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Internet: A Global Phenomenon
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  • Internet: A Global Phenomenon

    This dataset contains information on internet access around the world.

    The workspace is set up with two CSV files containing information on global internet access for years ranging from 1990 to 2020.

    • internet_users.csv
      • users - The number of people who have used the internet in the last three months
      • share - The share of the entity's population who have used the internet in the last three months
    • adoption.csv
      • fixed_telephone_subs - The number of people who have a telephone landline connection
      • fixed_telephone_subs_share - The share of the entity's population who have a telephone landline connection
      • fixed_broadband_subs - The number of people who have a broadband internet landline connection
      • fixed_broadband_subs_share - The share of the entity's population who have a broadband internet landline connection
      • mobile_cell_subs - The number of people who have a mobile subscription
      • mobile_cell_subs_share - The share of the entity's population who have a mobile subscription

    Both data files are indexed on the following 3 attributes:

    • entity - The name of the country, region, or group.
    • code - Unique id for the country (null for other entities).
    • year - Year from 1990 to 2020.

    Check out the guiding questions or the scenario described below to get started with this dataset! Feel free to make this workspace yours by adding and removing cells, or editing any of the existing cells.

    Source: Our World In Data

    🌎 Some guiding questions to help you explore this data:

    1. What are the top 5 countries with the highest internet use (by population share)?
    2. What are the top 5 countries with the highest internet use for some large regions?
    3. What is the correlation between internet usage (population share) and broadband subscriptions for 2020?

    Note: This is how the World Bank defines the different regions.

    📊 Visualization ideas

    • Line chart: Display internet usage over time of the top 5 countries.
    • Map: Vividly illustrate the internet usage around the world in a certain year on a map. Leveraging, for example, GeoPandas or Folium.

    🔍 Scenario: Identify emerging markets for a global internet provider

    This scenario helps you develop an end-to-end project for your portfolio.

    Background: You work for a global internet provider on a mission to provide affordable Internet access to everybody around the world using satellites. You are tasked with identifying which markets (regions or countries) are most worthwhile to focus efforts on.

    Objective: Construct a top 5 list of countries where there is a big opportunity to roll out our services. Try to consider the amount of people not having access to (good) wired or mobile internet and their spending power.

    Unknown integration
    DataFrameavailable as
    df1
    variable
    SELECT * FROM 'adoption.csv'
    LIMIT 40
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    -- Number of unique countries and entities in 'adoption' table

    Unknown integration
    DataFrameavailable as
    df8
    variable
    SELECT COUNT (DISTINCT code) AS num_countries, 
    		COUNT (DISTINCT entity) AS num_entities
    FROM 'adoption.csv'
    		
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    --Database exploration from years 2000 and 2020 in 'adoption' table --Continents or Regions excluded to avoid duplication

    Unknown integration
    DataFrameavailable as
    df5
    variable
    SELECT *
    FROM 'adoption.csv'
    WHERE year >= 2000 AND code IS NOT NULL
    ORDER By year
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    --Entities in adoption but not in internet_users --Why? because there are no ocuntries, there are regions, continents

    Unknown integration
    DataFrameavailable as
    df4
    variable
    SELECT t1.code AS country,
    	t1.entity AS country_region_group,
    	t2.users AS users,
    	t2.share AS Share
    FROM 'adoption.csv' AS t1
    LEFT JOIN 'internet_users.csv' AS t2
    ON t1.code = t2.code
    WHERE t2.code IS NULL OR t2.entity IS NULL
    ORDER BY t2.share, t2.users DESC
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    --HINT! entity names differ from table to table, so be careful when chosing which column to display in the outcome --Other observation is when categorising, if we do it by 'entity', then he select 'users', if we do it by 'code' (unique country), then we can do it by share

    Unknown integration
    DataFrameavailable as
    df6
    variable
    SELECT *
    FROM 'internet_users.csv'
    WHERE entity = 'Lower-middle-income countries'
    AND year >= 2000;
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

    --Number of unique countries and entities analyzed in 'internet_users' table -- Data Exploration

    Unknown integration
    DataFrameavailable as
    df7
    variable
    SELECT COUNT (DISTINCT code) AS num_countries, 
    		COUNT (DISTINCT entity) AS num_entity
    FROM 'internet_users.csv'
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.