Tableau- The Titanic by Rebecca J

Main ideas / concepts conveyed

• Purpose & motivation: Rebecca Jones built a Tableau project about the Titanic because the Titanic interested her personally (including a visit to the Titanic Museum) and because the dataset would be relatable to students.
• Data source & credibility: The dataset was taken from Tableau’s official website, described as “trustworthy.”
• Dataset overview:
  • Approximately 2,207 passengers/crew (rows).
  • Key fields include: name, gender, age, class/crew title, embarked (country embarked from), ticket number, fare, SibSp (siblings/spouses aboard), Parch (parents/children aboard), and survived (Yes/No).

Questions answered (Module 1)

The project aimed to answer three questions using dashboards and a Tableau story:

1. Where did most passengers come from?

   • Determined using the embarked field (country of embarkation).

2. What was the average survival age for each gender in each class?

   • Calculated/visualized by filtering survival to Yes and comparing ages across gender and class.

3. Does having more parents or children aboard increase or decrease the likelihood of survival?

   • Analyzed using Parch (parents/children on board) versus survival.

Tableau dashboard / story structure & what each visualization showed

(Presented as the “method” used to answer the questions.)

1) Data exploration setup

• The dataset is loaded into Tableau with 2,207 rows, containing demographic, ticket, travel, and survival-related fields.
• The dashboard supports interactive filtering (commonly by selecting Survived = Yes/No and gender).

2) “Survival by age” visualization

• Color legend:
  • Red = not surviving
  • Green = surviving
• Interactivity: users can click/filter by Survived (Yes/No) and gender (female/male).
• Key finding:
  • The largest portion of people who did not survive were aged 15–44.
  • When focusing on males, a large portion of not survivors is also concentrated in that range.

3) “Survival by class” visualization

• Displays survival patterns across categories (including class/roles such as Second, Third, Crew, Engineer, Restaurant, Battalion, etc.).
• Interactivity: can sort or filter by male vs. female.
• Key findings:
  • Some crew/occupational categories (described as “debt crew and engineering and the restaurants staff”) showed only males, regardless of survival outcome.
  • For males, third class is indicated as the largest portion that died.
  • For females, the visualization supports comparison of survival across classes.

4) “Embarked” map/graph + “Survival by embarked” view

• Key finding:
  • The most common embarkation country was America, with 264 passengers.
• Interactivity: shows countries and counts to address where most passengers came from.

5) “Survival by family size” (Parch-based) visualization

• Used to evaluate whether more parents/children increases or decreases survival likelihood.
• Key trend described:
  • Survival rate drops tremendously when the number of parents/children reaches around 4.
  • With 0–1 (no children/one parent or child), survival is higher.
  • By 4–5, survival becomes much lower.
  • By 6 or 9, survival is described as very little to none.

Story section: module answers (with numerical highlights)

Module question 1: Most passengers embarked from where?

• Answer: America
• Value stated: 264 passengers

Module question 2: Average survival age by gender in each class

• Filter approach: view survival = Yes
• Key findings stated:
  • Males and females: average survival age is about 30 years “regardless of class” (as described in the narrative).
  • Third class:
    • Largest portion of deaths (as previously stated)
    • Average survival age is about 22 (younger than other classes).
  • First class:
    • Average survival age:
      • Females ~37
      • Males ~36
    • Interpretation offered: first class may have benefited from better privileges/lifeboat access.

Module question 3: Do more parents/children increase survival likelihood?

• Key comparison described:
  • 0 kids/parents: survival rate around 0.33 (~34%)
  • 1 parent/child: about 6%
  • 2: about 5%
  • 3: about 6%
  • Then for 4–5: survival drops sharply
  • For 6 or 9: survival is described as very little to no chance
• Additional interpretation:
  • The speaker notes that it might be expected that having more children could increase chances for lifeboat privileges, but the data suggests the opposite pattern.

Additional insights emphasized in the story

• Women vs. men on board (and survivors):
  • The project suggests females have about half the survival compared to males in the counts/patterns shown.
  • Rationale given: far fewer women were on board (<600 women) compared to men (>1600 men).
• Gender difference by age range (most significant):
  • Ages 15–44 show a “massive difference”:
    • Males have about twice the chance of not surviving compared to females (as described by the speaker).

Speaker / sources

• Speaker: Rebecca Jones
• Source of dataset: Tableau’s official website (dataset used for Titanic passenger/crew data)