Data Analytics Assignment · Tableau

Titanic Survival
Analysis

Explore the Titanic passenger dataset to uncover survival patterns, demographic differences, class-based outcomes, and the relationship between passenger characteristics and survival.

Intermediate Estimated time: 2–3 hours Software: Tableau Dataset: 891 records

Explore the assignment →

01 · Assignment overview

Understand the story behind the numbers

The Titanic dataset provides passenger-level information about demographics, ticket class, fares, embarkation ports, and survival. Your objective is to transform this raw data into meaningful insights using Tableau.

Explore the Data

Understand the dataset, identify missing values, examine field types, and prepare the data for analysis.

Calculate KPIs

Measure passenger distribution and survival rates across gender, passenger class, and other dimensions.

Build Visualizations

Create charts and a geographic map to communicate patterns, comparisons, and important findings.

Business scenario: You are a data analyst studying the Titanic passenger manifest. Your task is to investigate which passenger groups were more likely to survive and present your findings through an interactive Tableau dashboard.

02 · Dataset explorer

Get familiar with your dataset

Download the dataset, review its structure, and understand what each column represents before starting the analysis.

891

Total passenger records

Total columns

38.38%

Overall survival rate

Embarkation ports

↓ Download Titanic Dataset

Data Dictionary

Use this reference to understand the available fields.

Column	Description	Data Type
PassengerId	Unique passenger identifier	Integer
Survived	Survival indicator: 0 = No, 1 = Yes	Integer
Pclass	Ticket class: 1 = First, 2 = Second, 3 = Third	Integer
Name	Passenger's full name	String
Sex	Passenger gender as recorded in the dataset	String
Age	Passenger age in years	Decimal
SibSp	Number of siblings or spouses aboard	Integer
Parch	Number of parents or children aboard	Integer
Ticket	Ticket identification number	String
Fare	Passenger fare paid	Decimal
Cabin	Cabin identification	String
Embarked	Port of embarkation: C, Q, or S	String

Sample Records

Search the sample records to explore the dataset.

Passenger ID	Name	Sex	Age	Class	Fare	Survived	Embarked
1	Braund, Mr. Owen Harris	male	22	3	7.25	No	S
2	Cumings, Mrs. John Bradley	female	38	1	71.2833	Yes	C
3	Heikkinen, Miss. Laina	female	26	3	7.925	Yes	S
4	Futrelle, Mrs. Jacques Heath	female	35	1	53.1	Yes	S
5	Allen, Mr. William Henry	male	35	3	8.05	No	S
6	Moran, Mr. James	male	Missing	3	8.4583	No	Q
7	McCarthy, Mr. Timothy J	male	54	1	51.8625	No	S
8	Palsson, Master. Gosta	male	2	3	21.075	No	S

Important data-quality observations
The dataset contains 177 missing Age values, 687 missing Cabin values, and 2 missing Embarked values. Decide how to handle missing values before using these fields in your visualizations. For the cabin analysis, classify passengers into "Cabin Available" and "Cabin Missing" rather than deleting records.

Embarkation port reference: C = Cherbourg, Q = Queenstown (present-day Cobh), S = Southampton. Use these locations when creating your map visualization.

03 · Guided challenges

KPI Requirements

Create the following calculated measures and use them to understand the passenger population and survival outcomes.

01 Basic

Total Passengers

Determine the total number of passengers in the Titanic dataset to understand the overall size of the population.

COUNT(PassengerId)

Count the unique PassengerId values. Ensure each passenger is counted only once.

02 Basic

Survival Rate

Calculate the percentage of passengers who survived relative to the total number of passengers.

AVG(Survived) × 100

Since Survived contains 1 for survivors and 0 for non-survivors, its average gives the survival proportion. Format the result as a percentage.

03 Basic

Total Passengers by Gender

Identify the number of male and female passengers to understand the gender distribution.

COUNT(PassengerId) Sex

Place Sex as a dimension and count PassengerId as the measure. Compare the male and female passenger counts.

04 Intermediate

Survival Rate by Gender

Calculate and compare male and female survival rates to identify gender-based differences.

AVG(Survived) Sex

Group the average of Survived by Sex. Format the measure as a percentage and compare the two groups.

05 Intermediate

Survival Rate by Class

Determine the survival rate for each ticket class and assess whether passenger class is associated with survival.

AVG(Survived) Pclass

Group the average survival indicator by Pclass. Label the categories First Class, Second Class, and Third Class.

Tableau tip: Create calculated fields where necessary. For a binary survival field containing 0 and 1, AVG(Survived) is a convenient way to calculate the survival percentage.

04 · Visualization challenges

Chart Requirements

Build the following visualizations in Tableau. Select appropriate dimensions, measures, sorting, labels, and tooltips to make each chart easy to interpret.

01 Pie / Donut Chart

Total Passengers by Embarkation Port

Show the distribution of passengers by embarkation port to understand where passengers boarded the Titanic.

Embarked COUNT(PassengerId)

Use Embarked as the category and passenger count as the measure. Show the port name, passenger count, and percentage of total. Handle the two missing port values appropriately.

02 Bar Chart

Top 10 Most Expensive Tickets

Display the ten highest individual passenger fares to highlight differences in ticket prices.

Name Fare

Sort Fare in descending order and apply a Top 10 filter. Use passenger name as the category and Fare as the measure. Consider including PassengerId in the tooltip.

03 Bar Chart

Survival Rate by Cabin Availability

Compare survival rates of passengers with cabin information against passengers without cabin information.

Cabin AVG(Survived)

Create a calculated field using IF ISNULL([Cabin]) THEN "Cabin Missing" ELSE "Cabin Available" END. Compare the average of Survived for these two categories.

04 Map Chart

Embarkation Port and Survival Rates

Map the survival rates for passengers from different embarkation ports to compare outcomes geographically.

Embarked AVG(Survived)

Create a geographic field for each port. Use the port locations as map marks and encode AVG(Survived) using color. Add passenger count and survival rate to the tooltip.

05 Bar Chart

Top 10 Highest Fares Paid by Survivors

Highlight the ten highest fares among passengers who survived the disaster.

Survived = 1 Name Fare

Filter Survived to 1, sort Fare in descending order, and display only the top ten passengers. Use passenger name and fare in the visualization.

Map reference

Embarkation Port Locations

These are the three ports you will need to locate when creating your Tableau map. The survival percentages shown are calculated from the supplied dataset.

Marker color represents the relative survival rate. Select a marker to view its passenger count and survival percentage. Map locations are approximate port coordinates.

05 · Go beyond the basics

Advanced Challenges

Once you complete the required KPIs and charts, attempt these additional questions to deepen your analysis.

Bonus 01

Age and Survival

Investigate whether age influenced survival. Create age groups and compare survival rates across them. Handle missing ages separately.

Bonus 02

Family Size and Survival

Create a family-size measure using SibSp and Parch. Investigate whether passengers travelling alone had different survival outcomes from those travelling with family.

Bonus 03

Fare vs. Survival

Examine the relationship between ticket fare and survival. Use a scatter plot or fare bands to identify possible patterns.