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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 5 Credits

GSP787

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Introduction

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?

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.

To complete this lab, you need:

  • Access to a standard internet browser (Chrome browser recommended).
Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.
  • Time to complete the lab---remember, once you start, you cannot pause a lab.
Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

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.

Task 1. Total confirmed cases

  • Build a query that will answer "What was the total count of confirmed cases on ?" 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

Task 2. Worst affected areas

  • Build a query for answering "How many states in the US had more than deaths on ?" The query needs to list the output in the field count_of_states.
Note: 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

Task 3. Identifying hotspots

  • Build a query that will answer "List all the states in the United States of America that had more than confirmed cases on ?" 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

Task 4. Fatality ratio

  1. Build a query that will answer "What was the case-fatality ratio in Italy for the month of 2020?" Case-fatality ratio here is defined as (total deaths / total confirmed cases) * 100.
  2. Write a query to return the ratio for the month of 2020 and contain 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

Task 5. Identifying specific day

  • Build a query that will answer: "On what day did the total number of deaths cross 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

Task 6. Finding days with zero net new cases

The following query is written to identify the number of days in India between and 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

Task 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 % 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

Task 8. Recovery rate

  1. Build a query to list the recovery rates of countries arranged in descending order (limit to ) upto the date May 10, 2020.

  2. 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

Task 9. CDGR - Cumulative daily growth rate

  • The following query is trying to calculate the CDGR on (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 Jan 24, 2020

  • days_diff is the number of days between Jan 24 - 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 Functions, operators, and conditionals documentation to learn more about the SQL function referenced `LEAD()`.

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

Task 10. Create a Looker Studio report

  • Create a Looker Studio report that plots the following for the United States:

    • Number of Confirmed Cases
    • Number of Deaths
    • Date range :

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

Note: Use the below image as a reference while building the report and make sure your report is similar to it. Note: Do not use the Explore with Looker Studio option from BigQuery.

line graph

Congratulations!

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This self-paced lab is part of the Insights from Data with BigQuery skill badge quest. Completing this skill badge quest earns you the badge above, to recognize your achievement. Share your badge on your resume and social platforms, and announce your accomplishment using #GoogleCloudBadge.

This skill badge quest is part of Google’s Data Analyst learning path. If you have already completed the other skill badge quests in this learning path, search the Google Cloud Skills Boost catalog for 20+ other skill badge quests in which you can enroll.

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Manual Last Updated November 17, 2022

Lab Last Tested November 17, 2022

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