Strategic Outcomes of Survivor: A Data Analysis Portfolio (Seasons 1-43)

In the first screenshot, I started the process by cleaning the data in Power Query Editor within Excel. I focused on removing unnecessary text from the 'hometown' column to maintain data consistency and prepare for further analysis.

I'm demonstrating how to split a column by a delimiter in Power Query. This is a crucial step to ensure that data is properly structured, which facilitates more accurate and efficient analysis.

Captures my work in Tableau Public, where I created relationships between different data tables. This is where I synthesized disparate data sources to provide a comprehensive view of the dataset.

I’m using Microsoft SQL Server Management Studio. Here, I’m setting up a new database, highlighting my ability to work across various database management systems.

Shows my work in modifying column properties in an import flat file wizard. This step was key in shaping the data structure to be conducive to my analysis requirements.

Part 2 of above

I’m executing a SQL query to aggregate data. This query calculates the total number of votes against contestants, which is vital for understanding voting patterns.

shows a similar SQL query, but this time I'm retrieving the number of votes against each contestant as well as the number of seasons they participated in. This provides deeper insights into the contestants' performance across different seasons.

I’ve used an INNER JOIN to combine data from different tables, linking the contestant data with the season information. This enriched dataset allows for a multifaceted analysis.

the tenth image illustrates a more complex SQL query where I've used CONCAT to create a new column that combines the contestant's name with their respective season. This enhances the readability of the data and prepares it for a more narrative-driven analysis.

I connected an Excel file as a data source in Tableau Public and prepared it for analysis, focusing on the most votes received in a single season of Survivor.

I performed an SQL query to find the age at which each Survivor winner won their season, joining data on contestants with season details for a complete picture.

I executed an SQL query to count the number of male and female contestants by the placement they finished, providing insights into gender distribution across different ranks in Survivor.

This SQL query helped me analyze the gender of the winners per season, allowing me to observe patterns and trends across different Survivor seasons.

In Tableau, I created a visualization that showcases the professions of Survivor winners, adding a personal dimension to the data narrative.

I visualized the relationship between contestants' professions and their performance at the merge stage in Survivor, highlighting any correlations between career backgrounds and game strategy.

This map visualization represents where Survivor contestants are from, offering geographical context to the player demographics and potentially influencing factors in the game's dynamics.

I constructed a bar chart in Tableau to illustrate the players with the most votes against them in Survivor history, which can signal player threat levels or social dynamics.

This bar chart displays the most votes received by contestants in a single season, highlighting the individuals who were targeted the most within a given season.

I plotted the ages of Survivor winners when they won their season, offering insight into how age may correlate with winning the game.

This bar chart visualizes the average placement for Survivor contestants who have played three or more times. The length and color of the bars indicate the average finish position, providing a quick understanding of which contestants tend to go further in the game.

The bubble chart represents the average finish positions of contestants who have played Survivor four times. The size of the bubble correlates with the average placement, and the varying colors help distinguish between different players.

Another bar chart details the average placements of contestants who played exactly two times. This chart helps to compare the consistency of their game across two different seasons.

This combined visualization brings together the average placements of both 3-time players and 2-time players along with a bubble chart for 4-time players. It provides a comprehensive view of contestants' performances based on the number of times they've played.

A pie chart here illustrates the gender distribution of Survivor winners, offering insights into the balance between male and female winners throughout the show's history.

A simple yet effective numerical visualization that clearly presents the average age of Survivor winners. This large, bold number makes an immediate impact, suggesting the prime age for winning the game based on historical data.