Chat with us, powered by LiveChat 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 | Wridemy

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

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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