/PF_DS_PT03_G2_EsperanzaVida

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PF_DS_PT03_G2_EsperanzaVida

Following the requisites on the Project Description.

Main Requisites

Must:

  • Think that we are a data consulting firm.
  • Identify factors that influence the "life expectancy" indicator.
  • Work with at least 10 datasets from the World Bank.
  • Use other data sources (at least 2) external to the World Bank to supplement the data.
  • Implement an API to download data from the World Bank.
  • Exclude biological factors and focus on socio-economic factors (Suggestion).
  • Consider cultural issues, human habits, access to healthcare, gender gap, among other aspects (Optional).
  • Create a README with a project summary.
  • Document everything in a separate PDF file.
  • Work with at least 30 countries.
  • Study for at least 30 years.

Milestones

Sprint 1

Have defined:

  • Specific objectives of the group.
  • At least 5 Key Performance Indicators (KPIs).
  • Technologies to be used.
  • Project scope (and out of scope) document.
  • Preliminary Exploratory Data Analysis (EDA), data - quality.
  • GitHub repository.
  • Proposed technology stack implementation.
  • Planning and effort estimation. Gantt chart.
  • Work methodology.
  • Roles and responsibilities.

Sprint 2

Have worked on:

  • Proper design of the Entity-Relationship (ER) model.
  • Documentation.
  • Proposed architecture and diagram.
  • Data dictionary.
  • Sample Data Analysis.
  • Minimum Viable Product (MVP) Dashboard.
  • Machine Learning models and MVP product.
  • Automated Data Warehouse with initial loading.
  • Pipelines for feeding the Data Warehouse.
  • Data validation.
  • At least 2 fact tables and 5 dimensional tables.
  • Incremental Data Loading (could be in video format).
  • Use of Big Data tools such as HDFS, Hive, Spark, and/or No-SQL databases, and/or cloud services.

Sprint 3

Have completed:

  • Final Dashboard.
  • Reports.
  • Storytelling.
  • ML Product.
  • Necessary model adjustments.
  • General project demo.
  • Documentation.
  • Business insights discovered.
  • Business recommendations.
  • Linking KPIs with relevant data.
  • Reviewing milestones presented in previous demos.
  • Final refinements based on feedback from the Head Manager (HM) and Product Owner (PO).
  • Implementing the Machine Learning model.
  • Project-specific extras.
  • Implementing a report with geographical visualization (if applicable).

Last Demo

Present:

  • General project demo.
  • Final deliverable.
  • Documentation.
  • Project video for graduation.

['USA','CHN','JPN','AUS','DEU','CHE','ESP','CAN','FRA','NOR','KOR','NZE','FIN','GBR','SGP','IND','ARG','BRA','URY','CHL','BOL','PER','CUB','VEN','MEX','COL','PRI','SLV','QAT','SYR']