From df39adb0d36d961759ef14e654a6a1efca917585 Mon Sep 17 00:00:00 2001 From: Phone Thiri Yadana Date: Wed, 20 Oct 2021 19:37:17 +0800 Subject: [PATCH] 365 sql just exploring --- .../01_mock_interview.sql | 106 ++++++ .../02_mock_interview.sql | 50 +++ .../5_steps_framework.png | Bin 0 -> 129176 bytes .../functions_to_master.png | Bin 0 -> 109250 bytes .../interviews_setup.sql | 331 ++++++++++++++++++ 5 files changed, 487 insertions(+) create mode 100644 SQL for Data Science Interviews - 365 DS/01_mock_interview.sql create mode 100644 SQL for Data Science Interviews - 365 DS/02_mock_interview.sql create mode 100644 SQL for Data Science Interviews - 365 DS/5_steps_framework.png create mode 100644 SQL for Data Science Interviews - 365 DS/functions_to_master.png create mode 100644 SQL for Data Science Interviews - 365 DS/interviews_setup.sql diff --git a/SQL for Data Science Interviews - 365 DS/01_mock_interview.sql b/SQL for Data Science Interviews - 365 DS/01_mock_interview.sql new file mode 100644 index 0000000..c3824fe --- /dev/null +++ b/SQL for Data Science Interviews - 365 DS/01_mock_interview.sql @@ -0,0 +1,106 @@ +/* +Mock Interview 1) + +-- table name: post_events + +-- user_id int +-- created_at datetime +-- event_name varchar +-- (event, post, cancel) + + +Question: What information would you like to start off by pulling to get an overall understanding of +the post feature? + + +Possible Answer: +We might want to get an idea of OVERALL HEALTH. ++ Total number of posts (number of enters) ++ Posts Made by Date ++ Success Rate ++ Cancel Rate + +*/ + + +/***** Success Rate *****/ +-- success rate by date +-- date | success rate = number of posts / number of enters + +-- explaination in english +-- group by date +-- count of number of posts / count of number enters + +SELECT created_at, + COUNT(CASE WHEN event_name = 'post' 1 ELSE null END) * 1.00 / + COUNT (CASE WHEN event_name = 'enter' 1 ELSE null END) * 100 AS percent_success +FROM post_events +GROUP BY created_at +ORDER BY created_at; + + +/* +Question: When sucess rates are low, how can we diagonis the issue? + +Possible Answer: There can be several approach to this problem. But one possible way is we can take a look at the map +out between created_at and success_rate. And see whether there is a pattern during a certain period of time. +This can be one off dip, etc. Based on this information, we can take a further look at if there is any underlying +issue in the application or not. Or if there is any potential group of users who are causing this kind of unsccessful posts. +*/ + + +/* +Questions: + +What are the success rates by day? + +Which day of the week has the lowest success rate? +*/ + +-- group by dow of created_date +-- average of perc_success +-- order by per_success +-- day | per_sucess + +WITH created_events AS( + SELECT created_at, + COUNT(CASE WHEN event_name = 'post' 1 ELSE null END) * 1.00 / + COUNT (CASE WHEN event_name = 'enter' 1 ELSE null END) * 100 AS percent_success + FROM post_events + GROUP BY created_at + ORDER BY created_at) + +SELECT EXTRACT (dow FROM created_at) AS dow, +AVG(percent_success) +FROM created_events +GROUP BY 1 +ORDER BY 2 ASC; + + +/* +Question: What could be a problem if we're aggregating on percent success? + +Possible Answer: this can lead to a problem that we're not taking into consideration of underlying distribution +of percent success across the dates. +*/ + + +SELECT EXTRACT (dow FROM created_at) AS dow, + COUNT(CASE WHEN event_name = 'post' 1 ELSE null END) * 1.00 / + COUNT (CASE WHEN event_name = 'enter' 1 ELSE null END) * 100 AS percent_success +FROM created_events +GROUP BY 1 +ORDER BY 2 ASC; + +/*************************** Using actual table *******************************/ + +SELECT * +FROM interviews.post_events +LIMIT 10; + +SELECT EXTRACT (dow FROM created_at) AS dow, + COUNT(CASE WHEN event_name = 'post' THEN 1 ELSE null END) * 1.00 / + COUNT (CASE WHEN event_name = 'enter' THEN 1 ELSE null END) * 100 AS percent_success +FROM interviews.post_events +GROUP BY 1 +ORDER BY 2 ASC; \ No newline at end of file diff --git a/SQL for Data Science Interviews - 365 DS/02_mock_interview.sql b/SQL for Data Science Interviews - 365 DS/02_mock_interview.sql new file mode 100644 index 0000000..f22aae8 --- /dev/null +++ b/SQL for Data Science Interviews - 365 DS/02_mock_interview.sql @@ -0,0 +1,50 @@ +/* +Mock Interview 2) + +Question: + +-- 1) Find the date with the highest total energy consumption from our datacenters. +-- 2) Output the date along with the total engery consumption across all datacenters. + +-- Table: eu_energy +date datetime +consumption int + +-- Table: asia_energy +date datetime +consumption int + +-- Table: na_energy +date datetime +consumption int + +*/ + +/****** + +Always make sure you under the tables and columns correctly. Ask the interviewer if you need to make any assumptions +on the columns data, etc. + +1) So there are 3 tables representing energy consumptions across different continents. +Can I assume that there is only one energy consumption for each particular date Or can there be a multiple consumptions +for a specific date? Is there any possible missing values too? + +We can just sum it up across different dates across the different tables. +*****/ + + +/* +Question: What would you do if in the first table there are two of the same dates with +different energy consumptions? + +Possibe Answer: +Then we can just group by using Date and sum the engery consumptions. +*/ + + +/**** + +Clarification Question back to Interviwer: +Is there a situation that there are Multiple Dates with the same highest Total Energy Consumption? +****/ + diff --git a/SQL for Data Science Interviews - 365 DS/5_steps_framework.png b/SQL for Data Science Interviews - 365 DS/5_steps_framework.png new file mode 100644 index 0000000000000000000000000000000000000000..55020697757fcf34397040c978cf403b620dd879 GIT binary patch literal 129176 zcmV(}K+wO5P)}J4&0v@Bh)rpR;tgH#_s*`#zdCv-E?%e?ciFgqW}F7fJ;4H!a{3e8o;l zm#88TooK3}srad`+if;Ul5|}Q$br1vT({emP%GXdN6t6Hy~8kG`NK*iUj|J7eNq3xTDS*yrJntrxbrPhKJu5ut}nb|EY=s{lmK&-i?2W 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