adding analytical patterns to handbook

bootcamp/materials/4-applying-analytical-patterns/README.md

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+# 4 Applying Analytics Patterns and Advanced SQL
+
+We are going to be talking about a few different analytical patterns
+
+- State Change Tracking
+- Smoothing of trend lines
+- J curve analysis

bootcamp/materials/4-applying-analytical-patterns/homework/homework.md

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+# Week 4 Applying Analytical Patterns
+The homework this week will be using the `players`, `players_scd`, and `player_seasons` tables from week 1
+
+- A query that does state change tracking for `players`
+  - A player entering the league should be `New`
+  - A player leaving the league should be `Retired`
+  - A player staying in the league should be `Continued Playing`
+  - A player that comes out of retirement should be `Returned from Retirement`
+  - A player that stays out of the league should be `Stayed Retired`
+
+- A query that uses `GROUPING SETS` to do efficient aggregations of `game_details` data
+  - Aggregate this dataset along the following dimensions
+    - player and team
+      - Answer questions like who scored the most points playing for one team?
+    - player and season
+      - Answer questions like who scored the most points in one season?
+    - team
+      - Answer questions like which team has won the most games?
+
+- A query that uses window functions on `game_details` to find out the following things:
+  - What is the most games a team has won in a 90 game stretch? 
+  - How many games in a row did LeBron James score over 10 points a game?
+
+
+Please add these queries into a folder `homework/<discord-username>`

bootcamp/materials/4-applying-analytical-patterns/lecture-lab/funnel_analysis.sql

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+WITH deduped_events AS (
+    SELECT
+        url, host, user_id,event_time
+    FROM events
+    GROUP BY 1,2,3,4
+),
+     clean_events AS (
+         SELECT *, DATE(event_time) as event_date FROM deduped_events
+WHERE user_id IS NOT NULL
+ORDER BY user_id, event_time
+     ),
+     converted AS (
+         SELECT ce1.user_id,
+                ce1.event_time,
+                ce1.url,
+                COUNT(DISTINCT CASE WHEN ce2.url = '/api/v1/user' THEN ce2.url END) as converted
+         FROM clean_events ce1
+                  JOIN clean_events ce2
+                       ON ce2.user_id = ce1.user_id
+                           AND ce2.event_date = ce1.event_date
+                           AND ce2.event_time > ce1.event_time
+
+         GROUP BY 1, 2,3
+     )
+
+SELECT url, COUNT(1), CAST(SUM(converted) AS REAL)/COUNT(1)
+FROM converted
+GROUP BY 1
+HAVING CAST(SUM(converted) AS REAL)/COUNT(1) > 0
+AND COUNT(1) > 100

bootcamp/materials/4-applying-analytical-patterns/lecture-lab/grouping_sets.sql

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+CREATE TABLE device_hits_dashboard AS
+
+WITH events_augmented AS (
+    SELECT COALESCE(d.os_type, 'unknown')      AS os_type,
+           COALESCE(d.device_type, 'unknown')  AS device_type,
+           COALESCE(d.browser_type, 'unknown') AS browser_type,
+           url,
+           user_id
+    FROM events e
+             JOIN devices d on e.device_id = d.device_id
+)
+
+SELECT
+       CASE
+           WHEN GROUPING(os_type) = 0
+               AND GROUPING(device_type) = 0
+               AND GROUPING(browser_type) = 0
+               THEN 'os_type__device_type__browser'
+           WHEN GROUPING(browser_type) = 0 THEN 'browser_type'
+           WHEN GROUPING(device_type) = 0 THEN 'device_type'
+           WHEN GROUPING(os_type) = 0 THEN 'os_type'
+       END as aggregation_level,
+       COALESCE(os_type, '(overall)') as os_type,
+       COALESCE(device_type, '(overall)') as device_type,
+       COALESCE(browser_type, '(overall)') as browser_type,
+       COUNT(1) as number_of_hits
+FROM events_augmented
+GROUP BY GROUPING SETS (
+        (browser_type, device_type, os_type),
+        (browser_type),
+        (os_type),
+        (device_type)
+    )
+ORDER BY COUNT(1) DESC

bootcamp/materials/4-applying-analytical-patterns/lecture-lab/growth_accounting.sql

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+WITH yesterday AS (
+    SELECT * FROM users_growth_accounting
+    WHERE date = DATE('2023-03-09')
+),
+     today AS (
+         SELECT
+            CAST(user_id AS TEXT) as user_id,
+            DATE_TRUNC('day', event_time::timestamp) as today_date,
+            COUNT(1)
+         FROM events
+         WHERE DATE_TRUNC('day', event_time::timestamp) = DATE('2023-03-10')
+         AND user_id IS NOT NULL
+         GROUP BY user_id, DATE_TRUNC('day', event_time::timestamp)
+     )
+
+         SELECT COALESCE(t.user_id, y.user_id)                    as user_id,
+                COALESCE(y.first_active_date, t.today_date)       AS first_active_date,
+                COALESCE(t.today_date, y.last_active_date)        AS last_active_date,
+                CASE
+                    WHEN y.user_id IS NULL THEN 'New'
+                    WHEN y.last_active_date = t.today_date - Interval '1 day' THEN 'Retained'
+                    WHEN y.last_active_date < t.today_date - Interval '1 day' THEN 'Resurrected'
+                    WHEN t.today_date IS NULL AND y.last_active_date = y.date THEN 'Churned'
+                    ELSE 'Stale'
+                    END                                           as daily_active_state,
+                CASE
+                    WHEN y.user_id IS NULL THEN 'New'
+                    WHEN y.last_active_date < t.today_date - Interval '7 day' THEN 'Resurrected'
+                    WHEN
+                            t.today_date IS NULL
+                            AND y.last_active_date = y.date - interval '7 day' THEN 'Churned'
+                    WHEN COALESCE(t.today_date, y.last_active_date) + INTERVAL '7 day' >= y.date THEN 'Retained'
+                    ELSE 'Stale'
+                    END                                           as weekly_active_state,
+                COALESCE(y.dates_active,
+                         ARRAY []::DATE[])
+                    || CASE
+                           WHEN
+                               t.user_id IS NOT NULL
+                               THEN ARRAY [t.today_date]
+                           ELSE ARRAY []::DATE[]
+                    END                                           AS date_list,
+                COALESCE(t.today_date, y.date + Interval '1 day') as date
+         FROM today t
+                  FULL OUTER JOIN yesterday y
+                                  ON t.user_id = y.user_id

bootcamp/materials/4-applying-analytical-patterns/lecture-lab/retention_analysis.sql

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+SELECT
+       date - first_active_date AS days_since_first_active,
+       CAST(COUNT(CASE
+           WHEN daily_active_state
+                    IN ('Retained', 'Resurrected', 'New') THEN 1 END) AS REAL)/COUNT(1) as pct_active,
+       COUNT(1) FROM users_growth_accounting
+GROUP BY date - first_active_date;

bootcamp/materials/4-applying-analytical-patterns/lecture-lab/window_based_analysis.sql

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+WITH events_augmented AS (
+    SELECT COALESCE(d.os_type, 'unknown')      AS os_type,
+           COALESCE(d.device_type, 'unknown')  AS device_type,
+           COALESCE(d.browser_type, 'unknown') AS browser_type,
+           url,
+           user_id,
+           CASE
+               WHEN referrer like '%linkedin%' THEN 'Linkedin'
+               WHEN referrer like '%t.co%' THEN 'Twitter'
+               WHEN referrer like '%google%' THEN 'Google'
+               WHEN referrer like '%lnkd%' THEN 'Linkedin'
+               WHEN referrer like '%eczachly%' THEN 'On Site'
+               WHEN referrer LIKE '%zachwilson%' THEN 'On Site'
+               ELSE referrer
+               END                             as referrer,
+           DATE(event_time)                    AS event_date
+    FROM events e
+             JOIN devices d on e.device_id = d.device_id
+),
+     aggregated AS (
+         SELECT url, referrer, event_date, COUNT(1) as count
+         FROM events_augmented
+         GROUP BY url, referrer, event_date
+     ),
+     windowed AS (
+         SELECT referrer,
+                url,
+                event_date,
+                count,
+                SUM(count) OVER (
+                    PARTITION BY referrer, url, DATE_TRUNC('month', event_date)
+                    ORDER BY event_date
+                    ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
+                    ) AS monthly_cumulative_sum,
+                SUM(count) OVER (
+                    PARTITION BY referrer, url
+                    ORDER BY event_date
+                    ) AS rolling_cumulative_sum,
+                SUM(count) OVER (
+                    PARTITION BY referrer, url
+                    ORDER BY event_date
+                    ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING
+                    ) AS total_cumulative_sum,
+                SUM(count) OVER (
+                    PARTITION BY referrer, url
+                    ORDER BY event_date
+                    ROWS BETWEEN 6 preceding AND CURRENT ROW
+                    ) AS weekly_rolling_count,
+                SUM(count) OVER (
+                    PARTITION BY referrer, url
+                    ORDER BY event_date
+                    ROWS BETWEEN 13 preceding AND 6 preceding
+                    ) AS previous_weekly_rolling_count
+         FROM aggregated
+         ORDER BY referrer, url, event_date
+     )
+
+SELECT referrer,
+       url,
+       event_date,
+       count,
+       weekly_rolling_count,
+       previous_weekly_rolling_count,
+       CAST(count AS REAL) / monthly_cumulative_sum as pct_of_month,
+       CAST(count AS REAL) / total_cumulative_sum   as pct_of_total
+FROM windowed
+WHERE total_cumulative_sum > 500
+  AND referrer IS NOT NULL

bootcamp/materials/4-applying-analytical-patterns/tables/user_growth_accounting.sql

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+ CREATE TABLE users_growth_accounting (
+     user_id TEXT,
+     first_active_date DATE,
+     last_active_date DATE,
+     daily_active_state TEXT,
+     weekly_active_state TEXT,
+     dates_active DATE[],
+     date DATE,
+     PRIMARY KEY (user_id, date)
+ );