Category: Data Analytics

This person is responsible for creating visuals in

Tableau that are specific to their case. We will expect the visuals to be professional and
more sophisticated than just basic bar graphs; the specifics of this are outlined in the
Tableau trainings. This person should have completed and scored well on the 3 Tableau
discussions and be interested in data visualization.
This person is required to first watch the Group Project – Tableau Tutorial (Pts. 1
and 2) that is included in the Data Visualization Specialist training section on D2L
AND the Tableau Level-Up workshops (Pts 1 and 2) linked in the same folder.
These can be completed while you wait for the Data Analyst to clean the data and
share it with you.

Make sure the Tableau is hyperlinked in the document as shown in the trainings (not just the URL)
Either in the report, on the Tableau, or both, you need 2 things:
o Explanation of what the visuals are and how to use them
o Some example insights from the visuals
Make sure to include the visuals asked for in the case!
Some variety of types of visuals explored
General professionalism: avoid typos, consistent font, pieces lined up, no overlapping pieces, text that
fits (not ending in …), not a ton of white space, no generic titles, etc.
Make sure to include some of the advanced features shown in the trainings: actions, buttons, sets,
dynamic titles, filters that affect multiple visuals, visuals inside a tooltip, advanced formatting, etc.

o You dont need all of these, but at least a few of them to get full pointsData Exploration using Tableau
This section is unique and is primarily the responsibility of the Data Visualization Specialist.
First, the data visualizations should be created using Tableau with input from the rest of the
team and with special collaboration with the Data Analyst. Once you are happy with it, it
should be posted to one of the group members Tableau Public profiles.
o Note, based on the limited time constraints of the project, what is observed and
found in the visual does NOT have to be the same as the Data Analyst. You can make
two separate recommendations overall with one being from the visuals and the
other being from the advanced regression model.
o Make sure to review the best practices we have at the end of each Tableau
discussion assignment.
Once you have complete visualizations, please include a brief description with any key
takeaways/insights within the report and then hyperlink to your final visual which only
needs to be included on one members Tableau Public profile.
The visuals should be relatively complex and utilize features and techniques discussed in the
training(s). Basic bar graphs that dont include any extra functionality will not earn you
many points.

previous tableau examples:

Requirements: 4 dashboards | .doc file

This person is responsible for creating visuals in

Tableau that are specific to their case. We will expect the visuals to be professional and
more sophisticated than just basic bar graphs; the specifics of this are outlined in the
Tableau trainings. This person should have completed and scored well on the 3 Tableau
discussions and be interested in data visualization.
This person is required to first watch the Group Project – Tableau Tutorial (Pts. 1
and 2) that is included in the Data Visualization Specialist training section on D2L
AND the Tableau Level-Up workshops (Pts 1 and 2) linked in the same folder.
These can be completed while you wait for the Data Analyst to clean the data and
share it with you.

Make sure the Tableau is hyperlinked in the document as shown in the trainings (not just the URL)
Either in the report, on the Tableau, or both, you need 2 things:
o Explanation of what the visuals are and how to use them
o Some example insights from the visuals
Make sure to include the visuals asked for in the case!
Some variety of types of visuals explored
General professionalism: avoid typos, consistent font, pieces lined up, no overlapping pieces, text that
fits (not ending in …), not a ton of white space, no generic titles, etc.
Make sure to include some of the advanced features shown in the trainings: actions, buttons, sets,
dynamic titles, filters that affect multiple visuals, visuals inside a tooltip, advanced formatting, etc.

o You dont need all of these, but at least a few of them to get full pointsData Exploration using Tableau
This section is unique and is primarily the responsibility of the Data Visualization Specialist.
First, the data visualizations should be created using Tableau with input from the rest of the
team and with special collaboration with the Data Analyst. Once you are happy with it, it
should be posted to one of the group members Tableau Public profiles.
o Note, based on the limited time constraints of the project, what is observed and
found in the visual does NOT have to be the same as the Data Analyst. You can make
two separate recommendations overall with one being from the visuals and the
other being from the advanced regression model.
o Make sure to review the best practices we have at the end of each Tableau
discussion assignment.
Once you have complete visualizations, please include a brief description with any key
takeaways/insights within the report and then hyperlink to your final visual which only
needs to be included on one members Tableau Public profile.
The visuals should be relatively complex and utilize features and techniques discussed in the
training(s). Basic bar graphs that dont include any extra functionality will not earn you
many points.

previous tableau examples:

Requirements: 4 dashboards | .doc file

This person is responsible for creating visuals in
Tableau that are specific to their case. We will expect the visuals to be professional and
more sophisticated than just basic bar graphs; the specifics of this are outlined in the
Tableau trainings. This person should have completed and scored well on the 3 Tableau
discussions and be interested in data visualization.
This person is required to first watch the Group Project – Tableau Tutorial (Pts. 1
and 2) that is included in the Data Visualization Specialist training section on D2L
AND the Tableau Level-Up workshops (Pts 1 and 2) linked in the same folder.
These can be completed while you wait for the Data Analyst to clean the data and
share it with you.

Make sure the Tableau is hyperlinked in the document as shown in the trainings (not just the URL)
Either in the report, on the Tableau, or both, you need 2 things:
o Explanation of what the visuals are and how to use them
o Some example insights from the visuals
Make sure to include the visuals asked for in the case!
Some variety of types of visuals explored
General professionalism: avoid typos, consistent font, pieces lined up, no overlapping pieces, text that
fits (not ending in …), not a ton of white space, no generic titles, etc.
Make sure to include some of the advanced features shown in the trainings: actions, buttons, sets,
dynamic titles, filters that affect multiple visuals, visuals inside a tooltip, advanced formatting, etc.

o You dont need all of these, but at least a few of them to get full pointsData Exploration using Tableau
This section is unique and is primarily the responsibility of the Data Visualization Specialist.
First, the data visualizations should be created using Tableau with input from the rest of the
team and with special collaboration with the Data Analyst. Once you are happy with it, it
should be posted to one of the group members Tableau Public profiles.
o Note, based on the limited time constraints of the project, what is observed and
found in the visual does NOT have to be the same as the Data Analyst. You can make
two separate recommendations overall with one being from the visuals and the
other being from the advanced regression model.
o Make sure to review the best practices we have at the end of each Tableau
discussion assignment.
Once you have complete visualizations, please include a brief description with any key
takeaways/insights within the report and then hyperlink to your final visual which only
needs to be included on one members Tableau Public profile.
The visuals should be relatively complex and utilize features and techniques discussed in the
training(s). Basic bar graphs that dont include any extra functionality will not earn you
many points.

previous tableau examples:

This person is responsible for creating visuals in
Tableau that are specific to their case. We will expect the visuals to be professional and
more sophisticated than just basic bar graphs; the specifics of this are outlined in the
Tableau trainings. This person should have completed and scored well on the 3 Tableau
discussions and be interested in data visualization.
This person is required to first watch the Group Project – Tableau Tutorial (Pts. 1
and 2) that is included in the Data Visualization Specialist training section on D2L
AND the Tableau Level-Up workshops (Pts 1 and 2) linked in the same folder.
These can be completed while you wait for the Data Analyst to clean the data and
share it with you.

Make sure the Tableau is hyperlinked in the document as shown in the trainings (not just the URL)
Either in the report, on the Tableau, or both, you need 2 things:
o Explanation of what the visuals are and how to use them
o Some example insights from the visuals
Make sure to include the visuals asked for in the case!
Some variety of types of visuals explored
General professionalism: avoid typos, consistent font, pieces lined up, no overlapping pieces, text that
fits (not ending in …), not a ton of white space, no generic titles, etc.
Make sure to include some of the advanced features shown in the trainings: actions, buttons, sets,
dynamic titles, filters that affect multiple visuals, visuals inside a tooltip, advanced formatting, etc.
o You dont need all of these, but at least a few of them to get full points

Data Exploration using Tableau
o You should create one or multiple exploratory dashboard(s) in Tableau that visualizes
your chosen dependent variable across categories such as card type, income category,
and sex, and various numeric variables; note, for this case, because of the nature of the
binary dependent variable, you should display it as a percentage for each category. You
should create several different types of visuals that leverage the dynamic nature of
Tableau for exploratory analysis, incorporating some of the advanced features shown in
the trainings.

here are the trainings that show the tableau visualizations they want to see. please do not use AI unless it is for the recommendations andvisualization explanation

• A completely free, self-paced Data Analyst Bootcamp is available, structured as a YouTube playlist covering essential skills like SQL, Excel, Tableau, Power BI, and Python.
• The bootcamp includes practical projects for each skill (e.g., data exploration, data cleaning, and portfolio projects) to build hands-on experience and a portfolio.
• A free certification of completion is provided via GitHub upon finishing the bootcamp, operating on an honor system without formal verification.
• The curriculum guides learners on creating a free portfolio website and offers resources for crafting a data analyst resume and job-hunting strategies using LinkedIn.
• The creator emphasizes that foundational data analytics education should be accessible, having organized existing free tutorials into a structured learning path to help learners gain skills and land jobs.

see attached assignment and the template thats needed to be used.

its an AI project. Details are all in files

Kindly review my answers, correct any errors, and develop a well-structured report based on the cases, enhancing the quality of my work

need to do changes for my analysis in spss as given in revision file.

We have already did the code part, please do the report part for us. What we need a beautiful,academic, industrual report.

Goal: Predict airline passenger satisfaction to identify service quality gaps and support operational improvements in staffing, process design, and resource allocation.

You can download the data through this llink in kaggle.

Expectations:

• Data Preparation: Clean and preprocess the dataset, addressing missing values, outliers, and categorical encodings such as gender, customer type, and travel class.
• Exploratory Data Analysis (EDA): Examine satisfaction levels across flight types, service touchpoints such as boarding efficiency, seat comfort, and inflight service, and passenger demographics. Identify which operational factors most strongly drive dissatisfaction. Conduct any additional EDA relevant to the modeling process and final conclusions.
• Modeling: Build and evaluate classification models to predict whether a passenger is satisfied or neutral/dissatisfied. Compare model performance using appropriate evaluation metrics and interpret key contributing features.
• Insights and Recommendations: Provide 23 operationally grounded recommendations: such as prioritizing service improvements in high-dissatisfaction touchpoints, adjusting staffing or training protocols for underperforming service areas, or identifying passenger segments that disproportionately experience service failures.