datascience

Analyze International Debt Statistics

Project Description

It’s not that we humans only take debts to manage our necessities. A country may also take debt to manage its economy. For example, infrastructure spending is one costly ingredient required for a country’s citizens to lead comfortable lives. The World Bank is the organization that provides debt to countries.

In this project, you are going to analyze international debt data collected by The World Bank. The dataset contains information about the amount of debt (in USD) owed by developing countries across several categories.

• You are going to find the answers to questions like:
  • What is the total amount of debt that is owed by the countries listed in the dataset?
  • Which country owns the maximum amount of debt and what does that amount look like?
  • What is the average amount of debt owed by countries across different debt indicators?

The data used in this project is provided by The World Bank. It contains both national and regional debt statistics for several countries across the globe as recorded from 1970 to 2015. [Source: DataCamp]

Data Exploration

Project Description

In this project, we have done a data analysis for a prestigious country club called Pinebrook. The club has seen a significant increase in membership cancellations over the past few years. Management has asked us to create a report summarizing the membership profile to try and understand what’s driving these cancellations.

• The jupyter notebook includes concepts as:
  • How to translate business questions into powerful SQL queries
  • Use SELECT to return the exact data you need
  • Use WHERE to filter your returned data
  • Create powerful aggregations to sort and analyze your data
  • Organize your data using ORDER BY and LIMIT

Data analysis with SQL

Project Description

In this project we have implemented advanced queries to calculate core business metrics and Key Performance Indicator (KPI).

• The jupyter notebook includes concepts as:
  • Use Common Table Expressions to temporarily store a query’s results
  • Fetch values from different rows using window functions
  • Use self-joins to peak into the future