 

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