This phase of the challenge encourages students to combine and analyze the proposed data to see how different social factors affect life expectancy from 2000 to 2020. Study how life expectancy has changed over that 20-year period and show where important improvements and changes were made.

Your Task:

• Data Integration
• Goal:
• Integrate the provided datasets using Denodo Express, ensuring alignment in data structures, formats, and units, to establish an analytical foundation.
• Focus:
• Connection.
• Import the mandatory sources. Make sure that you use the right connection.
• Create base views that will be useful for later combinations.
• Check if the extracted fields are insightful and pertinent to the challenge''s objectives.
• Combination.
• Evaluate the usage of operations like joins, groupings, and unions.
• Optimization
• Ensure views are effectively executed.
• Optimal and correct data integration will be a key evaluation criteria along with minimizing the data visualization tool queries to the Denodo Platform.
• Comprehensive Analysis
• Goal:
• Using a data visualization tool (recommended Microsoft Power BI Desktop), analyze the previously integrated data to create advanced visualizations and present your findings.
• Focus:
• Identify Life Expectancy Patterns and Outliers:
• Find patterns and unusual data points in life expectancy across different continents and countries.
• Highlight successful regions and areas that need help.
• Evaluate Social Factors:
• Assess the impact of GDP per capita, access to safely managed drinking water, and the ratio of medical doctors (per 10,000 people) on life expectancy.
• Show how the above factors are related.

Guiding Questions:

• Which countries or continents have significantly improved life expectancy, and what helped them succeed?
• Where are the biggest differences in life expectancy, and what actions can be taken based on the identified social factors?
• Has access to safe drinking water affected life expectancy over the past 20 years? If so, how?
• Does the number of doctors impact life expectancy in different countries and continents?
• How is the growth rate of GDP per capita related to changes in life expectancy in different countries and continents, from 2000 to 2020?

• Core Dataset:
• Life Expectancy at Birth (years): WEB | API
• Source: World Bank
• As an indicator of social development, life expectancy at birth reflects the average number of years a newborn is expected to live, based on current mortality rates.
• Use the API link in the Data Integration section.
• Note that the country field must be replaced with a parameterized value. Find some tips here.
• Health Sector Factors:
• Population Using Safely Managed Drinking Water Services (%): WEB | API
• Source: World Health Organization
• Description: This metric measures the percentage of the population with access to drinking water that is safely managed, highlighting progress in environmental health and public safety.
• Use the API link in the Data Integration section.
• Medical Doctors (per 10,000 Population): WEB | API
• Source: World Health Organization
• Description: Indicates the ratio of medical doctors to the population, providing a clear measure of the availability and accessibility of medical care in different continents.
• Social and Economic Factors:
• GDP per capita growth (annual %): WEB | API
• Source: World Bank
• Description: This economic measure reflects the annual growth rate of income per person, offering insights into the economic health of a nation and its potential impacts on social outcomes, including health.
• Note that the country field must be replaced with a parameterized value. Find some tips here.
• Mapping Dataset:
• Continents Map
• Source: Our World in Data
• Description: Facilitates a comparative analysis by continents and countries for understanding geographical differences in life expectancy and its influencing factors.

Incorporate additional datasets to uncover underlying trends and gain deeper insights into social development.

Your Task:

• Discover and Integrate New Data Sources:
• Find and integrate, using Denodo Express, additional datasets that show different factors affecting life expectancy. Using datasets from APIs can help you achieve a higher score.
• Combine this data with the mandatory datasets.
• Holistic Analysis:
• Analyze and visualize, using a data visualization tool (recommended Power BI Desktop), the relationships between various factors that affect social development.
• Suggest actions and strategies to improve social development.

Guiding Questions:

• What other aspects or datasets could help us better understand how health, education, and economic factors affect social progress?
• Which indicators have the strongest links to improvements in life expectancy, and how can these relationships guide strategic decisions?
• Are there any unexpected patterns or anomalies in the data that point to new areas for research or intervention?
• What strategies can be developed from the analysis to solve challenges in social development?