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Insights from Data with BigQuery: Challenge Lab

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Insights from Data with BigQuery: Challenge Lab

1 hour 30 minutes 9 Credits

GSP787

Google Cloud Self-Paced Labs

Overview

You must complete a series of tasks within the allocated time period. Instead of following step-by-step instructions, you'll be given a scenario and a set of tasks - you figure out how to complete it on your own! An automated scoring system (shown on this page) will provide feedback on whether you have completed your tasks correctly.

To score 100% you must complete all tasks within the time period!

When you take a Challenge Lab, you will not be taught Google Cloud concepts. To build the solution to the challenge presented, use skills learned from the labs in the quest this challenge lab is part of. You will be expected to extend your learned skills; you will be expected to change broken queries.

This lab is recommended for students who have enrolled in the Insights from Data with BigQuery quest. Are you ready for the challenge?

Scenario

You're part of a public health organization which is tasked with identifying answers to queries related to the Covid-19 pandemic. Obtaining the right answers will help the organization in planning and focusing healthcare efforts and awareness programs appropriately.

The dataset and table that will be used for this analysis will be : bigquery-public-data.covid19_open_data.covid19_open_data. This repository contains country-level datasets of daily time-series data related to COVID-19 globally. It includes data relating to demographics, economy, epidemiology, geography, health, hospitalizations, mobility, government response, and weather.

Setup and Requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

What you need

To complete this lab, you need:

  • Access to a standard internet browser (Chrome browser recommended).
  • Time to complete the lab.

Note: If you already have your own personal Google Cloud account or project, do not use it for this lab.

Note: If you are using a Chrome OS device, open an Incognito window to run this lab.

How to start your lab and sign in to the Google Cloud Console

  1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is a panel populated with the temporary credentials that you must use for this lab.

    Open Google Console

  2. Copy the username, and then click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.

    Sign in

    Tip: Open the tabs in separate windows, side-by-side.

  3. In the Sign in page, paste the username that you copied from the left panel. Then copy and paste the password.

    Important: You must use the credentials from the left panel. Do not use your Google Cloud Training credentials. If you have your own Google Cloud account, do not use it for this lab (avoids incurring charges).

  4. Click through the subsequent pages:

    • Accept the terms and conditions.
    • Do not add recovery options or two-factor authentication (because this is a temporary account).
    • Do not sign up for free trials.

After a few moments, the Cloud Console opens in this tab.

Query 1: Total Confirmed Cases

Build a query that will answer "What was the total count of confirmed cases on Apr 15, 2020?" The query needs to return a single row containing the sum of confirmed cases across all countries. The name of the column should be total_cases_worldwide.

Columns to reference:

  • cumulative_confirmed
  • date

Click Check my progress to verify the objective. Total Confirmed Cases

Query 2: Worst Affected Areas

Build a query for answering "How many states in the US had more than 100 deaths on Apr 10, 2020?" The query needs to list the output in the field count_of_states. Hint: Don't include NULL values.

Columns to reference:

  • country_name
  • subregion1_name (for state information)
  • cumulative_deceased

Click Check my progress to verify the objective. Worst Affected Areas

Query 3: Identifying Hotspots

Build a query that will answer "List all the states in the United States of America that had more than 1000 confirmed cases on Apr 10, 2020?" The query needs to return the State Name and the corresponding confirmed cases arranged in descending order. Name of the fields to return state and total_confirmed_cases.

Columns to reference:

  • country_code
  • subregion1_name (for state information)
  • cumulative_confirmed

Click Check my progress to verify the objective. Identifying Hotspots

Query 4: Fatality Ratio

Build a query that will answer "What was the case-fatality ratio in Italy for the month of April 2020?" Case-fatality ratio here is defined as (total deaths / total confirmed cases) * 100. Write a query to return the ratio for the month of April 2020 and containing the following fields in the output: total_confirmed_cases, total_deaths, case_fatality_ratio.

Columns to reference:

  • country_name
  • cumulative_confirmed
  • cumulative_deceased

Click Check my progress to verify the objective. Fatality Ratio

Query 5: Identifying specific day

Build a query that will answer: "On what day did the total number of deaths cross 10000 in Italy?" The query should return the date in the format yyyy-mm-dd.

Columns to reference:

  • country_name
  • cumulative_deceased

Click Check my progress to verify the objective. Identifying Hotspots

Query 6: Finding days with zero net new cases

The following query is written to identify the number of days in India between 21 Feb 2020 and 15 March 2020 when there were zero increases in the number of confirmed cases. However it is not executing properly. You need to update the query to complete it and obtain the result:

WITH india_cases_by_date AS ( SELECT date, SUM(cumulative_confirmed) AS cases FROM `bigquery-public-data.covid19_open_data.covid19_open_data` WHERE country_name="India" AND date between '2020-02-21' and '2020-03-15' GROUP BY date ORDER BY date ASC ) , india_previous_day_comparison AS (SELECT date, cases, LAG(cases) OVER(ORDER BY date) AS previous_day, cases - LAG(cases) OVER(ORDER BY date) AS net_new_cases FROM india_cases_by_date )

Click Check my progress to verify the objective. Finding days with zero net new cases

Query 7: Doubling rate

Using the previous query as a template, write a query to find out the dates on which the confirmed cases increased by more than 10% compared to the previous day (indicating doubling rate of ~ 7 days) in the US between the dates March 22, 2020 and April 20, 2020. The query needs to return the list of dates, the confirmed cases on that day, the confirmed cases the previous day, and the percentage increase in cases between the days. Use the following names for the returned fields: Date, Confirmed_Cases_On_Day, Confirmed_Cases_Previous_Day and Percentage_Increase_In_Cases.

Click Check my progress to verify the objective. Doubling rate

Query 8: Recovery rate

Build a query to list the recovery rates of countries arranged in descending order (limit to 10) upto the date May 10, 2020. Restrict the query to only those countries having more than 50K confirmed cases. The query needs to return the following fields: country, recovered_cases, confirmed_cases, recovery_rate.

Columns to reference:

  • country_name
  • cumulative_confirmed
  • cumulative_recovered

Click Check my progress to verify the objective. Recovery rate

Query 9: CDGR - Cumulative Daily Growth Rate

The following query is trying to calculate the CDGR on May 10, 2020(Cumulative Daily Growth Rate) for France since the day the first case was reported. The first case was reported on Jan 24, 2020. The CDGR is calculated as:

((last_day_cases/first_day_cases)^1/days_diff)-1)

Where :

  • last_day_cases is the number of confirmed cases on May 10, 2020
  • first_day_cases is the number of confirmed cases on Feb 02, 2020
  • days_diff is the number of days between Feb 02 - May 10, 2020

The query isn’t executing properly. Can you fix the error to make the query execute successfully?

WITH france_cases AS ( SELECT date, SUM(cumulative_confirmed) AS total_cases FROM `bigquery-public-data.covid19_open_data.covid19_open_data` WHERE country_name="France" AND date IN ('2020-01-24', '2020-05-10') GROUP BY date ORDER BY date) , summary as ( SELECT total_cases AS first_day_cases, LEAD(total_cases) AS last_day_cases, DATE_DIFF(LEAD(date) OVER(ORDER BY date),date, day) AS days_diff FROM france_cases LIMIT 1 ) select first_day_cases, last_day_cases, days_diff, SQRT((last_day_cases/first_day_cases),(1/days_diff))-1 as cdgr from summary

Note: Refer to the following page to learn more about the SQL function referenced LEAD().

Click Check my progress to verify the objective. CDGR - Cumulative Daily Growth Rate

Create a Datastudio report

Create a Google Data Studio report that plots the following for the United States:

  • Number of Confirmed Cases
  • Number of Deaths
  • Date range : 2020-03-15 to 2020-04-30

Click Check my progress to verify the objective. Create a Datastudio report

Hint: Use the below image as a reference while building the report and make sure your report matches it.

Hint: Do not use Explore with Data Studio option from BigQuery.

screenshot

Congratulations!

In this lab you used BigQuery to query the public Covid dataset (bigquery-public-data.covid19_open_data.covid19_open_data) to answer important questions related to the Covid-19 pandemic. You also created a Datastudio report for one of the query outputs to visualize the cases and deaths trend.

Insights_from_Data_with_BQ_Skill_WBG.png

Finish Your Quest

This self-paced lab is part of the Qwiklabs Insights from Data with BigQuery Quest. A Quest is a series of related labs that form a learning path. Completing this Quest earns you the badge above, to recognize your achievement. You can make your badge public and link to them in your online resume or social media account. Enroll in this Quest and get immediate completion credit if you've taken this lab. See other available Qwiklabs Quests.

This skill badge quest is part of Google Cloud’s Data Analyst learning path. Continue your learning journey by enrolling in the Create ML Models with BigQuery ML quest.

Next Steps / Learn More

This lab is also part of a series of labs called Challenge Labs. These labs are designed test your Google Cloud knowledge and skill. Search for "Challenge Lab" in the lab catalog and challenge yourself!

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Manual Last Updated October 13, 2021
Lab Last Tested October 13, 2021

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