Skip to content

Commit f4414a4

Browse files
author
Will
committed
Merge branch 'update_demo_app'
2 parents 7bec99e + bee2d7c commit f4414a4

2 files changed

Lines changed: 125 additions & 1 deletion

File tree

.gitignore

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -11,7 +11,7 @@ _site/
1111
*_files/
1212

1313
# Converted notebooks (generated from Jupytext .py files)
14-
Team_Projects/**/*.qmd
14+
#Team_Projects/**/*.qmd
1515

1616
# Python
1717
__pycache__/
Lines changed: 124 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,124 @@
1+
---
2+
title: "Education & Economic Growth"
3+
subtitle: "World Bank Data Analysis"
4+
author: "Sample Team "
5+
date: "2025"
6+
format:
7+
revealjs:
8+
theme: default
9+
slide-number: true
10+
preview-links: auto
11+
footer: "DataDive 2025 - World Bank"
12+
---
13+
14+
## Overview
15+
16+
This presentation explores the relationship between **education levels** and **economic growth indicators** using World Bank data.
17+
18+
. . .
19+
20+
Key findings:
21+
22+
- Higher education correlates with stronger economies
23+
- Technology and manufacturing sectors show strongest effects
24+
- Regional variations exist in education-growth relationships
25+
26+
## Data Sources
27+
28+
We analyzed data from multiple World Bank datasets:
29+
30+
- **World Development Indicators (WDI)**
31+
- **Education Statistics (EdStats)**
32+
- **Jobs Indicators Database**
33+
34+
::: {.notes}
35+
These datasets span from 1990-2023 and cover over 200 countries.
36+
:::
37+
38+
## Methodology
39+
40+
::: {.incremental}
41+
1. Data collection and cleaning
42+
2. Feature engineering for education metrics
43+
3. Economic indicator analysis
44+
4. Correlation and regression modeling
45+
5. Regional segmentation analysis
46+
:::
47+
48+
## Key Findings {.smaller}
49+
50+
| Region | Education Score | GDP Growth | Correlation |
51+
|--------|----------------|------------|-------------|
52+
| East Asia | 85.2 | 6.8% | 0.82 |
53+
| Europe | 88.5 | 2.1% | 0.75 |
54+
| N. America | 87.3 | 2.4% | 0.71 |
55+
| Sub-Saharan Africa | 62.1 | 4.2% | 0.68 |
56+
57+
## Technology Sector Impact
58+
59+
Countries with higher tertiary education rates show:
60+
61+
- 📈 **3.2x** more tech sector jobs
62+
- 💰 **45%** higher average wages in tech
63+
- 🏢 **2.1x** more tech startups per capita
64+
65+
## Manufacturing Sector
66+
67+
Education's role in manufacturing transformation:
68+
69+
![Manufacturing Evolution](https://images.unsplash.com/photo-1581091226825-a6a2a5aee158?w=600)
70+
71+
::: {.fragment}
72+
*Higher skilled workforce enables advanced manufacturing adoption*
73+
:::
74+
75+
## Regional Analysis
76+
77+
```{python}
78+
#| echo: false
79+
#| eval: false
80+
import altair as alt
81+
import pandas as pd
82+
83+
# Sample data for visualization
84+
data = pd.DataFrame({
85+
'Region': ['East Asia', 'Europe', 'N. America', 'S. America', 'Africa'],
86+
'Education Index': [85, 88, 87, 72, 62],
87+
'GDP Growth': [6.8, 2.1, 2.4, 1.8, 4.2]
88+
})
89+
90+
chart = alt.Chart(data).mark_circle(size=100).encode(
91+
x='Education Index',
92+
y='GDP Growth',
93+
color='Region',
94+
tooltip=['Region', 'Education Index', 'GDP Growth']
95+
).properties(width=500, height=300)
96+
97+
chart
98+
```
99+
100+
*Visualization: Regional education vs. economic growth*
101+
102+
## Recommendations
103+
104+
::: {.callout-tip}
105+
## Policy Implications
106+
1. Increase tertiary education access
107+
2. Align curricula with industry needs
108+
3. Invest in vocational training programs
109+
:::
110+
111+
## Next Steps
112+
113+
- Expand analysis to include more recent data
114+
- Develop predictive models for policy planning
115+
- Create interactive dashboard for stakeholders
116+
117+
## Questions?
118+
119+
**Contact:**
120+
121+
- Team Testers
122+
- DataDive 2025
123+
124+
Thank you for your attention! 🎓📊

0 commit comments

Comments
 (0)