20 Apr Description Dear Participants, ‘Today many big organizations are sitting on large chunks of data, not knowing what to do with it. They invite consultants & business analysts to have a l
Description
Dear Participants,
"Today many big organizations are sitting on large chunks of data, not knowing what to do with it. They invite consultants & business analysts to have a look at data and come up with insights that could help the organization run their business better. There is no clear set of instructions in such open-ended problems and it is expected of the consultant to do a lot of exploration first and formulate the problems themselves. These DVT projects fall into the bucket of such open-ended problems and a specific problem statement has not been given intentionally. It is expected of students to explore the data and come up with good insights. There is no right and wrong answer here. There should a clear logical story which should come out of their submission."
Please find below DVT Project instructions:
Note: Please upload the project on Tableau public and include the URL in a word doc and upload it.
- Any assignment found copied/ plagiarized with another person will not be graded and marked as zero.
- Please ensure timely submission as a post-deadline assignment will not be accepted.
Please find the instructions here.
To learn how to publish your tableau file, click here.
Once you publish the story to the tableau public, please make sure you hide it on your profile so that it is not visible publicly. Only those who have access to the visualization link will be able to see it. Please follow the instructions from here to know how you can hide your visualizations.
Please use the following datasets- (Car Claim Insurance.xlsx)
Business Context
We are all aware that accidents are prone everywhere due to negligent driving or climatic conditions. An insurance company always needs to be prepared to estimate the number of accidents and the claims that they can receive at a given point time. Also understanding the pattern of claims would help the companies to frame different types of policies for the users providing better benefits and at the same time increasing the premium to the company.
Problem Statement:
Consider that you are a Lead Data Analyst at an Insurance Claims company that has provided you with the Car Insurance Claims dataset. You have been given a task to explore the data, create different plots and interpret useful insights/findings. Your end goal here will be to create a storyboard that you have to present to the Senior Management and the story has to have an end objective and should follow a logical flow to display that you are heading towards achieving the end objective. This will help the Senior Management in taking some decisive actions on the current claims system in place. This storyboard will be an open-ended story for you to explore various different features in the data and try to showcase different plots. Make sure to have minimum clutter in the plots, follow a consistent color scheme across all the plots, and use proper colors to highlight a specific insight. Moreover, your plots on all the dashboards should be interactive and responsive. There should be 1 dashboard that should cover the summary of the story as well as your recommendations.
Important Note: Please reflect on all that you have learned while working on this project. This step is critical in cementing all your concepts and closing the loop. Please write down your thoughts here
Regards,
Program Office
Scoring guide (Rubric) – Rubric for DVT project (1)
CriteriaPoints
Creation of multiple charts and tables for representing useful insights/findings. The charts used should be inline with the objective that you wish to convey to the Senior Management.
[Mandatory 8 types of charts/tables from any of the following: text table, bar chart(multiple/stacked/side by side), bubble chart, treemap, Pareto chart, scatterplot, Wordcloud, line plot, histogram, boxplot, circle views, heatmap, highlighted tables. No restrictions on the upper limit of the number of charts/tables to be used] . **Please note the charts should represent useful insights**4
Creating a calculated field. The calculated field should add some meaningful value and should be inline with your storyboard which you will create in this project. ** Please make sure that you specify wherever calculated field has been used**
. The calculated field should add some meaningful value and should be inline with your storyboard which you will create in this project. ** Please make sure that you specify wherever calculated field has been used**3
Use filters, parameter, actions, etc in the charts so that it helps in understanding the data
4
Minimal clutter and consistency in use of colors across charts.
Avoid cluttering & make consistency in colors throughout5
Multiple Dashboards creation
Create at least 5 Dashboards which deliver some useful insights from business perspective.8
Correct interpretations, insights are expected from each type of chart created. The interpretations should be inline with the storyboard which is to be created in this project.
These interpretations can be in the captions of the storyboard or in the plots as well. **Please make sure the insights shared should be from business perspective & should deliver useful information & are quantifiable8
Interactivity among the charts on each Dashboard.
Each dashboard should be highly interactive8
1 Storyboard Creation
Storyboard is an important part of the project., make sure it shows a sequence of visualizations that work together to convey information.6
Logical flow to the story represented in the storyboard.
Sequence is very important, perhaps it is important to have a logical view (step by step) of the storyboard6
New dashboard which will cover the summary and the recommendations from the insights to be added to the end of the storyboard
This dashboard will be an extra dashboard apart from the mandatory 5 dashboards mentioned in the 5th part of the rubric. At least 5 summary/recommendation points should be mentioned in this dashboard(at least 1 recommendation & summary point from each dashboard you have created). 1 conclusion point of the story. This dashboard has to be a part of the storyboard created and not to be submitted separately. Note: This will not be evaluated if submitted as a separate dashboard/storyboard. Please do not add any plots/visualizations to this dashboard.8
Please reflect on all that you learnt and fill this reflection report – https://forms.gle/GsK23zzBauJPobw68
0
Data
ID | KIDSDRIV | BIRTH | HOMEKIDS | YOJ | INCOME | PARENT1 | HOME_VAL | MSTATUS | GENDER | EDUCATION | OCCUPATION | TRAVTIME | CAR_USE | BLUEBOOK | CAR_TYPE | OLDCLAIM | CLM_FREQ | CLM_AMT | CAR_AGE | URBANICITY |
100130023 | 0 | 1955-02-05 00:00:00 | 2 | 13 | 26,763 | Yes | 141,019 | No | F | Masters | Manager | 49 | Private | 11,500 | SUV | 0 | 0 | 0 | 11 | Highly Urban/ Urban |
100263241 | 0 | 1961-10-05 00:00:00 | 0 | 9 | 156,060 | No | 381,438 | Yes | M | Bachelors | Blue Collar | 29 | Commercial | 25,230 | Panel Truck | 8,207 | 1 | 0 | 9 | Highly Urban/ Urban |
100321982 | 0 | 1959-11-12 00:00:00 | 1 | 11 | 1,965 | Yes | 0 | No | F | PhD | Home Maker | 21 | Private | 20,630 | Minivan | 0 | 0 | 0 | 11 | Highly Urban/ Urban |
100391818 | 0 | 1961-10-11 00:00:00 | 1 | 10 | 62,361 | No | 185,738 | Yes | F | Bachelors | Home Maker | 32 | Private | 11,540 | SUV | 3,481 | 1 | 0 | 6 | Highly Urban/ Urban |
100549277 | 0 | 1966-10-06 00:00:00 | 1 | 10 | 34,192 | Yes | 0 | No | F | High School | Blue Collar | 22 | Commercial | 13,670 | SUV | 0 | 0 | 0 | 7 | Highly Rural/ Rural |
100550672 | 0 | 1958-07-15 00:00:00 | 2 | 7 | 17,755 | Yes | 148,815 | No | F | <High School | Clerical | 29 | Private | 14,480 | Minivan | 0 | 0 | 0 | 1 | Highly Urban/ Urban |
100560602 | 0 | 1965-10-21 00:00:00 | 4 | 16 | 72,641 | No | 230,443 | Yes | M | Bachelors | Clerical | 15 | Private | 4,800 | Pickup | 0 | 0 | 0 | 9 | Highly Urban/ Urban |
100698866 | 0 | 1939-01-10 00:00:00 | 0 | 9 | 100,207 | No | 320,199 | No | M | Bachelors | Manager | 34 | Private | 1,500 | Minivan | 15,403 | 2 | 0 | 11 | Highly Urban/ Urban |
101131398 | 1 | 1936-07-20 00:00:00 | 1 | 2 | 38,111 | No | 152,804 | Yes | F | Masters | Lawyer | 28 | Private | 15,700 | SUV | 0 | 0 | 8,633 | 19 | Highly Urban/ Urban |
101278471 | 0 | 1948-12-22 00:00:00 | 0 | 0 | 0 | No | 0 | No | F | <High School | Home Maker | 33 | Private | 9,490 | SUV | 0 | 0 | 0 | 5 | Highly Rural/ Rural |
101504483 | 0 | 1962-08-03 00:00:00 | 3 | 13 | 39,923 | No | 133,822 | Yes | M | <High School | Blue Collar | 5 | Commercial | 16,530 | Van | 10,546 | 3 | 0 | 1 | Highly Urban/ Urban |
101597061 | 0 | 1957-10-18 00:00:00 | 0 | 8 | 94,591 | No | 309,644 | No | M | <High School | Blue Collar | 21 | Commercial | 17,470 | Van | 16,121 | 3 | 3,428 | 8 | Highly Urban/ Urban |
101619581 | 0 | 1950-02-13 00:00:00 | 0 | 0 | 0 | No | 0 | No | M | <High School | Student | 90 | Private | 22,920 | Minivan | 0 | 0 | 0 | 1 | Highly Rural/ Rural |
102128945 | 0 | 1955-01-13 00:00:00 | 3 | 17 | 49,098 | No | 157,404 | Yes | M | Bachelors | Blue Collar | 33 | Commercial | 5,900 | Pickup | 0 | 0 | 0 | 13 | Highly Rural/ Rural |
102133550 | 0 | 1962-04-11 00:00:00 | 0 | 11 | 33,216 | No | 0 | No | M | High School | Clerical | 52 | Commercial | 30,340 | Panel Truck | 0 | 0 | 0 | 8 | Highly Urban/ Urban |
102270088 | 0 | 1969-11-08 00:00:00 | 1 | 10 | 39,710 | No | 173,759 | Yes | M | High School | Blue Collar | 32 | Commercial | 21,920 | Van | 0 | 0 | 3,853 | 10 | Highly Urban/ Urban |
102280835 | 0 | 1960-06-09 00:00:00 | 0 | 12 | 33,997 | No | 123,850 | Yes | F | <High School | Blue Collar | 8 | Private | 22,520 | Pickup | 0 | 0 | 0 | 6 | Highly Urban/ Urban |
102397935 | 0 | 1966-07-30 00:00:00 | 0 | 8 | 102,981 | No | 302,779 | Yes | M | High School | Professional | 29 | Private | 16,680 | Van | 0 | 0 | 0 | 11 | Highly Rural/ Rural |
102512651 | 0 | 1942-11-10 00:00:00 | 2 | 0 | 0 | No | 82,024 | Yes | F | Bachelors | Home Maker | 14 | Private | 1,500 | SUV | 8,694 | 3 | 3,235 | 5 | Highly Urban/ Urban |
102540356 | 0 | 1959-06-01 00:00:00 | 0 | 0 | 0 | No | 0 | No | F | High School | Student | 53 | Commercial | 22,180 | Sports Car | 0 | 0 | 0 | 1 | Highly Rural/ Rural |
102604051 | 0 | 1952-01-24 00:00:00 | 0 | 5 | 31,957 | No | 156,435 | Yes | M | High School | Clerical | 24 | Private | 9,400 | Minivan | 0 | 0 | 0 | 6 | Highly Urban/ Urban |
102604661 | 0 | 1955-02-06 00:00:00 | 0 | 9 | 124,829 | No | 0 | No | F | Masters | Lawyer | 39 | Private | 23,030 | SUV | 3,441 | 1 | 0 | 16 | Highly Urban/ Urban |
102713514 | 0 | 1963-10-23 00:00:00 | 2 | 8 | 45,034 | Yes | 175,140 | No | M | High School | Manager | 5 | Private | 14,160 | Minivan | 0 | 0 | 0 | 1 | Highly Urban/ Urban |
102962150 | 0 | 1941-11-16 00:00:00 | 0 | 7 | 65,923 | No | 238,115 | Yes | M | Bachelors | Manager | 30 | Commercial | 29,800 | Panel Truck | 3,886 | 2 | 0 | 8 | Highly Urban/ Urban |
102970449 | 0 | 1950-10-14 00:00:00 | 0 | 12 | 106,161 | No | 282,961 | Yes | F | Bachelors | Blue Collar | 31 | Commercial | 18,020 | SUV | 0 | 0 | 5,704 | 16 | Highly Urban/ Urban |
103057855 | 0 | 1958-08-16 00:00:00 | 0 | 13 | 39,055 | No | 173,444 | Yes | M | <High School | Blue Collar | 81 | Commercial | 26,020 | Panel Truck | 1,044 | 2 | 4,212 | 5 | Highly Urban/ Urban |
103069034 | 0 | 1948-01-25 00:00:00 | 0 | 12 | 101,859 | No | 272,958 | Yes | F | PhD | Professional | 48 | Private | 13,390 | Sports Car | 0 | 0 | 4,896 | 17 | Highly Rural/ Rural |
103155388 | 0 | 1967-08-26 00:00:00 | 2 | 13 | 72,188 | No | 229,982 | Yes | F | Bachelors | Professional | 29 | Private | 9,790 | SUV | 3,176 | 2 | 0 | 13 | Highly Urban/ Urban |
103170411 | 0 | 1950-05-08 00:00:00 | 0 | 14 | 21,129 | No | 0 | No | F | Masters | Home Maker | 24 | Private | 8,660 | SUV | 0 | 0 | 0 | 15 | Highly Urban/ Urban |
103299315 | 0 | 1952-03-24 00:00:00 | 0 | 13 | 45,431 | No | 192,184 | Yes | M | <High School | Blue Collar | 45 | Commercial | 16,780 | Van | 3,809 | 2 | 4,430 | 1 | Highly Urban/ Urban |
103490683 | 0 | 1951-07-07 00:00:00 | 0 | 10 | 106,752 | No | 0 | No | M | Bachelors | Professional | 46 | Private | 19,540 | Van | 8,212 | 3 | 3,989 | 15 | Highly Urban/ Urban |
103642796 | 0 | 1947-05-20 00:00:00 | 0 | 13 | 16,005 | No | 52,557 | Yes | M | <High School | Clerical | 38 | Private | 11,570 | Pickup | 3,546 | 1 | 0 | 1 | Highly Urban/ Urban |
103651063 | 0 | 1954-09-06 00:00:00 | 0 | 12 | 107,327 | No | 316,035 | No | F | Bachelors | Professional | 38 | Private | 18,540 | SUV | 0 | 0 | 0 | 9 | Highly Urban/ Urban |
103716673 | 0 | 1961-10-27 00:00:00 | 0 | 14 | 25,835 | No | 109,685 | Yes | M | High School | Clerical | 29 | Private | 8,490 | Pickup | 0 | 0 | 0 | 12 | Highly Urban/ Urban |
103788606 | 0 | 1943-03-09 00:00:00 | 0 | 10 | 55,722 | No | 243,285 | Yes | M | High School | Blue Collar | 21 | Commercial | 23,130 | Panel Truck | 0 | 0 | 0 | 1 | Highly Rural/ Rural |
103790027 | 0 | 1977-07-05 00:
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