Week 2 of the Leep Talent Data Technician Skills Bootcamp (Level 3)
This section showcases the Tableau skills I developed during the second week of my bootcamp, building interactive dashboards and visualisations across global health and music streaming datasets.
All dashboards are published live on Tableau Public — linked below each project.
| Project | Dataset | Visuals |
|---|---|---|
| Project 1 — Global Health Insights | GapminderHealth | 4 worksheets + dashboard |
| Project 1 — Extra Visualisations | GapminderHealth | 6 additional worksheets |
| Project 2 — Spotify Music Trends | Spotify Features | 5 worksheets + dashboard |
Dataset: GapminderHealth.xlsx
File: GapminderHealth.xlsx
Live Dashboard: 🔗 View on Tableau Public
A global health dataset with approximately 6,000 records spanning multiple decades (1990–2008), covering countries across all major continents. Fields include Life Expectancy, BMI, Blood Pressure, Cholesterol, Lung/Liver/Stomach Cancer rates, Population, Population Growth, Gender, Country, Continent, and Year. The dataset is sourced via the Gapminder Foundation, which compiles public health data for research and education.
In my own words: This dataset captures decades of health metrics across the globe, giving analysts the kind of multi-dimensional view that a public health organisation — like the NHS or WHO — would need to identify where to focus support, prevention, and resource allocation.
Working as a data analyst for a global health organisation, my team needed to quickly understand key health trends and disparities across countries and continents — particularly how life expectancy has changed over time and how it relates to other health indicators.
Organisation type: National health service, international health charity, or government health department (e.g. NHS, WHO, Public Health England)
This kind of analysis helps health organisations identify underperforming regions, design targeted intervention programmes, and allocate funding where it will have the most impact on population health outcomes.
Worksheet name: Life Expectancy by Continent
Built a horizontal bar chart comparing average life expectancy across continents, sorted descending to surface the highest-performing regions immediately.
What I did:
- Dragged
Continentto Rows andLife Expectancyto Columns - Changed aggregation to Average
- Sorted by field (descending) to rank continents clearly
- Adjusted colours and labels for readability
Worksheet name: Life Expectancy Trend
Built a multi-line time series chart showing how life expectancy has changed over time for the top 5 countries by average life expectancy, with each country shown as a distinct colour.
What I did:
- Dragged
Yearto Columns andLife Expectancyto Rows (Average) - Applied a Top N filter to
Country— Top 5 by Average Life Expectancy - Added
Countryto the Colour mark to differentiate lines - Fixed the Y-axis range (Start: 70, End: 85) to remove whitespace and improve clarity
- Added data labels via the Labels section in the Marks card
Worksheet name: Population by Gender
Built a pie chart showing the population split between male and female for Canada in 2008, with interactive filter controls for Country and Year.
What I did:
- Dragged
Genderto Columns andPopulationto Rows to create a bar chart - Converted to a Pie chart via the Show Me panel
- Added a
Countryfilter, set to Canada, with the filter shown as a dropdown - Added a
Yearfilter, set to include from 2008 onwards - Dragged
Populationto the Label mark to display values on each slice - Resized the chart for visibility and readability on the dashboard
Worksheet name: Life Expectancy vs BMI
Built a scatter plot to explore the relationship between average life expectancy and average BMI across countries, with each point representing a country and coloured by continent.
What I did:
- Dragged
Life Expectancyto Rows andBMIto Columns - Added
Countryto the Detail mark — each dot represents one country - Added
Continentto the Colour mark to group countries visually - Customised the axis ranges to centre the data (X: 500–1350, Y: 1000–3500)
Dashboard name: Global Health Dashboard
Combined all four worksheets into a single interactive dashboard, with the Continent colour filter repositioned to float beside the scatter plot, and the pie chart resized for balanced layout.
From the dashboard, I found that life expectancy is generally higher in Europe and some developed countries compared to other continents. The trend chart shows life expectancy has gradually increased over time across most countries. The scatter plot suggests a relationship between BMI and life expectancy — showing how lifestyle and health factors influence overall outcomes. The population by gender pie chart highlights roughly equal male/female splits, which can form the basis of further demographic analysis.
What this means for the NHS:
By analysing patterns such as BMI and life expectancy trends, the NHS could better understand where to focus health campaigns or prevention programmes. If certain health factors are linked to lower life expectancy in specific regions, early intervention strategies can be targeted where they will be most impactful.
Live Dashboard: 🔗 Global Health Dashboard on Tableau Public
These additional worksheets extend the GapminderHealth analysis with more advanced visual types and calculated fields, published alongside the main dashboard.
Live Extra Visuals:
🔗 Global Average BMI by Country
🔗 Life Expectancy Trends by Continent
Built a filled choropleth map showing average BMI by country, using a red-blue diverging colour palette to make disparities immediately visible.
What I did:
- Set
Countrygeographic role to Country/Region - Double-clicked
Countryto auto-generate a map using Tableau's latitude/longitude - Changed mark type to Filled Map
- Dragged
Life Expectancyto Colour and changed aggregation to Average - Applied a red-blue diverging stepped colour palette for clearer range distinctions
A stacked bar chart showing how life expectancy has evolved year by year across continents from 1990 to 2008, with each continent represented by a distinct colour.
What I did:
- Dragged
Yearto Columns andLife Expectancyto Rows (SUM) - Changed mark type to Bar
- Dragged
Continentto Colour - Added
Life Expectancyto Detail (set to Average) for a more precise breakdown - Adjusted the colour palette and bar width via the Size slider
An enhanced scatter plot comparing average BMI against average life expectancy by country, with a linear trend line added to reveal the overall correlation and notable outliers (e.g. Sierra Leone).
What I did:
- Dragged
BMIto Columns andLife Expectancyto Rows (both set to Average) - Changed mark type to Circle
- Added
Countryto Detail andContinentto Colour - Added a linear trend line via the Analytics tab (drag Trend Line → Linear)
- Set view to Fit Entire View
Created a calculated field combining Liver, Lung, and Stomach Cancer rates into a single Total Cancer Rate metric, then built a horizontal bar chart sorted descending to identify countries with the highest combined cancer burden.
Calculated field used:
[Liver Cancer] + [Lung Cancer] + [Stomach Cancer]
What I did:
- Created a calculated field
Total Cancer Ratevia Analysis → Create Calculated Field - Dragged
Countryto Rows andTotal Cancer Rateto Columns - Applied
Continentto Colour - Sorted descending to rank countries from highest to lowest cancer rate
A line chart showing population growth trends per continent from 1990 to 2008, revealing which regions are growing fastest and how that growth has shifted over time.
What I did:
- Dragged
Yearto Columns andPopulation Growthto Rows - Applied
Continentto Colour - Changed mark type to Line
A bar chart showing average life expectancy broken down by country and gender for the year 2008, coloured by continent and enhanced with average BMI as an additional detail mark.
What I did:
- Added
Genderto Columns andCountry+Life Expectancy(Average) to Rows - Changed mark type to Bar
- Added
Continentto Colour - Added
BMI(Average) to the Marks card as additional detail - Filtered
Yearto 2008 (converted Year to Discrete first) - Edited axis title for clarity
Dataset: SpotifyFeatures_-_xlsx_version.xlsx
File: SpotifyFeatures_-_xlsx_version.xlsx
Live Dashboard: 🔗 View on Tableau Public
A Spotify track-level dataset containing audio feature measurements for thousands of songs across multiple genres. Key fields include: genre, artist_name, track_name, popularity (0–100 scale), acousticness, danceability, energy, instrumentalness, liveness, loudness, speechiness, tempo, valence, and duration_ms. Each field is a numerical Spotify-calculated metric describing an audio characteristic of the track.
In my own words: This dataset gives a detailed audio fingerprint for thousands of tracks, letting an analyst explore what musical characteristics drive popularity — exactly the kind of insight a streaming platform, record label, or music producer would want before investing in new artists or playlists.
Organisation type: Music streaming platform, record label, talent agency, or digital marketing company
Understanding which genres and audio characteristics correlate with high popularity helps a business like Spotify decide which genres to promote, helps labels understand what a hit record sounds like in data, and helps artists make more informed creative decisions about their music.
A bar chart showing how many tracks appear in the dataset for each genre, giving a sense of which genres are most represented.
What I did:
- Dragged
Genreto Rows andTrack Name(Count) to Columns - Sorted descending to rank genres by volume
A bar chart comparing the average popularity score (0–100) across all genres, revealing which genres tend to produce the most-listened-to tracks.
What I did:
- Dragged
Genreto Rows andPopularity(Average) to Columns - Applied colour to highlight the spread between high and low popularity genres
- Sorted descending
A scatter plot exploring the relationship between a track's energy level and its danceability score, to test whether more energetic songs tend to be more danceable.
What I did:
- Dragged
Energyto Columns andDanceabilityto Rows - Added
Genreto Colour to see if the relationship holds across different music types - Applied a trend line (Analytics tab → Linear Trend Line)
A visualisation grouping tracks into popularity band categories (e.g. Low, Medium, High) to show what proportion of songs fall into each tier across the dataset.
What I did:
- Created a calculated field or bin to segment the
Popularityscore into bands - Visualised the distribution to show the concentration of tracks in the medium popularity range
A visualisation showing the spread (range) of popularity scores for individual tracks or genre groupings — highlighting which genres produce consistently popular music vs. those with highly variable results.
What I did:
- Used
Track NameandGenreas dimensions - Plotted popularity range or distribution to reveal consistency vs. variability
Dashboard name: Spotify Dashboard
Combined all five Spotify worksheets into a single interactive dashboard giving a holistic view of genre performance, audio characteristics, and popularity patterns.
Most songs fall into the medium popularity range (around 70%), showing that the majority of tracks perform at an average level. Genres like Pop, Rap and Rock have the highest average popularity. The relationship between energy and danceability shows a clear positive trend — more energetic songs are generally more danceable, which may contribute to their success. Some genres show tight popularity ranges (consistent performers), while others have wide spreads, meaning their success is less predictable and more track-dependent.
What this means for the business: A streaming platform or label can use this analysis to understand which genres are reliable performers vs. which are hit-or-miss. The energy/danceability relationship suggests that investing in high-energy genres may correlate with stronger streaming numbers — a useful signal for playlist curation and A&R decisions.
Live Dashboard: 🔗 Spotify Music Trends Dashboard on Tableau Public
- Tableau Public Desktop — primary visualisation and dashboard tool
- Chart types: Bar, Horizontal Bar, Line, Pie, Scatter, Filled Map, Stacked Bar
- Features: Filters (Top N, discrete year, country), Colour marks, Size marks, Detail marks, Labels, Trend Lines (Linear), Calculated Fields, Fixed Axis Ranges, Pages shelf (animation)
- Calculated fields:
Total Cancer Rate = [Liver Cancer] + [Lung Cancer] + [Stomach Cancer] - Published to: Tableau Public
| File | Description | Source |
|---|---|---|
GapminderHealth.xlsx |
~6,000 global health records across countries, years, and genders | Bootcamp (Gapminder Foundation) |
SpotifyFeatures_-_xlsx_version.xlsx |
Track-level Spotify audio features and popularity scores | Bootcamp (Kaggle) |