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 0000000..5502069
Binary files /dev/null and b/SQL for Data Science Interviews - 365 DS/5_steps_framework.png differ
diff --git a/SQL for Data Science Interviews - 365 DS/functions_to_master.png b/SQL for Data Science Interviews - 365 DS/functions_to_master.png
new file mode 100644
index 0000000..55b7983
Binary files /dev/null and b/SQL for Data Science Interviews - 365 DS/functions_to_master.png differ
diff --git a/SQL for Data Science Interviews - 365 DS/interviews_setup.sql b/SQL for Data Science Interviews - 365 DS/interviews_setup.sql
new file mode 100644
index 0000000..49d642a
--- /dev/null
+++ b/SQL for Data Science Interviews - 365 DS/interviews_setup.sql	
@@ -0,0 +1,331 @@
+  create schema interviews;
+
+  set schema 'interviews';
+
+    -- q1 
+  drop table if exists post_events;
+
+  CREATE TABLE post_events (
+  user_id integer, 
+  created_at date, 
+  event_name varchar(50) -- (enter, post, cancel)
+  );
+
+  insert into post_events values (1, '2020-06-01', 'enter');
+  insert into post_events values (1, '2020-06-01', 'post');
+  insert into post_events values (2, '2020-06-01', 'enter');
+  insert into post_events values (2, '2020-06-01', 'cancel');
+  insert into post_events values (2, '2020-06-01', 'enter');
+  insert into post_events values (2, '2020-06-01', 'cancel');
+  insert into post_events values (1, '2020-06-02', 'enter');
+  insert into post_events values (1, '2020-06-02', 'cancel');
+  insert into post_events values (3, '2020-06-02', 'enter');
+  insert into post_events values (3, '2020-06-02', 'post');
+  insert into post_events values (1, '2020-06-03', 'enter');
+  insert into post_events values (1, '2020-06-03', 'cancel');
+  insert into post_events values (3, '2020-06-03', 'enter');
+  insert into post_events values (3, '2020-06-03', 'cancel');
+
+    -- q2 
+  drop table if exists eu_energy;
+  drop table if exists asia_energy;
+  drop table if exists na_energy;
+ 
+  CREATE TABLE eu_energy (
+  date date, 
+  consumption int
+  );
+
+  CREATE TABLE asia_energy (
+  date date, 
+  consumption int
+  );
+
+  CREATE TABLE na_energy (
+  date date, 
+  consumption int
+  );
+
+  insert into eu_energy values ('2019-06-01', 10);
+  insert into eu_energy values ('2019-06-02', 11);
+  insert into eu_energy values ('2019-06-03', 20);
+  insert into eu_energy values ('2019-06-04', 10);
+  insert into eu_energy values ('2019-06-05', 10);
+  insert into eu_energy values ('2019-06-06', 10);
+
+  insert into asia_energy values ('2019-06-01', 44);
+  insert into asia_energy values ('2019-06-02', 30);
+  insert into asia_energy values ('2019-06-03', 12);
+  insert into asia_energy values ('2019-06-04', 30);
+  insert into asia_energy values ('2019-06-05', 30);
+  insert into asia_energy values ('2019-06-06', 44);
+
+  insert into na_energy values ('2019-06-01', 29);
+  insert into na_energy values ('2019-06-02', 30);
+  insert into na_energy values ('2019-06-03', 29);
+  insert into na_energy values ('2019-06-04', 29);
+  insert into na_energy values ('2019-06-05', 29);
+  insert into na_energy values ('2019-06-06', 29);
+
+  -- q3 
+
+  drop table if exists account_status;
+
+  CREATE TABLE account_status (
+  acc_id int, 
+  date date, 
+  status varchar(20) -- (open, closed)
+  );
+
+  insert into account_status values (1, '2020-01-07', 'open');
+  insert into account_status values (2, '2020-01-07', 'open');
+  insert into account_status values (3, '2020-01-07', 'open');
+  insert into account_status values (4, '2020-01-07', 'open');
+  insert into account_status values (1, '2020-01-08', 'open');
+  insert into account_status values (2, '2020-01-08', 'closed');
+  insert into account_status values (3, '2020-01-08', 'open');
+  insert into account_status values (4, '2020-01-08', 'open');
+  insert into account_status values (1, '2020-01-09', 'open');
+  insert into account_status values (2, '2020-01-09', 'closed');
+  insert into account_status values (3, '2020-01-09', 'open');
+  insert into account_status values (4, '2020-01-09', 'closed');
+  insert into account_status values (1, '2020-01-10', 'open');
+  insert into account_status values (2, '2020-01-10', 'open');
+  insert into account_status values (3, '2020-01-10', 'open');
+  insert into account_status values (4, '2020-01-10', 'closed');
+
+  -- q4 
+
+  drop table if exists request_logs;
+
+  CREATE TABLE request_logs (
+  request_id int, 
+  request_date date, 
+  request_status varchar(20), -- ('canceled_by_driver', 'canceled_by_client', 'success')
+  distance_to_travel float, 
+  monetary_cost float, 
+  driver_to_client_distance float
+  );
+
+  insert into request_logs values (1, '2020-01-07', 'success', 15, 22, 4);
+  insert into request_logs values (2, '2020-01-09', 'success', 12, 10, 9);
+  insert into request_logs values (3, '2020-01-11', 'success', 9, 4, 4);
+  insert into request_logs values (4, '2020-01-21', 'canceled_by_driver', 13, 11, 40); 
+  insert into request_logs values (5, '2020-02-07', 'success', 15, 22, 4);
+  insert into request_logs values (6, '2020-02-09', 'canceled_by_client', 20, 32, 50);
+  insert into request_logs values (7, '2020-02-11', 'success', 56, 60, 1);
+  insert into request_logs values (8, '2020-02-21', 'canceled_by_driver', 3, 5, 99);   
+  insert into request_logs values (9, '2020-03-07', 'success', 15, 22, 4);
+  insert into request_logs values (10, '2020-03-09', 'success', 12, 10, 9);
+  insert into request_logs values (11, '2020-03-11', 'success', 9, 4, 4);
+  insert into request_logs values (12, '2020-03-21', 'canceled_by_client', 13, 11, 40); 
+  insert into request_logs values (13, '2020-04-07', 'success', 15, 22, 4);
+  insert into request_logs values (14, '2020-04-09', 'success', 16, 9, 1);
+  insert into request_logs values (15, '2020-04-11', 'success', 18, 25, 6);
+  insert into request_logs values (16, '2020-04-21', 'canceled_by_driver', 88, 121, 6);  
+
+  -- q5
+  drop table if exists twitter_employee;
+
+  CREATE TABLE twitter_employee (
+  id int,
+  first_name varchar(50),
+  last_name varchar(50),
+  age int,
+  sex varchar(10),
+  employee_title varchar(50),
+  department varchar(50),
+  salary int,
+  target int,
+  bonus int,
+  email varchar(50),
+  city varchar(20),
+  address varchar(100),
+  manager_id int
+  );
+
+  insert into twitter_employee values (1, 'Jacob', 'Smith', 28, 'male', 'software engineer', 'tech', 80000, 82000, 10000, 
+    'jacobsmith@twitter.com','bay area', NULL, 2);
+  insert into twitter_employee values (2, 'Sally', 'White', 33, 'female', 'software engineer manager', 'tech', 110000, 120000, 15000, 
+    'sallywhite@twitter.com','chicago', NULL, 4);
+  insert into twitter_employee values (3, 'Arvind', 'Agarwal', 45, 'male', 'software engineer', 'tech', 400000, 420000, 50000, 
+    NULL,'', NULL, 2);
+  insert into twitter_employee values (4, 'Lisa', 'Kim', 28, NULL, 'data scientist', 'tech', 70000, 790000, 12000, 
+    NULL,'charlotte', NULL, 2);
+
+
+  insert into twitter_employee values (5, 'Ajay', 'Khan', 19, NULL, 'product manager intern', 'product', 40000, 40000, NULL, 
+    NULL,'denver', NULL, NULL);
+  insert into twitter_employee values (6, 'Walter', 'White', 25, NULL, 'project manager', 'product', 100000, 100000, NULL, 
+    NULL,'toronto', NULL, NULL);
+  insert into twitter_employee values (7, 'Paris', 'Green', 25, NULL, 'product manager', 'product', 100000, 100000, NULL, 
+    NULL,'toronto', NULL, NULL);
+  insert into twitter_employee values (12, 'Kyle', 'Kyle', 50, NULL, 'product manager', 'product', 1200000, 1200000, NULL, 
+    NULL,'bay area', NULL, NULL);
+
+
+
+  insert into twitter_employee values (8, 'Kelly', 'James', 19, NULL, NULL, 'hr', 40000, 40000, 20000, 
+    NULL,NULL, NULL, NULL);
+  insert into twitter_employee values (9, 'Susan', 'Lee', 43, 'female', NULL, 'hr', 60000, 60000, 20000, 
+    NULL,NULL, NULL, NULL);
+  insert into twitter_employee values (10, 'Jonas', 'Kwan', 25, 'male', NULL, 'hr', 60000, 60000, 20000, 
+    NULL,NULL, NULL, NULL);
+  insert into twitter_employee values (11, 'Amanda', 'Lee', 40, 'non-binary', 'hr lead', 'hr', 300000, 300000, 30000, 
+    NULL,NULL, NULL, NULL);
+
+insert into twitter_employee values (13, 'Beepbeep', 'Boop', 1, 'male', 'cat', 'entertainment', NULL, NULL, NULL, 
+    NULL,'chicago', NULL, NULL);
+  insert into twitter_employee values (14, 'Boop', 'Beepbeep', 1, 'female', 'cat', 'entertainment', NULL, NULL, NULL, 
+    NULL,'chicago', NULL, NULL);
+
+-- q6
+drop table if exists users;
+drop table if exists comments;
+
+  CREATE TABLE users (
+  id int,
+  name varchar(50),
+  joined_at date,
+  city_id int,
+  device int
+  );
+
+  CREATE TABLE comments (
+  user_id int,
+  body varchar(500),
+  created_at date
+  );
+
+  insert into users values (1, 'Bob Smith', '2018-01-23', 12, 9421);
+  insert into users values (2, 'Ashley Sparks', '2019-01-01', 12, 9422);
+  insert into users values (3, 'Donald Rump', '2020-01-14', 11, 9421);
+  insert into users values (4, 'Jacob Smith', '2018-08-31', 31, 9423);
+  insert into users values (5, 'Amanda Leon', '2018-04-18', 31, 9424);
+  insert into users values (6, 'Robert Little', '2018-07-23', 12, 9425);
+  insert into users values (7, 'Robert Big', '2020-01-31', 12, 9426);
+  insert into users values (8, 'Stuart Little', '2017-05-17', 11, 1356);
+  insert into users values (9, 'Samantha Junior', '2018-01-23', 12, 3421);
+  insert into users values (10, 'Kyle Curry', '2018-01-23', 12, 6537);
+  insert into users values (11, 'Jay Kim', '2018-01-21', 12, 2952);
+  insert into users values (12, 'Allen Barbara', '2018-11-25', 12, 6859);
+  insert into users values (13, 'Kelley Patel', '2011-01-01', 11, 5909);
+  insert into users values (14, 'Danielle Kaur', '2019-12-23', 12, 4950);
+
+
+  insert into comments values (1, 'I love cats', '2020-01-23');
+  insert into comments values (1, 'and dogs!', '2020-01-23');
+  insert into comments values (1, 'because there is meaning', '2020-01-23');
+  insert into comments values (7, 'cats are alright', '2020-01-18');
+  insert into comments values (7, 'no cats are great', '2020-01-05');
+  insert into comments values (12, 'I like papayas', '2020-01-21');
+  insert into comments values (12, 'no', '2020-01-31');
+  insert into comments values (12, 'im being nice', '2020-01-05');
+  insert into comments values (4, 'no go away', '2020-01-05');
+  insert into comments values (11, 'good', '2020-01-05');
+  insert into comments values (11, 'jolly good', '2020-05-05');
+  insert into comments values (11, 'cauliflower rice', '2020-11-05');
+  insert into comments values (5, 'data science is great!', '2020-01-11');
+  insert into comments values (11, 'yeah its alright', '2020-01-11');
+
+-- q7
+
+drop table if exists transactions;
+
+  CREATE TABLE transactions (
+  id int,
+  created_at date,
+  value int,
+  purchase_id int
+  );
+
+  insert into transactions values (1, '2020-01-23', 10, 100);
+  insert into transactions values (1, '2020-01-23', 99, 101);
+  insert into transactions values (1, '2020-02-23', 242, 102);
+  insert into transactions values (1, '2020-02-23', 135, 103);
+  insert into transactions values (1, '2020-02-23', 234, 104);
+  insert into transactions values (1, '2020-03-23', 3453, 105);
+  insert into transactions values (1, '2020-04-23', 112, 106);
+  insert into transactions values (1, '2020-04-23', 1453, 107);
+  insert into transactions values (1, '2020-05-23', 4564, 108);
+
+-- q8 = q4 
+
+-- q9 
+drop table if exists web_log;
+
+CREATE TABLE web_log (
+  user_id int,
+  timestamp timestamp,
+  action varchar(20)
+  -- ('page_load', 'page_exit', 'scoll_up', 'scroll_down')
+);
+
+insert into web_log values (1, NOW() - interval '1 hour 30 minutes', 'page_load');
+insert into web_log values (1, NOW() - interval '1 hour 10 minutes', 'page_load');
+insert into web_log values (1, NOW() - interval '1 hour 5 minutes', 'scroll_up');
+insert into web_log values (1, NOW() - interval '1 hour 4 minutes', 'scroll_down');
+insert into web_log values (1, NOW() - interval '1 hour 3 minutes', 'scroll_down');
+insert into web_log values (1, NOW() - interval '1 hour 3 minutes', 'page_exit');
+
+insert into web_log values (2, NOW() - interval '3 hour 30 minutes', 'page_load');
+insert into web_log values (2, NOW() - interval '3 hour 10 minutes', 'page_load');
+insert into web_log values (2, NOW() - interval '2 hour 5 minutes', 'scroll_up');
+insert into web_log values (2, NOW() - interval '2 hour 4 minutes', 'scroll_down');
+insert into web_log values (2, NOW() - interval '2 hour 3 minutes 4 seconds', 'scroll_down');
+insert into web_log values (2, NOW() - interval '2 hour 3 minutes 1 second', 'scroll_down');
+insert into web_log values (2, NOW() - interval '2 hour 3 minutes', 'scroll_down');
+insert into web_log values (2, NOW() - interval '1 hour', 'page_exit');
+insert into web_log values (2, NOW() - interval '1 hour', 'page_exit');
+
+insert into web_log values (3, NOW() - interval '10 days 2 hours 5 minutes', 'scroll_down');
+insert into web_log values (3, NOW() - interval '10 days 2 hours 4 minutes', 'scroll_down');
+
+insert into web_log values (4, NOW() - interval '13 days 21 hours 10 minutes', 'page_load');
+insert into web_log values (4, NOW() - interval '13 days 2 hours 4 minutes', 'scroll_down');
+insert into web_log values (4, NOW() - interval '13 days 2 hours 3 minutes', 'scroll_down');
+
+-- q10 
+
+  DROP TABLE IF EXISTS orders; 
+  DROP TABLE IF EXISTS customers;
+
+  CREATE TABLE customers (
+    id INTEGER,
+    first_name VARCHAR(20) NOT NULL,
+    last_name VARCHAR(10) NOT NULL,
+    city VARCHAR(20),
+    address VARCHAR(50),
+    phone_number VARCHAR(10),
+    PRIMARY KEY (id)
+  );
+
+  CREATE TABLE orders (
+    id INTEGER,
+    cust_id INTEGER NOT NULL,
+    order_date VARCHAR(10) NOT NULL,
+    order_quantity INTEGER NOT NULL,
+    order_details VARCHAR(50),
+    order_cost INTEGER NOT NULL,
+    PRIMARY KEY (id)
+  );
+
+
+  INSERT INTO customers VALUES (1, 'John', 'Adam','London', '2020 Park Drive', '9944563214');
+  INSERT INTO customers VALUES (2, 'Sally','Brown','Chicago', NULL, '9594836054');
+  INSERT INTO customers VALUES (7, 'Jim','Tee','Tokyo', '50 North Street', '9594896054');
+  INSERT INTO customers VALUES (3, 'Pauline', 'Jones', NULL, NULL, NULL);
+  INSERT INTO customers VALUES (4, 'David', 'White', 'Colorado', NULL, NULL);
+  INSERT INTO customers VALUES (5, 'Sal', 'Tan', 'Amsterdam', '10 Amsterdam Road', NULL);
+  INSERT INTO customers VALUES (6, 'Karen', 'Lee', 'Kingston', NULL, '4563978500');
+
+  INSERT INTO orders VALUES (1, 1,'2019-03-04', 5, 'hat', 20);
+  INSERT INTO orders VALUES (2, 1, '2019-03-12', 1, 'sweater', 100);
+  INSERT INTO orders VALUES (3, 3, '2019-04-12', 1, 'sweater', 100);
+  INSERT INTO orders VALUES (4, 3, '2019-04-12', 2, 'coat', 400);
+  INSERT INTO orders VALUES (5, 2, '2019-01-01', 1, 'coat', 400);
+  INSERT INTO orders VALUES (6, 6, '2019-04-01', 3, 'shoes', 100);
+  INSERT INTO orders VALUES (7, 6, '2019-04-01', 3, 'vest', 200);
+  INSERT INTO orders VALUES (8, 6, '2019-04-28', 1, 'vest', 200);
+
+  COMMIT;
\ No newline at end of file