├── 1 Import and Export └── Create Schema and Import Data │ ├── 2_Merge_Join_Concatenate.xlsx │ ├── 2_concatenate_df1.csv │ ├── 2_concatenate_df2.csv │ ├── 2_concatenate_df3.csv │ ├── 2_merge_df1.csv │ ├── 2_merge_df2.csv │ ├── sample_sales.csv │ └── sample_sales_wError.csv ├── 2_Merge_Join_Concatenate.xlsx ├── Banking Case Study ├── Banking Case Study.zip ├── t1_leads.csv ├── t2_city.csv ├── t3_employer.csv ├── t4_bank.csv ├── t5_source.csv └── t6_emi.csv ├── J_Teaching ├── Data.zip ├── Merge Example.zip ├── SQL Code.pdf ├── SQL_Basics_30Mar2020.db ├── SQL_Code_30Mar2020.sql ├── SQL_Session_25Aug2019.sql ├── sample_db1.db └── sample_db1_Relationship Diagram.pdf ├── LEARN SQL Basics though a CASE STUDY Webinar.pdf ├── LEARN SQL Basics though a CASE STUDY Webinar.pptx ├── LICENSE ├── Quiz SQL - Codes and Datasets.zip ├── Quiz SQL Code.pdf ├── README.md ├── SQL L1 course ├── Case Study 1 │ ├── OrderDetails.csv │ ├── Products.csv │ ├── PropertyInfo.csv │ ├── SQL Case Study Code 1 IPBA 11.sql │ ├── SQL Case Study Code 1.sql │ ├── SQL for Data Science.pdf │ ├── SQL for Data Science │ │ ├── CaseStudy_Superstore.mmap │ │ └── Page-2.mmap │ ├── SuperStore.xlsx │ └── superstore.zip ├── Insights 1.sql └── Trials.sql ├── SQL Training ├── 1 Session Retail Case Study │ ├── 1 Session Retail Case Study.zip │ ├── Retail Case Study 24May2020.pptx │ ├── Retail Case Study Business Questions Solution 24 May2020.sql │ ├── Retail Case Study Check Data Solution 24 May2020.sql │ ├── Retail_Data_Customers.csv │ ├── Retail_Data_Transactions.csv │ └── Retail_Data_Transactions_14_15.csv ├── Assignment │ ├── SQL Assignment 1.zip │ ├── SQL Assignment 2.zip │ ├── SQL Assignment 3_IPL .zip │ └── SQL Assignment 4.zip ├── Friends_Example.xlsx ├── Primary_Key_Example_23May2020.xlsx ├── README.md ├── SQL_Join_Returned_Orders_26May2020.sql ├── SQL_Joins_26May2020.sql ├── SQL_SubQuery_CaseStudy_26May2020.sql ├── SQL_SubQuery_Example_26May2020.sql ├── SQL_SubQuery_Question_26May2020.sql ├── SQL_SuperStore_Alter_23May2020.sql ├── SQL_SuperStore_Create_Unique_23May2020.sql ├── SQL_SuperStore_Group_by_23May2020.sql ├── SQL_SuperStore_Home_Work_23May2020.sql └── introductiontosql-161216154706.pdf ├── SQL_Top10_SO ├── .vscode │ └── extensions.json ├── 00_RetailData.ipynb ├── 101_Inner_Outer_join.sql ├── 104_Finding_Duplicates.sql ├── 108_IF_THEN_CONDITION_SQL.sql ├── 108_IF_THEN_CONDITION_SQL_working.sql └── SQLQuery_1.sql ├── SQLite_Browser └── DB.Browser.for.SQLite-3.11.2-win64.msi ├── Titanic SQL ├── Titanic_Data_Cleaning_23Aug2019.sql ├── test.csv ├── titanic.db └── train.csv └── Z Sample Data ├── Excel Tableau Dataset └── Tableau_Dataset_XYZ-1.xlsx ├── SQL TOP SO ├── 101_InnerJoin_OuterJoin.xlsx ├── 101_Q1_Training.csv ├── 101_Q2_Training.csv ├── 104_Name_Location.csv ├── 104_Name_Location.xlsx ├── 108_IF_THEN_CONDITION_SQL.csv └── 108_IF_THEN_Condition_in_SQL.xlsx ├── Super Store Returns ├── Sample - Superstore.xls ├── returned_orders.csv └── superstore_sales.csv ├── SuperStore Normalised ├── superstore_customers.csv └── superstore_orders.csv ├── TR_Data.zip ├── TR_Data ├── TR_OrderDetails.csv ├── TR_Products.csv └── TR_PropertyInfo.csv ├── cricket-world-cup-2019-players-data.zip ├── fifa-world-cup.zip ├── fifa-world-cup ├── WorldCupMatches.csv ├── WorldCupPlayers.csv └── WorldCups.csv ├── ipl.zip └── novel-corona-virus-2019-dataset.zip /1 Import and Export/Create Schema and Import Data/2_Merge_Join_Concatenate.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/1 Import and Export/Create Schema and Import Data/2_Merge_Join_Concatenate.xlsx -------------------------------------------------------------------------------- /1 Import and Export/Create Schema and Import Data/2_concatenate_df1.csv: -------------------------------------------------------------------------------- 1 | ID,Student_Name,Score 2 | 101,Manoj,12 3 | 102,Rakesh,68 4 | 103,Rosy,87 5 | 104,Yogesh,95 6 | -------------------------------------------------------------------------------- /1 Import and Export/Create Schema and Import Data/2_concatenate_df2.csv: -------------------------------------------------------------------------------- 1 | ID,Student_Name,Score 2 | 105,Rahul,69 3 | 106,Anu,73 4 | 107,Rosalin,5 5 | 108,Ankur,13 6 | -------------------------------------------------------------------------------- /1 Import and Export/Create Schema and Import Data/2_concatenate_df3.csv: -------------------------------------------------------------------------------- 1 | ID,Student_Name,Score 2 | 109,Arvind,51 3 | 110,Aradhana,57 4 | 111,Avinash,79 5 | 112,Deepthi,28 6 | -------------------------------------------------------------------------------- /1 Import and Export/Create Schema and Import Data/2_merge_df1.csv: -------------------------------------------------------------------------------- 1 | Cust_ID,Customer 2 | 101,Manoj 3 | 102,Rakesh 4 | 103,Rosy 5 | 104,Yogesh 6 | -------------------------------------------------------------------------------- /1 Import and Export/Create Schema and Import Data/2_merge_df2.csv: -------------------------------------------------------------------------------- 1 | Purchase_ID,Cust_ID,Sales 2 | 101,103,6235 3 | 102,104,7533 4 | 103,102,9427 5 | 104,101,5795 6 | 105,101,4486 7 | 106,102,2750 8 | 107,101,4453 9 | 108,104,6048 10 | 109,101,2915 11 | 110,102,4103 12 | -------------------------------------------------------------------------------- /1 Import and Export/Create Schema and Import Data/sample_sales.csv: -------------------------------------------------------------------------------- 1 | id,SalesRep,Region,Month,Sales,Units Sold 2 | 1,Amy,North,Jan,23040,239 3 | 1,Amy,North,Feb,24131,79 4 | 1,Amy,North,Mar,24646,71 5 | 1,Amy,North,Apr,22047,71 6 | 1,Amy,North,May,24971, 7 | 1,Amy,North,Jun,24218,92 8 | 1,Amy,North,Jul,25735,175 9 | 1,Amy,North,Aug,,87 10 | 1,Amy,North,Sep,25749,557 11 | 1,Amy,North,Oct,24437,95 12 | 1,Amy,North,Nov,25355,706 13 | 1,Amy,North,Dec,25899,180 14 | 1,Bob,North,Jan,20024,103 15 | 1,Bob,North,Feb,23822,267 16 | 1,Bob,North,Mar,24854,96 17 | 1,Bob,North,Apr,22838,74 18 | 1,Bob,North,May,25320,231 19 | 1,Bob,North,Jun,24733,164 20 | 1,Bob,North,Jul,21184,68 21 | 1,Bob,North,Aug,23174,114 22 | 1,Bob,North,Sep,25999,84 23 | 1,Bob,North,Oct,22639,260 24 | 1,Bob,North,Nov,23949,109 25 | 1,Bob,North,Dec,23179,465 26 | 1,Chuck,South,Jan,19886,95 27 | 1,Chuck,South,Feb,23494,148 28 | 1,Chuck,South,Mar,21824,83 29 | 1,Chuck,South,Apr,22058,96 30 | 1,Chuck,South,May,20280,453 31 | 1,Chuck,South,Jun,23965,760 32 | 1,Chuck,South,Jul,23032,155 33 | 1,Chuck,South,Aug,21273,769 34 | 1,Chuck,South,Sep,21584,114 35 | 1,Chuck,South,Oct,19625,83 36 | 1,Chuck,South,Nov,19832,70 37 | 1,Chuck,South,Dec,20583,178 38 | 1,Doug,South,Jan,26264,92 39 | 1,Doug,South,Feb,29953,852 40 | 1,Doug,South,Mar,25041,86 41 | 1,Doug,South,Apr,29338,223 42 | 1,Doug,South,May,25150,242 43 | 1,Doug,South,Jun,27371,95 44 | 1,Doug,South,Jul,25044,82 45 | 1,Doug,South,Aug,29506,103 46 | 1,Doug,South,Sep,29061,146 47 | 1,Doug,South,Oct,27113,120 48 | 1,Doug,South,Nov,25953,81 49 | 1,Doug,South,Dec,28670,198 50 | -------------------------------------------------------------------------------- /1 Import and Export/Create Schema and Import Data/sample_sales_wError.csv: -------------------------------------------------------------------------------- 1 | id,SalesRep,Region,Month,Sales,Units Sold 2 | 1,Amy,North,Jan,23040,239 3 | 1,Amy,North,Feb,24131,79 4 | 1,Amy,North,Mar,24646,71 5 | 1,Amy,North,Apr,22047,71 6 | 1,Amy,North,May,24971, 7 | 1,Amy,North,Jun,24218,92 8 | 1,Amy,North,Jul,25735,175 9 | 1,Amy,North,Aug,,87 10 | 1,Amy,North,Sep,25749,557 11 | 1,Amy,North,Oct,24437,95 12 | 1,Amy,North,Nov,25355,706 13 | 1,Amy,North,Dec,25899,180 14 | 1,Bob,North,Jan,20024,103 15 | 1,Bob,North,Feb,23822,267 16 | 1,Bob,North,Mar,24854,96 17 | 1,Bob,North,Apr,22838,74 18 | 1,Bob,North,May,25320,231 19 | 1,Bob,North,Jun,24733,164 20 | 1,Bob,North,Jul,21184,68 21 | 1,Bob,North,Aug,23174,114 22 | 1,Bob,North,Sep,25999,84 23 | 1,Bob,North,Oct,22639,260 24 | 1,Bob,North,Nov,23949,109 25 | 1,Bob,North,Dec,23179,465 26 | 1,Chuck,South,Jan,19886,95 27 | 1,Chuck,South,Feb,23494,148 28 | 1,Chuck,South,Mar,21824,83 29 | 1,Chuck,South,Apr,22058,96 30 | 1,Chuck,South,May,20280,453 31 | 1,Chuck,South,Jun,23965,760 32 | 1,Chuck,South,Jul,23032,155 33 | 1,Chuck,South,Aug,21273,769 34 | 1,Chuck,South,Sep,21584,114 35 | 1,Chuck,South,Oct,19625,83 36 | 1,Chuck,South,Nov,19832,70 37 | 1,Chuck,South,Dec,20583,178 38 | 1,Doug,South,Jan,26264,92 39 | 1,Doug,South,Feb,29953,852 40 | 1,Doug,South,Mar,25041,86 41 | 1,Doug,South,Apr,29338,223 42 | 1,Doug,South,May,25150,242 43 | 1,Doug,South,Jun,27371,95 44 | 1,Doug,South,Jul,25044,82 45 | 1,Doug,South,Aug,29506,103 46 | 1,Doug,South,Sep,29061,146 47 | 1,Doug,South,Oct,27113,120 48 | 1,Doug,South,Nov,25953,81 49 | 1,Doug,South,Dec,28670,198 50 | -------------------------------------------------------------------------------- /2_Merge_Join_Concatenate.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/2_Merge_Join_Concatenate.xlsx -------------------------------------------------------------------------------- /Banking Case Study/Banking Case Study.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/Banking Case Study/Banking Case Study.zip -------------------------------------------------------------------------------- /Banking Case Study/t2_city.csv: -------------------------------------------------------------------------------- 1 | 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606 | C10446,C 607 | C10571,C 608 | C10648,C 609 | C10714,C 610 | C10465,C 611 | C10483,C 612 | C10450,C 613 | C10585,C 614 | C10328,C 615 | C10489,C 616 | C10358,C 617 | C10514,C 618 | C10559,C 619 | C10631,C 620 | C10507,C 621 | C10654,C 622 | C10493,C 623 | C10674,C 624 | C10635,C 625 | C10412,C 626 | C10482,C 627 | C10496,C 628 | C10534,C 629 | C10539,C 630 | C10599,C 631 | C10617,C 632 | C10602,C 633 | C10646,C 634 | C10663,C 635 | C10543,C 636 | C10390,C 637 | C10555,C 638 | C10628,C 639 | C10597,C 640 | C10689,C 641 | C10580,C 642 | C10709,C 643 | C10531,C 644 | C10595,C 645 | C10661,C 646 | C10529,C 647 | C10554,C 648 | C10600,C 649 | C10667,C 650 | C10638,C 651 | C10702,C 652 | C10593,C 653 | C10680,C 654 | C10641,C 655 | C10713,C 656 | C10526,C 657 | C10591,C 658 | C10553,C 659 | C10574,C 660 | C10592,C 661 | C10682,C 662 | C10550,C 663 | C10583,C 664 | C10542,C 665 | C10485,C 666 | C10650,C 667 | C10658,C 668 | C10435,C 669 | C10596,C 670 | C10692,C 671 | C10563,C 672 | C10612,C 673 | C10717,C 674 | C10626,C 675 | C10693,C 676 | C10673,C 677 | C10685,C 678 | C10676,C 679 | C10686,C 680 | -------------------------------------------------------------------------------- /Banking Case Study/t4_bank.csv: -------------------------------------------------------------------------------- 1 | Customer_Existing_Primary_Bank_Code,Primary_Bank_Type 2 | B001,P 3 | B002,P 4 | B003,G 5 | B014,P 6 | B006,P 7 | B017,G 8 | B004,P 9 | B023,G 10 | B011,P 11 | B022,G 12 | B009,G 13 | B021,P 14 | B016,G 15 | B033,G 16 | B043,P 17 | B005,P 18 | B028,P 19 | B008,G 20 | B047,P 21 | B024,G 22 | B032,G 23 | B010,G 24 | B020,G 25 | B031,G 26 | B025,G 27 | B018,G 28 | B007,G 29 | B012,G 30 | B039,G 31 | B015,P 32 | B026,P 33 | B030,P 34 | B037,G 35 | B045,G 36 | B041,P 37 | B013,G 38 | B044,P 39 | B019,G 40 | B036,G 41 | B042,G 42 | B027,G 43 | B038,P 44 | B034,G 45 | B035,P 46 | B040,P 47 | B029,G 48 | B046,P 49 | B055,G 50 | B049,G 51 | B048,P 52 | B050,P 53 | B051,P 54 | B056,G 55 | B053,P 56 | B054,G 57 | B052,P 58 | B057,P 59 | -------------------------------------------------------------------------------- /Banking Case Study/t5_source.csv: -------------------------------------------------------------------------------- 1 | Source,Source_Category 2 | S122,G 3 | S143,B 4 | S134,B 5 | S133,B 6 | S122,C 7 | S122,B 8 | S122,E 9 | S133,E 10 | S133,F 11 | S133,C 12 | S159,B 13 | S122,F 14 | S137,B 15 | S159,C 16 | S143,C 17 | S143,F 18 | S127,B 19 | S144,B 20 | S123,B 21 | S156,B 22 | S151,B 23 | S134,C 24 | S127,C 25 | S144,C 26 | S143,E 27 | S153,C 28 | S124,B 29 | S153,B 30 | S134,E 31 | S151,C 32 | S161,C 33 | S122,D 34 | S137,C 35 | S139,B 36 | S154,A 37 | S161,B 38 | S157,B 39 | S156,C 40 | S134,F 41 | S162,B 42 | S162,C 43 | S141,B 44 | S158,B 45 | S157,C 46 | S129,B 47 | S129,C 48 | S136,B 49 | S141,C 50 | S130,B 51 | S138,A 52 | S155,B 53 | S158,C 54 | S150,B 55 | S160,B 56 | S135,B 57 | S160,C 58 | S140,B 59 | 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-------------------------------------------------------------------------------- /J_Teaching/SQL_Basics_30Mar2020.db: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/J_Teaching/SQL_Basics_30Mar2020.db -------------------------------------------------------------------------------- /J_Teaching/SQL_Code_30Mar2020.sql: -------------------------------------------------------------------------------- 1 | Select 1 + 9; 2 | 3 | -- Select 4 | -- Distinct, limit 5 | -- Sort 6 | -- Where 7 | 8 | select OrderId 9 | , OrderDate 10 | , OrderPriority 11 | , Sales 12 | , ShipMode 13 | from SalesData 14 | where OrderPriority = "High" 15 | order by Sales DESC limit 10; 16 | 17 | 18 | select OrderId 19 | , OrderDate 20 | , OrderPriority 21 | , Sales 22 | , ShipMode 23 | from SalesData 24 | where OrderPriority = "High" and Sales > 20000 25 | order by Sales DESC; 26 | 27 | 28 | select OrderId 29 | , OrderDate 30 | , OrderPriority 31 | , Sales 32 | , Profit 33 | , ShipMode 34 | from SalesData 35 | where OrderPriority = "High" 36 | order by Profit DESC Limit 15; 37 | 38 | 39 | -- Group by = Excel (Pivot) 40 | 41 | Select 42 | OrderPriority 43 | , count(RowID) as No_Orders 44 | from SalesData 45 | where Sales > 10000 46 | group by OrderPriority 47 | order by 2 DESC; 48 | 49 | 50 | -- Having 51 | 52 | Select 53 | OrderPriority 54 | , count(RowID) as No_Orders 55 | from SalesData 56 | group by OrderPriority 57 | having No_Orders > 1700 58 | order by 2 DESC; 59 | 60 | 61 | -- Maximum Sales by which Product Category? 62 | 63 | select 64 | ProductCategory 65 | , Sum(Sales) as Total_Sales 66 | from SalesData 67 | group by ProductCategory; 68 | 69 | select 70 | [ProductSub-Category] 71 | , Sum(Sales) as Total_Sales 72 | from SalesData 73 | group by [ProductSub-Category] 74 | having Total_Sales > 1000000 75 | order by 2 DESC; 76 | 77 | -- Joins 78 | 79 | -- Join Example 80 | select 81 | b.Cust_ID, b.Sales, a.Customer 82 | from merge_df2 b 83 | left join merge_df1 a on a.Cust_ID = b.Cust_ID 84 | 85 | 86 | -- Case Study 87 | 88 | Select o.* -- all columns from OrderDetails 89 | , p.ProductName, p.ProductCategory, p.Price 90 | , o.Quantity * p.Price as Sales 91 | from OrderDetails o 92 | left join Products p on o.ProductID = p.ProductID 93 | ; 94 | 95 | 96 | Select p.ProductName, sum(o.Quantity * p.Price) as Sales 97 | from OrderDetails o 98 | left join Products p on o.ProductID = p.ProductID 99 | group by p.ProductName 100 | order by 2 DESC 101 | ; 102 | -------------------------------------------------------------------------------- /J_Teaching/SQL_Session_25Aug2019.sql: -------------------------------------------------------------------------------- 1 | /* This is how you comment 2 | multiple lines at a time 3 | Got it!! */ 4 | 5 | -- You can use the SELECT statement to perform a simple 6 | -- calculation as follows: 7 | SELECT 8 | 1 + 4; 9 | -- SOLVE - ? How do you give name to column here? 10 | 11 | -- You can use multiple expressions in the SELECT statement as follows: 12 | -- Select a few columns from tracks table in DB 13 | SELECT 14 | trackid, 15 | name, 16 | composer, 17 | unitprice 18 | FROM 19 | tracks; 20 | 21 | /* SOLVE- ? How to select All the columns from tracks table? 22 | - Way/s - Which one is easier and which is recommended? */ 23 | 24 | -- Get the cities from where customer belongs 25 | 26 | 27 | 28 | SELECT 29 | city 30 | FROM 31 | customers; 32 | -- SOLVE - ? What is the issue with above query? 33 | 34 | 35 | -- Use of DISTINCT 36 | SELECT DISTINCT 37 | city 38 | FROM 39 | customers; 40 | 41 | -- SOLVE - ? Get the companies of the customers 42 | -- What is unusual about result? 43 | 44 | SELECT 45 | company 46 | FROM 47 | customers; 48 | 49 | SELECT DISTINCT 50 | company 51 | FROM 52 | customers; 53 | 54 | -- USE OF ORDER BY 55 | -- Get data - name, milliseconds, and album id columns form tracks 56 | SELECT 57 | name, 58 | milliseconds, 59 | albumid 60 | FROM 61 | tracks; 62 | 63 | -- Now get the same data sorted based on AlbumId column in ascending order 64 | SELECT 65 | name, 66 | milliseconds, 67 | albumid 68 | FROM 69 | tracks 70 | ORDER BY 71 | albumid ASC; 72 | 73 | -- Now sort the sorted result (by AlbumId) above... 74 | -- by the Milliseconds column in descending order 75 | 76 | SELECT 77 | name, 78 | milliseconds, 79 | albumid 80 | FROM 81 | tracks 82 | ORDER BY 83 | albumid ASC, 84 | milliseconds DESC; 85 | 86 | -- Using column positions in ORDER BY 87 | SELECT 88 | name, 89 | milliseconds, 90 | albumid 91 | FROM 92 | tracks 93 | ORDER BY 94 | 3,2; 95 | 96 | -- Use of LIMIT clause 97 | -- get the first 10 rows in the tracks table 98 | SELECT 99 | trackId, 100 | name 101 | FROM 102 | tracks 103 | LIMIT 10; 104 | 105 | -- get 10 rows starting from the 10th row in the tracks table 106 | SELECT 107 | trackId, 108 | name 109 | FROM 110 | tracks 111 | LIMIT 10 OFFSET 10; 112 | 113 | -- SOLVE: get the top 10 largest tracks in MB 114 | 115 | 116 | 117 | 118 | SELECT 119 | trackid, 120 | name, 121 | bytes 122 | FROM 123 | tracks 124 | ORDER BY 125 | bytes DESC 126 | LIMIT 10; 127 | 128 | -- SOLVE NUGGET - ? Get all customers and their first name where company is missing 129 | -- select customerid, firstname, company 130 | --from customers 131 | --where company is NULL; 132 | 133 | --Use of where clause 134 | -- Get all tracks in the album id 1 135 | 136 | SELECT 137 | name, 138 | milliseconds, 139 | bytes, 140 | albumid 141 | FROM 142 | tracks 143 | WHERE 144 | albumid = 1; 145 | 146 | -- SOLVE: Get tracks on the album 1 that have the length greater than 250,000 milliseconds 147 | 148 | 149 | 150 | SELECT 151 | name, 152 | milliseconds, 153 | bytes, 154 | albumid 155 | FROM 156 | tracks 157 | WHERE 158 | albumid = 1 159 | AND milliseconds > 250000; 160 | 161 | 162 | -- Find which tracks are composed by all people with 'Smith' in thier names 163 | -- Wildcard characters 164 | SELECT 165 | name, 166 | albumid, 167 | composer 168 | FROM 169 | tracks 170 | WHERE 171 | composer LIKE '%Smith%' 172 | ORDER BY 173 | albumid; 174 | 175 | -- Use of wildcards % and _ 176 | SELECT 177 | trackid, 178 | name 179 | FROM 180 | tracks 181 | WHERE 182 | name LIKE 'Wild%'; 183 | 184 | SELECT 185 | trackid, 186 | name 187 | FROM 188 | tracks 189 | WHERE 190 | name LIKE '%Wild'; 191 | 192 | SELECT 193 | trackid, 194 | name 195 | FROM 196 | tracks 197 | WHERE 198 | name LIKE '%Wild%'; 199 | 200 | 201 | -- SOLVE: Find tracks that have media type id 2 or 3 202 | 203 | 204 | SELECT 205 | name, 206 | albumid, 207 | mediatypeid 208 | FROM 209 | tracks 210 | WHERE 211 | mediatypeid = 2 or mediatypeid = 3; 212 | 213 | -- SOLVE: What if I have to chose data for more than 25 different media types (hypothetically)? 214 | 215 | 216 | 217 | --Use of IN operator and Not In operator 218 | SELECT 219 | name, albumid, mediatypeid 220 | FROM 221 | tracks 222 | WHERE 223 | mediatypeid IN (2, 3); 224 | 225 | -- list of tracks whose genre id is not in a list of (1,2,3) 226 | SELECT 227 | trackid, 228 | name, 229 | genreid 230 | FROM 231 | tracks 232 | WHERE 233 | genreid NOT IN (1,2,3); 234 | 235 | 236 | -- Use of BETWEEN operator 237 | -- finds invoices whose total is between 14.91 and 18.86: 238 | SELECT 239 | InvoiceId, 240 | BillingAddress, 241 | Total 242 | FROM 243 | invoices 244 | WHERE 245 | Total BETWEEN 14.91 and 18.86 246 | ORDER BY 247 | Total; 248 | 249 | 250 | -- SOLVE: find the invoices whose total are less than 1 or greater than 20 251 | 252 | 253 | 254 | 255 | 256 | SELECT 257 | InvoiceId, 258 | BillingAddress, 259 | Total 260 | FROM 261 | invoices 262 | WHERE 263 | Total NOT BETWEEN 1 and 20 264 | ORDER BY 265 | Total; 266 | 267 | 268 | -- finds invoices whose invoice dates are from January 1 2010 and January 31 2010: 269 | SELECT 270 | InvoiceId, 271 | BillingAddress, 272 | InvoiceDate, 273 | Total 274 | FROM 275 | invoices 276 | WHERE 277 | InvoiceDate BETWEEN '2010-01-01' AND '2010-01-31' 278 | ORDER BY 279 | InvoiceDate; 280 | 281 | 282 | 283 | -- Get the tracks that belong to the artist id = 12 284 | -- Subqueries 285 | 286 | 287 | SELECT 288 | trackid, 289 | name, 290 | albumid 291 | FROM 292 | tracks 293 | WHERE 294 | albumid IN ( 295 | SELECT 296 | albumid 297 | FROM 298 | albums 299 | WHERE 300 | artistid = 12 301 | ); 302 | 303 | 304 | 305 | -- JOINS 306 | -- Inner join 307 | 308 | -- get the album titles for all tracks 309 | SELECT 310 | trackid, 311 | name, 312 | title 313 | FROM 314 | tracks 315 | INNER JOIN albums ON albums.albumid = tracks.albumid; 316 | 317 | 318 | 319 | -- How do you validate join is correct? 320 | SELECT 321 | trackid, 322 | name, 323 | tracks.albumid AS album_id_tracks, 324 | albums.albumid AS album_id_albums, 325 | title 326 | FROM 327 | tracks 328 | INNER JOIN albums ON albums.albumid = tracks.albumid; 329 | 330 | 331 | -- SOLVE - ? How to get tracks and albums of artist with ID = 10 332 | 333 | 334 | 335 | 336 | SELECT 337 | trackid, 338 | tracks.name AS Track, 339 | albums.title AS Album, 340 | artists.name AS Artist 341 | FROM 342 | tracks 343 | INNER JOIN albums ON albums.albumid = tracks.albumid 344 | INNER JOIN artists ON artists.artistid = albums.artistid 345 | WHERE 346 | artists.artistid = 10; 347 | 348 | 349 | -- SOLVE: Find the artists who do not have any albums 350 | 351 | 352 | SELECT 353 | artists.ArtistId, 354 | albumId 355 | FROM 356 | artists 357 | LEFT JOIN albums ON albums.artistid = artists.artistid 358 | ORDER BY 359 | albumid; 360 | 361 | --OR 362 | SELECT 363 | artists.ArtistId, 364 | albumId 365 | FROM 366 | artists 367 | LEFT JOIN albums ON albums.artistid = artists.artistid 368 | WHERE 369 | albumid IS NULL; 370 | 371 | 372 | -- Note - SQLite does not support RIGHT JOIN and FULL OUTER join 373 | 374 | -- GROUP BY 375 | -- Find the number of tracks per album 376 | 377 | SELECT 378 | albumid, 379 | COUNT(trackid) as track_count 380 | FROM 381 | tracks 382 | GROUP BY 383 | albumid; 384 | 385 | -- order above result by count of tracks 386 | SELECT 387 | albumid, 388 | COUNT(trackid) as track_count 389 | FROM 390 | tracks 391 | GROUP BY 392 | albumid 393 | ORDER BY 2 DESC; 394 | 395 | -- Get the number of tracks per album along with album title 396 | 397 | select a.albumID , b.title, count (trackid) as track_count 398 | from 399 | tracks as a 400 | left join 401 | albums as b on a.albumid = b.albumid 402 | group by 1,2; 403 | 404 | -- GROUP BY with HAVING clause - to filter by aggregate value in group by 405 | -- get the albums that have more than 15 tracks 406 | 407 | SELECT 408 | tracks.albumid, 409 | title, 410 | COUNT(trackid) 411 | FROM 412 | tracks 413 | INNER JOIN albums ON albums.albumid = tracks.albumid 414 | GROUP BY 415 | tracks.albumid 416 | HAVING COUNT(trackid) > 15; 417 | 418 | -- Get total length and bytes for each album 419 | SELECT 420 | albumid, 421 | sum(milliseconds) as length, 422 | sum(bytes) as size 423 | FROM 424 | tracks 425 | GROUP BY 426 | albumid; 427 | 428 | -- Get the album id, album title, maximum, minimum and the average length of tracks 429 | SELECT 430 | tracks.albumid, 431 | title, 432 | min(milliseconds), 433 | max(milliseconds), 434 | round(avg(milliseconds),2) 435 | FROM 436 | tracks 437 | INNER JOIN albums ON albums.albumid = tracks.albumid 438 | GROUP BY 439 | tracks.albumid; 440 | 441 | -- Get count of tracks by media type and genre 442 | SELECT 443 | mediatypeid, 444 | genreid, 445 | count(trackid) 446 | FROM 447 | tracks 448 | GROUP BY 449 | mediatypeid, 450 | genreid; 451 | 452 | -- find albums that have the total length greater than 60,000,000 milliseconds 453 | SELECT 454 | tracks.albumid, 455 | title, 456 | sum(Milliseconds) AS length 457 | FROM 458 | tracks 459 | INNER JOIN albums ON albums.AlbumId = tracks.AlbumId 460 | GROUP BY 461 | tracks.albumid 462 | HAVING 463 | length > 60000000; 464 | 465 | -- SQL Subquery 466 | -- Find all the tracks in the album with the title Let There Be Rock 467 | 468 | SELECT trackid, 469 | name, 470 | tracks.albumid 471 | FROM tracks left join albums 472 | on tracks.albumid = albums.albumid 473 | where albums.Title = 'Let There Be Rock'; 474 | 475 | --OR 476 | SELECT trackid, 477 | name, 478 | albumid 479 | FROM tracks 480 | WHERE albumid = ( 481 | SELECT albumid 482 | FROM albums 483 | WHERE title = 'Let There Be Rock' 484 | ); 485 | 486 | 487 | 488 | -- Find the customers whose sales representatives are in Canada 489 | SELECT customerid, 490 | firstname, 491 | lastname 492 | FROM customers 493 | WHERE supportrepid IN ( 494 | SELECT employeeid 495 | FROM employees 496 | WHERE country = 'Canada' 497 | ); 498 | 499 | --OR 500 | SELECT a.customerid, 501 | a.firstname, 502 | a.lastname 503 | FROM customers as a LEFT join employees as b 504 | on a.SupportRepId = b.EmployeeID 505 | where b.country = 'Canada'; 506 | 507 | 508 | -- Get average album size in bytes 509 | SELECT avg(album.size) 510 | FROM ( 511 | SELECT sum(bytes) as size 512 | FROM tracks 513 | GROUP BY albumid 514 | ) 515 | AS album; 516 | 517 | -- Correlate Subquery?? Let' not cover in scope 518 | 519 | -- INSERT ROWS in TABLES 520 | 521 | -- single row 522 | INSERT INTO artists (name) 523 | VALUES 524 | ('Bud Powell'); 525 | 526 | -- multiple rows 527 | INSERT INTO artists (name) 528 | VALUES 529 | ("Buddy Rich"), 530 | ("Candido"), 531 | ("Charlie Byrd"); 532 | 533 | -- create backup of artists 534 | CREATE TABLE artists_backup( 535 | artistid INTEGER PRIMARY KEY AUTOINCREMENT, 536 | name NVARCHAR 537 | ); 538 | INSERT INTO artists_backup SELECT 539 | artistid, 540 | name 541 | FROM 542 | artists; 543 | 544 | -- SQL Update 545 | -- Update the last name of Jane (emp id = 3) as she got married to a Smith 546 | UPDATE employees 547 | SET lastname = 'Peacock' 548 | WHERE 549 | employeeid = 3; 550 | 551 | /* Suppose Park Margaret locates in Toronto and you want to change his address, 552 | city, and state information.*/ 553 | 554 | UPDATE employees 555 | SET city = 'Toronto', 556 | state = 'ON', 557 | postalcode = 'M5P 2N7' 558 | WHERE 559 | employeeid = 4; 560 | 561 | -- Deleting rows from table 562 | DELETE 563 | FROM 564 | artists_backup 565 | WHERE 566 | artistid = 1; 567 | -- Delete based on condition 568 | DELETE 569 | FROM 570 | artists_backup 571 | WHERE 572 | name LIKE '%Santana%'; 573 | 574 | -- Removing all rows of database 575 | DELETE 576 | FROM 577 | artists_backup; 578 | 579 | -- Dropping a table 580 | drop table artists_backup; -------------------------------------------------------------------------------- /J_Teaching/sample_db1.db: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/J_Teaching/sample_db1.db -------------------------------------------------------------------------------- /J_Teaching/sample_db1_Relationship Diagram.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/J_Teaching/sample_db1_Relationship Diagram.pdf -------------------------------------------------------------------------------- /LEARN SQL Basics though a CASE STUDY Webinar.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/LEARN SQL Basics though a CASE STUDY Webinar.pdf -------------------------------------------------------------------------------- /LEARN SQL Basics though a CASE STUDY Webinar.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/LEARN SQL Basics though a CASE STUDY Webinar.pptx -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020 Kunaal Naik 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Quiz SQL - Codes and Datasets.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/Quiz SQL - Codes and Datasets.zip -------------------------------------------------------------------------------- /Quiz SQL Code.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/Quiz SQL Code.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # YT_SQL_for_DataScience 2 | An SQL course for Data Science and Business Professionals 3 | -------------------------------------------------------------------------------- /SQL L1 course/Case Study 1/Products.csv: -------------------------------------------------------------------------------- 1 | ProductID,ProductName,ProductCategory,Price 2 | 1,Large Towel,Housekeeping,9 3 | 2,Hand Towel,Housekeeping,5 4 | 3,Washcloth,Housekeeping,3 5 | 4,Shampoo,Housekeeping,40 6 | 5,Moisturizer,Housekeeping,40 7 | 6,Conditioner,Housekeeping,40 8 | 7,Hand Soap,Housekeeping,35 9 | 8,Bath Soap,Housekeeping,35 10 | 9,Tissues,Housekeeping,14 11 | 10,Toilet Paper,Housekeeping,19 12 | 11,Shower Cap,Housekeeping,10 13 | 12,Bed Sheet (King),Housekeeping,29 14 | 13,Bed Sheet (Double),Housekeeping,20 15 | 14,Pillowcase,Housekeeping,15 16 | 15,Drinking Glass,Housekeeping,24 17 | 16,Mouthwash,Housekeeping,40 18 | 17,Clothes Hanger,Housekeeping,18 19 | 18,Coffee Cup,Housekeeping,24 20 | 19,Light Bulb,Housekeeping,17 21 | 20,Pens,Office Supplies,30 22 | 21,Printer Paper,Office Supplies,50 23 | 22,Paper Clips,Office Supplies,3 24 | 23,Printer Toner,Office Supplies,85 25 | 24,Envelopes (Letter),Office Supplies,24 26 | 25,Envelopes (Legal),Office Supplies,28 27 | 26,Envelopes (Catalog),Office Supplies,13 28 | 27,Invisible Tape,Office Supplies,8 29 | 28,Staples,Office Supplies,8 30 | 29,Erasable Markers,Office Supplies,6 31 | 30,Permanent Markers,Office Supplies,6 32 | 31,Sticky Notes,Office Supplies,20 33 | 32,Note Pads,Office Supplies,14 34 | 33,Towel Rack,Furnishings,10 35 | 34,Shower Curtain,Furnishings,5 36 | 35,Tissue Box,Furnishings,25 37 | 36,Armoire,Furnishings,115 38 | 37,Bed (King),Furnishings,300 39 | 38,Bed (Double),Furnishings,250 40 | 39,Alarm Clock,Furnishings,45 41 | 40,Coffee Maker,Furnishings,119 42 | 41,Office Chair,Furnishings,85 43 | 42,Reading Chair,Furnishings,70 44 | 43,Table Lamp,Furnishings,38 45 | 44,Floor Lamp,Furnishings,55 46 | 45,Computer Desk,Furnishings,102 47 | 46,Safe,Furnishings,115 48 | 47,Swivel Chair,Furnishings,75 49 | 48,Side Table,Furnishings,40 50 | 49,Large Vase,Furnishings,27 51 | 50,Small Vase,Furnishings,33 52 | 51,Painting,Furnishings,85 53 | 52,Ironing Board,Furnishings,18 54 | 53,Iron,Furnishings,20 55 | 54,Chest of Drawers,Furnishings,150 56 | 55,Television,Furnishings,200 57 | 56,Telephone,Furnishings,38 58 | 57,Wireless Router,Furnishings,70 59 | 58,Mirror,Furnishings,85 60 | 59,Screws,Maintenance,14 61 | 60,Portable Drill,Maintenance,195 62 | 61,Tape Measure,Maintenance,8 63 | 62,Leaf Blower,Maintenance,202 64 | 63,Duct Tape,Maintenance,27 65 | 64,Gaffer's Tape,Maintenance,27 66 | 65,Ladder,Maintenance,89 67 | 66,Wood Glue,Maintenance,21 68 | 67,Adjustable Wrench,Maintenance,10 69 | 68,Window Screen,Maintenance,30 70 | 69,Door Knob,Maintenance,41 71 | 70,Door Lock,Maintenance,117 72 | 71,Hammer,Maintenance,17 73 | 72,Nails,Maintenance,12 74 | 73,Mop,Maintenance,15 75 | 74,Large Plant,Public Areas,35 76 | 75,Small Plant,Public Areas,25 77 | 76,Ficus,Public Areas,80 78 | 77,Large Area Rug,Public Areas,40 79 | 78,Small Area Rug,Public Areas,20 80 | 79,Coffee Table,Public Areas,103 81 | 80,Poster Stand,Public Areas,27 82 | 81,Flyer Holder,Public Areas,3 83 | 82,Business Cards,Public Areas,60 84 | 83,Bar Stool,Public Areas,60 85 | 84,Tall Chair,Public Areas,75 86 | 85,Low Chair,Public Areas,80 87 | 86,Sofa,Public Areas,215 88 | 87,End Table,Public Areas,30 89 | 88,Serving Tray,Public Areas,6 90 | 89,Photograph,Public Areas,45 91 | 90,Luggage Cart,Public Areas,138 92 | 91,Small Sign,Public Areas,14 93 | 92,Large Sign,Public Areas,20 94 | 93,Terminal,Public Areas,85 95 | 94,Monitor,Public Areas,70 96 | -------------------------------------------------------------------------------- /SQL L1 course/Case Study 1/PropertyInfo.csv: -------------------------------------------------------------------------------- 1 | Prop ID,PropertyCity,PropertyState 2 | 1,New York,New York 3 | 2,Cincinnati,Ohio 4 | 3,Portland,Oregon 5 | 4,Seattle,Washington 6 | 5,Kansas City,Missouri 7 | 6,Orlando,Florida 8 | 7,Boston,Massachusetts 9 | 8,Philadelphia,Pennsylvania 10 | 9,Richmond,Virginia 11 | 10,Arlington,Virginia 12 | 11,Atlanta,Georgia 13 | 12,Dallas,Texas 14 | 13,New Orleans,Louisiana 15 | 14,Nashville,Tennessee 16 | 15,Chicago,Illinois 17 | 16,Denver,Colorado 18 | 17,Las Vegas,Nevada 19 | 18,Phoenix,Arizona 20 | 19,Los Angeles,California 21 | 20,San Francisco,California 22 | -------------------------------------------------------------------------------- /SQL L1 course/Case Study 1/SQL Case Study Code 1 IPBA 11.sql: -------------------------------------------------------------------------------- 1 | # Insights Stage 1 2 | 3 | select * 4 | from orderdetails 5 | order by Quantity DESC 6 | LIMIT 10 ; 7 | 8 | # Provides Unique Product Ids 9 | select distinct ProductID 10 | from orderdetails; 11 | 12 | # Provides Unique Product Count 13 | select count(distinct ProductID) 14 | from orderdetails; 15 | 16 | # Insight Stage 2 17 | 18 | # Top 5 19 | SELECT ProductID 20 | , sum(Quantity) as Total_Quantity 21 | FROM orderdetails 22 | GROUP BY ProductID 23 | ORDER BY 2 DESC; 24 | 25 | # Top 5 26 | SELECT ProductID 27 | , sum(Quantity) as Total_Quantity 28 | FROM orderdetails 29 | GROUP BY ProductID 30 | ORDER BY 2; # default ascending order 31 | 32 | 33 | # Insight Stage 3 - Joins 34 | 35 | SELECT * 36 | from orderdetails o 37 | left join products p on o.ProductID = p.ProductID 38 | left join propertyinfo prop on o.PropertyID = prop.`Prop ID`; 39 | 40 | # Notation 41 | #superstore.orderdetails.OrderID 42 | #orderdetails.OrderDate 43 | #orderdetails.PropertyID 44 | #sample_db.sample_sales.id 45 | 46 | SELECT * 47 | FROM orderdetails 48 | left join products on orderdetails.ProductID = products.ProductID 49 | left join propertyinfo on orderdetails.PropertyID = propertyinfo.`Prop ID`; 50 | 51 | # with alias table name 52 | SELECT * 53 | FROM orderdetails o 54 | left join products p on o.ProductID = p.ProductID 55 | left join propertyinfo prop on o.PropertyID = prop.`Prop ID`; 56 | 57 | #create sales 58 | SELECT *, o.Quantity * p.Price as Sales 59 | FROM orderdetails o 60 | left join products p on o.ProductID = p.ProductID 61 | left join propertyinfo prop on o.PropertyID = prop.`Prop ID`; 62 | 63 | # Create a table using a Query 64 | create table superstore_combined as 65 | SELECT o.*, prop.* 66 | , p.ProductName 67 | , p.ProductCategory 68 | , p.Price 69 | , o.Quantity * p.Price as Sales 70 | FROM orderdetails o 71 | left join products p on o.ProductID = p.ProductID 72 | left join propertyinfo prop on o.PropertyID = prop.`Prop ID`; 73 | 74 | # permanent table 75 | select * from superstore_combined ; 76 | 77 | # Remove one column 78 | ALTER TABLE superstore_combined 79 | DROP COLUMN `Prop ID`; 80 | 81 | # Insights Stage 3 - Having and Dates 82 | create temporary table superstore_temp as 83 | select * 84 | , STR_TO_DATE(OrderDate, '%d-%m-%Y') as OrdDate 85 | , YEAR(STR_TO_DATE(OrderDate, '%d-%m-%Y')) as OrdYear 86 | , MONTH(STR_TO_DATE(OrderDate, '%d-%m-%Y')) as OrdMonth 87 | from superstore_combined ; 88 | 89 | # Having on Temp Data 90 | select OrdYear, sum(Sales) as Total_Sales 91 | from superstore_temp 92 | group by OrdYear; 93 | 94 | select OrdMonth, sum(Sales) as Total_Sales 95 | from superstore_temp 96 | where OrdYear = 2016 97 | group by OrdMonth 98 | having Total_Sales > 120000 99 | order by 2 100 | ; 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 114 | -------------------------------------------------------------------------------- /SQL L1 course/Case Study 1/SQL Case Study Code 1.sql: -------------------------------------------------------------------------------- 1 | ## Order Details 2 | 3 | Select * from orderdetails; 4 | Select * from products; 5 | 6 | ## Insights 1 7 | Select * 8 | from OrderDetails 9 | order by Quantity DESC 10 | limit 10 ; 11 | 12 | Select distinct ProductId from OrderDetails; 13 | Select distinct propertyid from OrderDetails; 14 | 15 | ## Insights 3 16 | create table joined_orders AS 17 | ( 18 | select o.* 19 | , p.ProductName 20 | , p.ProductCategory 21 | , p.Price 22 | , o.Quantity * p.Price as Sales 23 | from OrderDetails o 24 | left join products p on o.`ProductID` = p.`ProductID` 25 | ) ; 26 | 27 | drop table joined_orders; 28 | create table joined_orders AS 29 | ( 30 | select o.* 31 | , p.ProductName 32 | , p.ProductCategory 33 | , p.Price 34 | , o.Quantity * p.Price as Sales 35 | , right(o.OrderDate, 4) * 1 as Year 36 | from OrderDetails o 37 | left join products p on o.`ProductID` = p.`ProductID` 38 | ) ; 39 | 40 | select 41 | Year, sum(Sales) as Total_Sales 42 | from joined_orders 43 | GROUP by Year 44 | 45 | ## Having 46 | select 47 | ProductCategory, sum(Sales) as Total_Sales 48 | from joined_orders 49 | GROUP by ProductCategory 50 | having Total_Sales > 500000; 51 | 52 | 53 | 54 | 55 | 56 | 57 | -------------------------------------------------------------------------------- /SQL L1 course/Case Study 1/SQL for Data Science.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL L1 course/Case Study 1/SQL for Data Science.pdf -------------------------------------------------------------------------------- /SQL L1 course/Case Study 1/SQL for Data Science/CaseStudy_Superstore.mmap: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL L1 course/Case Study 1/SQL for Data Science/CaseStudy_Superstore.mmap -------------------------------------------------------------------------------- /SQL L1 course/Case Study 1/SQL for Data Science/Page-2.mmap: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL L1 course/Case Study 1/SQL for Data Science/Page-2.mmap -------------------------------------------------------------------------------- /SQL L1 course/Case Study 1/SuperStore.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL L1 course/Case Study 1/SuperStore.xlsx -------------------------------------------------------------------------------- /SQL L1 course/Case Study 1/superstore.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL L1 course/Case Study 1/superstore.zip -------------------------------------------------------------------------------- /SQL L1 course/Insights 1.sql: -------------------------------------------------------------------------------- 1 | -- USE : dbo.OrderDetails 2 | Select * from dbo.OrderDetails 3 | 4 | -- 1/ Select top 10 transactions 5 | 6 | 7 | -- 2/ Select first transaction within each Product 8 | 9 | 10 | -- 3/ Select last transaction within each Product 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | ----------------------------------------------------------------- 41 | /* 42 | Now its your turn 43 | 44 | 1/ Select first transaction within each category 45 | 2/ Select last transaction within each category 46 | 47 | */ -------------------------------------------------------------------------------- /SQL L1 course/Trials.sql: -------------------------------------------------------------------------------- 1 | 2 | select * into #temp_summary from ( 3 | select *, YEAR(OrderDate) as YEAR from dbo.OrderDetails 4 | ) temp 5 | 6 | select YEAR, sum(Quantity) as Quantity from dbo.#temp_summary group by Year -------------------------------------------------------------------------------- /SQL Training/1 Session Retail Case Study/1 Session Retail Case Study.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL Training/1 Session Retail Case Study/1 Session Retail Case Study.zip -------------------------------------------------------------------------------- /SQL Training/1 Session Retail Case Study/Retail Case Study 24May2020.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL Training/1 Session Retail Case Study/Retail Case Study 24May2020.pptx -------------------------------------------------------------------------------- /SQL Training/1 Session Retail Case Study/Retail Case Study Business Questions Solution 24 May2020.sql: -------------------------------------------------------------------------------- 1 | # Did the transactions increase Year on Year? 2 | SELECT 3 | * 4 | #, STR_TO_DATE(trans_date,'%d,%MMM,%Y') 5 | , right(trans_date,2) + 2000 as Year 6 | #, year(trans_date) as Year 7 | FROM retail_data_transactions 8 | limit 5; 9 | 10 | #Year on Year 11 | SELECT 12 | right(trans_date,2) + 2000 as Year 13 | , count(customer_id) as purchases 14 | FROM retail_data_transactions 15 | group by right(trans_date,2) + 2000 16 | order by 1 asc 17 | ; 18 | 19 | SELECT trans_date,RIGHT(trans_date,2) + 2000 as Year 20 | FROM retail_data_transactions 21 | limit 5; 22 | 23 | 24 | 25 | 26 | 27 | -------------------------------------------------------------------------------- /SQL Training/1 Session Retail Case Study/Retail Case Study Check Data Solution 24 May2020.sql: -------------------------------------------------------------------------------- 1 | # Find the number of Customers 2 | SELECT 3 | count(distinct customer_id) 4 | FROM retail_data_transactions; 5 | 6 | # Total transactions in the data 7 | SELECT 8 | count(*) 9 | FROM retail_data_transactions; 10 | 11 | # Top 5 Customers with Maximum Sales 12 | SELECT 13 | customer_id 14 | , sum(tran_amount) as tran_amount 15 | FROM retail_data_transactions 16 | group by customer_id 17 | ORDER by tran_amount desc limit 5; 18 | 19 | # Top 5 Customers with Maximum Purchases 20 | SELECT 21 | customer_id 22 | , count(tran_amount) as purchases 23 | FROM retail_data_transactions 24 | group by customer_id 25 | ORDER by 2 desc limit 5; 26 | 27 | 28 | -------------------------------------------------------------------------------- /SQL Training/Assignment/SQL Assignment 1.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL Training/Assignment/SQL Assignment 1.zip -------------------------------------------------------------------------------- /SQL Training/Assignment/SQL Assignment 2.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL Training/Assignment/SQL Assignment 2.zip -------------------------------------------------------------------------------- /SQL Training/Assignment/SQL Assignment 3_IPL .zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL Training/Assignment/SQL Assignment 3_IPL .zip -------------------------------------------------------------------------------- /SQL Training/Assignment/SQL Assignment 4.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL Training/Assignment/SQL Assignment 4.zip -------------------------------------------------------------------------------- /SQL Training/Friends_Example.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL Training/Friends_Example.xlsx -------------------------------------------------------------------------------- /SQL Training/Primary_Key_Example_23May2020.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL Training/Primary_Key_Example_23May2020.xlsx -------------------------------------------------------------------------------- /SQL Training/README.md: -------------------------------------------------------------------------------- 1 | # Important Links 2 | 3 | - [Replace](https://www.w3schools.com/sql/func_mysql_replace.asp) 4 | - [Pivot](https://codingsight.com/pivot-tables-in-mysql/) 5 | - [Split Columns](https://www.tutorialspoint.com/how-to-split-a-column-in-mysql) 6 | -------------------------------------------------------------------------------- /SQL Training/SQL_Join_Returned_Orders_26May2020.sql: -------------------------------------------------------------------------------- 1 | # Find Customers who returned the order 2 | SELECT DISTINCT 3 | #S.*, 4 | C.`Customer Name`, R.Returned 5 | FROM superstore.superstore_sales as S 6 | left join superstore.superstore_customers as C on S.`Customer ID` = C.`Customer ID` 7 | left join superstore.returned_orders R on S.`Order ID` = R.`Order Id` 8 | where R.Returned = "Yes" 9 | ; 10 | 11 | SELECT 12 | S.*, C.`Customer Name`, R.Returned 13 | FROM superstore.superstore_sales as S 14 | left join superstore.superstore_customers as C on S.`Customer ID` = C.`Customer ID` 15 | left join superstore.returned_orders R on S.`Order ID` = R.`Order Id` 16 | ; -------------------------------------------------------------------------------- /SQL Training/SQL_Joins_26May2020.sql: -------------------------------------------------------------------------------- 1 | #SELECT * FROM retail_case_study_1.tr_orderdetails; 2 | 3 | SELECT 4 | ProductID 5 | , sum(Quantity) as Quantity 6 | FROM retail_case_study_1.tr_orderdetails 7 | group by ProductID 8 | order by 2 desc; 9 | 10 | SELECT 11 | A.*, B.ProductName, B.ProductCategory, B.Price 12 | FROM retail_case_study_1.tr_orderdetails as A 13 | LEFT JOIN retail_case_study_1.tr_products as B 14 | on A.ProductID = B.ProductID 15 | ; 16 | 17 | # Highest Quantity Sold 18 | SELECT 19 | A.ProductID 20 | , B.ProductName 21 | , sum(A.Quantity) as Quantity 22 | FROM retail_case_study_1.tr_orderdetails as A 23 | LEFT JOIN retail_case_study_1.tr_products as B 24 | on A.ProductID = B.ProductID 25 | group by B.ProductName 26 | order by 3 desc 27 | ; 28 | 29 | # Top 5 and Botton 5 in One Table 30 | SELECT 31 | A.ProductID 32 | , B.ProductName 33 | , sum(A.Quantity) as Quantity 34 | FROM retail_case_study_1.tr_orderdetails as A 35 | LEFT JOIN retail_case_study_1.tr_products as B 36 | on A.ProductID = B.ProductID 37 | group by B.ProductName 38 | order by 3 desc 39 | LIMIT 5; 40 | 41 | SELECT 42 | A.ProductID 43 | , B.ProductName 44 | , sum(A.Quantity) as Quantity 45 | FROM retail_case_study_1.tr_orderdetails as A 46 | LEFT JOIN retail_case_study_1.tr_products as B 47 | on A.ProductID = B.ProductID 48 | group by B.ProductName 49 | order by 3 asc 50 | LIMIT 5 51 | ; 52 | 53 | 54 | 55 | 56 | 57 | -------------------------------------------------------------------------------- /SQL Training/SQL_SubQuery_CaseStudy_26May2020.sql: -------------------------------------------------------------------------------- 1 | select 2 | ProductID, 3 | sum(Quantity) as Quantity 4 | from retail_case_study_1.tr_orderdetails 5 | group by ProductID 6 | order by 2 DESC; 7 | 8 | 9 | select 10 | a.ProductID 11 | , b.ProductName 12 | , sum(a.Quantity) as Tot_Quan 13 | from retail_case_study_1.tr_orderdetails a 14 | left join retail_case_study_1.tr_products b on a.ProductID = b.ProductID 15 | group by 16 | a.ProductID 17 | , b.ProductName 18 | order by 3 desc 19 | ; 20 | 21 | select 22 | a.ProductID 23 | , b.ProductName 24 | , sum(a.Quantity) as Tot_Quan 25 | , sum(a.Quantity*b.Price) as Sales 26 | from retail_case_study_1.tr_orderdetails a 27 | left join retail_case_study_1.tr_products b on a.ProductID = b.ProductID 28 | group by 29 | a.ProductID 30 | , b.ProductName 31 | order by 4 desc 32 | ; 33 | 34 | 35 | select 36 | a.PropertyID 37 | , b.PropertyCity 38 | , sum(a.Quantity*c.Price) as Sales 39 | from retail_case_study_1.tr_orderdetails a 40 | left join retail_case_study_1.tr_propertyinfo b on a.PropertyID = b.`Prop ID` 41 | left join retail_case_study_1.tr_products c on a.ProductID = c.ProductID 42 | #where a.Quantity*c.Price > 600 43 | group by 44 | a.PropertyID 45 | , b.PropertyCity 46 | having Sales > 27000 47 | order by 3 desc 48 | ; 49 | 50 | 51 | select * from tr_orderdetails 52 | where ProductID in (select productID from tr_top5_quantity) 53 | 54 | 55 | select * from tr_orderdetails 56 | where ProductId in (select distinct productID from tr_orderdetails where Quantity = 2) 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | 74 | 75 | 76 | 77 | -------------------------------------------------------------------------------- /SQL Training/SQL_SubQuery_Example_26May2020.sql: -------------------------------------------------------------------------------- 1 | # Top 5 and Botton 5 in One Table 2 | create table tr_top5_quantity as 3 | SELECT 4 | A.ProductID 5 | , B.ProductName 6 | , sum(A.Quantity) as Quantity 7 | FROM retail_case_study_1.tr_orderdetails as A 8 | LEFT JOIN retail_case_study_1.tr_products as B 9 | on A.ProductID = B.ProductID 10 | group by B.ProductName 11 | order by 3 desc 12 | LIMIT 5; 13 | 14 | select * from tr_top5_quantity; 15 | 16 | # Where Condition 17 | select * 18 | from retail_case_study_1.tr_orderdetails 19 | where ProductID in (78,12,58,60,90); 20 | 21 | # Sub Query Condition 22 | select * 23 | from retail_case_study_1.tr_orderdetails 24 | where ProductID in ( 25 | select ProductID from tr_top5_quantity 26 | ); -------------------------------------------------------------------------------- /SQL Training/SQL_SubQuery_Question_26May2020.sql: -------------------------------------------------------------------------------- 1 | # Top 5 City in Sales 2 | create table tr_top5_city as 3 | SELECT a.PropertyID , b.PropertyCity 4 | , SUM(a.Quantity) AS Quantity 5 | FROM tr_orderdetails AS a 6 | LEFT JOIN tr_propertyinfo AS b ON a.PropertyID = b.`Prop Id` 7 | group by b.PropertyCity 8 | ORDER BY quantity LIMIT 5; 9 | 10 | # Get All tranactions for the Top 5 City 11 | select * 12 | from retail_case_study_1.tr_orderdetails 13 | where PropertyID in ( 14 | select PropertyID from tr_top5_city 15 | ); -------------------------------------------------------------------------------- /SQL Training/SQL_SuperStore_Alter_23May2020.sql: -------------------------------------------------------------------------------- 1 | # Alter Table Example 2 | 3 | create table superstore_sales_bk as 4 | SELECT * from superstore_sales; 5 | 6 | ALTER TABLE `superstore`.`superstore_sales` DROP COLUMN `Customer Name`; 7 | ALTER TABLE `superstore`.`superstore_sales` DROP COLUMN Segment; 8 | ALTER TABLE `superstore`.`superstore_sales` DROP COLUMN City; 9 | ALTER TABLE `superstore`.`superstore_sales` DROP COLUMN State; 10 | ALTER TABLE `superstore`.`superstore_sales` DROP COLUMN Country; 11 | ALTER TABLE `superstore`.`superstore_sales` DROP COLUMN Region; 12 | ALTER TABLE `superstore`.`superstore_sales` DROP COLUMN `Postal Code` ; -------------------------------------------------------------------------------- /SQL Training/SQL_SuperStore_Create_Unique_23May2020.sql: -------------------------------------------------------------------------------- 1 | # Create and Unique Example 2 | 3 | create table superstore_customers as 4 | SELECT 5 | distinct `Customer ID` 6 | , `Customer Name` 7 | , Segment 8 | , City 9 | , State 10 | , Country 11 | , Region 12 | , `Postal Code` 13 | FROM superstore.superstore_sales; 14 | 15 | 16 | -------------------------------------------------------------------------------- /SQL Training/SQL_SuperStore_Group_by_23May2020.sql: -------------------------------------------------------------------------------- 1 | create view vw_superstore_orders as 2 | SELECT 3 | `Order Id` 4 | , max(`Order Date`) as `Order Date` 5 | , max(`Ship Date`) as `Ship Date` 6 | , `Ship Mode` 7 | , count(`Product id`) as Product_Count 8 | , sum(Sales) as Sales 9 | , Sum(Quantity) as Quantity 10 | , sum(Profit) as Profit 11 | FROM superstore.superstore_sales 12 | group by 13 | `Order Id` 14 | , `Ship Mode` 15 | ; -------------------------------------------------------------------------------- /SQL Training/SQL_SuperStore_Home_Work_23May2020.sql: -------------------------------------------------------------------------------- 1 | # Home Work 2 | 3 | Create table superstore_product as 4 | select 5 | distinct `Product Id` 6 | , category 7 | , `sub-category` 8 | , `product name` 9 | #, sales/quantity as Price 10 | from superstore_sales; 11 | 12 | ALTER TABLE `superstore`.`superstore_sales` DROP COLUMN category; 13 | ALTER TABLE `superstore`.`superstore_sales` DROP COLUMN `sub-category`; 14 | ALTER TABLE `superstore`.`superstore_sales` DROP COLUMN `product name`; 15 | 16 | -------------------------------------------------------------------------------- /SQL Training/introductiontosql-161216154706.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQL Training/introductiontosql-161216154706.pdf -------------------------------------------------------------------------------- /SQL_Top10_SO/.vscode/extensions.json: -------------------------------------------------------------------------------- 1 | { 2 | "recommendations": [ 3 | "r0tenur.schema-visualization" 4 | ] 5 | } -------------------------------------------------------------------------------- /SQL_Top10_SO/00_RetailData.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "metadata": { 3 | "kernelspec": { 4 | "name": "SQL", 5 | "display_name": "SQL", 6 | "language": "sql" 7 | }, 8 | "language_info": { 9 | "name": "sql", 10 | "version": "" 11 | } 12 | }, 13 | "nbformat_minor": 2, 14 | "nbformat": 4, 15 | "cells": [ 16 | { 17 | "cell_type": "markdown", 18 | "source": [ 19 | "# Select Data" 20 | ], 21 | "metadata": { 22 | "azdata_cell_guid": "136a45ef-5366-40ee-b402-24c502bfbd96" 23 | }, 24 | "attachments": {} 25 | }, 26 | { 27 | "cell_type": "code", 28 | "source": [ 29 | "Select top 10 * from dbo.retail_OrderDetails" 30 | ], 31 | "metadata": { 32 | "azdata_cell_guid": "ab4ae1a7-0d85-4012-bbbe-7507412adb8d" 33 | }, 34 | "outputs": [ 35 | { 36 | "output_type": "display_data", 37 | "data": { 38 | "text/html": "(10 rows affected)" 39 | }, 40 | "metadata": {} 41 | }, 42 | { 43 | "output_type": "display_data", 44 | "data": { 45 | "text/html": "Total execution time: 00:00:00.009" 46 | }, 47 | "metadata": {} 48 | }, 49 | { 50 | "output_type": "execute_result", 51 | "execution_count": 6, 52 | "data": { 53 | "application/vnd.dataresource+json": { 54 | "schema": { 55 | "fields": [ 56 | { 57 | "name": "OrderID" 58 | }, 59 | { 60 | "name": "OrderDate" 61 | }, 62 | { 63 | "name": "PropertyID" 64 | }, 65 | { 66 | "name": "ProductID" 67 | }, 68 | { 69 | "name": "Quantity" 70 | }, 71 | { 72 | "name": "Sales" 73 | }, 74 | { 75 | "name": "ProductName" 76 | }, 77 | { 78 | "name": "ProductCategory" 79 | }, 80 | { 81 | "name": "Price" 82 | }, 83 | { 84 | "name": "PropertyCity" 85 | }, 86 | { 87 | "name": "PropertyState" 88 | } 89 | ] 90 | }, 91 | "data": [ 92 | { 93 | "0": "1", 94 | "1": "2015-01-01", 95 | "2": "17", 96 | "3": "41", 97 | "4": "1", 98 | "5": "85", 99 | "6": "Office Chair", 100 | "7": "Furnishings", 101 | "8": "85", 102 | "9": "Las Vegas", 103 | "10": "Nevada" 104 | }, 105 | { 106 | "0": "2", 107 | "1": "2015-01-01", 108 | "2": "15", 109 | "3": "54", 110 | "4": "2", 111 | "5": "300", 112 | "6": "Chest of Drawers", 113 | "7": "Furnishings", 114 | "8": "150", 115 | "9": "Chicago", 116 | "10": "Illinois" 117 | }, 118 | { 119 | "0": "3", 120 | "1": "2015-01-01", 121 | "2": "5", 122 | "3": "71", 123 | "4": "2", 124 | "5": "34", 125 | "6": "Hammer", 126 | "7": "Maintenance", 127 | "8": "17", 128 | "9": "Kansas City", 129 | "10": "Missouri" 130 | }, 131 | { 132 | "0": "4", 133 | "1": "2015-01-01", 134 | "2": "19", 135 | "3": "3", 136 | "4": "2", 137 | "5": "6", 138 | "6": "Washcloth", 139 | "7": "Housekeeping", 140 | "8": "3", 141 | "9": "Los Angeles", 142 | "10": "California" 143 | }, 144 | { 145 | "0": "5", 146 | "1": "2015-01-01", 147 | "2": "12", 148 | "3": "45", 149 | "4": "3", 150 | "5": "306", 151 | "6": "Computer Desk", 152 | "7": "Furnishings", 153 | "8": "102", 154 | "9": "Dallas", 155 | "10": "Texas" 156 | }, 157 | { 158 | "0": "6", 159 | "1": "2015-01-01", 160 | "2": "6", 161 | "3": "94", 162 | "4": "2", 163 | "5": "140", 164 | "6": "Monitor", 165 | "7": "Public Areas", 166 | "8": "70", 167 | "9": "Orlando", 168 | "10": "Florida" 169 | }, 170 | { 171 | "0": "7", 172 | "1": "2015-01-01", 173 | "2": "5", 174 | "3": "35", 175 | "4": "2", 176 | "5": "50", 177 | "6": "Tissue Box", 178 | "7": "Furnishings", 179 | "8": "25", 180 | "9": "Kansas City", 181 | "10": "Missouri" 182 | }, 183 | { 184 | "0": "8", 185 | "1": "2015-01-02", 186 | "2": "16", 187 | "3": "93", 188 | "4": "1", 189 | "5": "85", 190 | "6": "Terminal", 191 | "7": "Public Areas", 192 | "8": "85", 193 | "9": "Denver", 194 | "10": "Colorado" 195 | }, 196 | { 197 | "0": "9", 198 | "1": "2015-01-02", 199 | "2": "4", 200 | "3": "93", 201 | "4": "3", 202 | "5": "255", 203 | "6": "Terminal", 204 | "7": "Public Areas", 205 | "8": "85", 206 | "9": "Seattle", 207 | "10": "Washington" 208 | }, 209 | { 210 | "0": "10", 211 | "1": "2015-01-02", 212 | "2": "2", 213 | "3": "61", 214 | "4": "2", 215 | "5": "16", 216 | "6": "Tape Measure", 217 | "7": "Maintenance", 218 | "8": "8", 219 | "9": "Cincinnati", 220 | "10": "Ohio" 221 | } 222 | ] 223 | }, 224 | "text/html": "
OrderIDOrderDatePropertyIDProductIDQuantitySalesProductNameProductCategoryPricePropertyCityPropertyState
12015-01-011741185Office ChairFurnishings85Las VegasNevada
22015-01-0115542300Chest of DrawersFurnishings150ChicagoIllinois
32015-01-01571234HammerMaintenance17Kansas CityMissouri
42015-01-0119326WashclothHousekeeping3Los AngelesCalifornia
52015-01-0112453306Computer DeskFurnishings102DallasTexas
62015-01-016942140MonitorPublic Areas70OrlandoFlorida
72015-01-01535250Tissue BoxFurnishings25Kansas CityMissouri
82015-01-021693185TerminalPublic Areas85DenverColorado
92015-01-024933255TerminalPublic Areas85SeattleWashington
102015-01-02261216Tape MeasureMaintenance8CincinnatiOhio
" 225 | }, 226 | "metadata": {} 227 | } 228 | ], 229 | "execution_count": 6 230 | }, 231 | { 232 | "cell_type": "code", 233 | "source": [ 234 | "Select top 10 * from dbo.retail_Products" 235 | ], 236 | "metadata": { 237 | "azdata_cell_guid": "a1c43122-e0d2-40af-b9f0-32a0d297648d" 238 | }, 239 | "outputs": [ 240 | { 241 | "output_type": "display_data", 242 | "data": { 243 | "text/html": "(10 rows affected)" 244 | }, 245 | "metadata": {} 246 | }, 247 | { 248 | "output_type": "display_data", 249 | "data": { 250 | "text/html": "Total execution time: 00:00:00.004" 251 | }, 252 | "metadata": {} 253 | }, 254 | { 255 | "output_type": "execute_result", 256 | "execution_count": 8, 257 | "data": { 258 | "application/vnd.dataresource+json": { 259 | "schema": { 260 | "fields": [ 261 | { 262 | "name": "ProductID" 263 | }, 264 | { 265 | "name": "ProductName" 266 | }, 267 | { 268 | "name": "ProductCategory" 269 | }, 270 | { 271 | "name": "Price" 272 | } 273 | ] 274 | }, 275 | "data": [ 276 | { 277 | "0": "1", 278 | "1": "Large Towel", 279 | "2": "Housekeeping", 280 | "3": "9" 281 | }, 282 | { 283 | "0": "2", 284 | "1": "Hand Towel", 285 | "2": "Housekeeping", 286 | "3": "5" 287 | }, 288 | { 289 | "0": "3", 290 | "1": "Washcloth", 291 | "2": "Housekeeping", 292 | "3": "3" 293 | }, 294 | { 295 | "0": "4", 296 | "1": "Shampoo", 297 | "2": "Housekeeping", 298 | "3": "40" 299 | }, 300 | { 301 | "0": "5", 302 | "1": "Moisturizer", 303 | "2": "Housekeeping", 304 | "3": "40" 305 | }, 306 | { 307 | "0": "6", 308 | "1": "Conditioner", 309 | "2": "Housekeeping", 310 | "3": "40" 311 | }, 312 | { 313 | "0": "7", 314 | "1": "Hand Soap", 315 | "2": "Housekeeping", 316 | "3": "35" 317 | }, 318 | { 319 | "0": "8", 320 | "1": "Bath Soap", 321 | "2": "Housekeeping", 322 | "3": "35" 323 | }, 324 | { 325 | "0": "9", 326 | "1": "Tissues", 327 | "2": "Housekeeping", 328 | "3": "14" 329 | }, 330 | { 331 | "0": "10", 332 | "1": "Toilet Paper", 333 | "2": "Housekeeping", 334 | "3": "19" 335 | } 336 | ] 337 | }, 338 | "text/html": "
ProductIDProductNameProductCategoryPrice
1Large TowelHousekeeping9
2Hand TowelHousekeeping5
3WashclothHousekeeping3
4ShampooHousekeeping40
5MoisturizerHousekeeping40
6ConditionerHousekeeping40
7Hand SoapHousekeeping35
8Bath SoapHousekeeping35
9TissuesHousekeeping14
10Toilet PaperHousekeeping19
" 339 | }, 340 | "metadata": {} 341 | } 342 | ], 343 | "execution_count": 8 344 | }, 345 | { 346 | "cell_type": "code", 347 | "source": [ 348 | "Select top 10 * from dbo.retail_PropertyInfo" 349 | ], 350 | "metadata": { 351 | "azdata_cell_guid": "7f50c319-96a9-482c-b3a8-dd9578b9b082" 352 | }, 353 | "outputs": [ 354 | { 355 | "output_type": "display_data", 356 | "data": { 357 | "text/html": "(10 rows affected)" 358 | }, 359 | "metadata": {} 360 | }, 361 | { 362 | "output_type": "display_data", 363 | "data": { 364 | "text/html": "Total execution time: 00:00:00.003" 365 | }, 366 | "metadata": {} 367 | }, 368 | { 369 | "output_type": "execute_result", 370 | "execution_count": 10, 371 | "data": { 372 | "application/vnd.dataresource+json": { 373 | "schema": { 374 | "fields": [ 375 | { 376 | "name": "Prop_ID" 377 | }, 378 | { 379 | "name": "PropertyCity" 380 | }, 381 | { 382 | "name": "PropertyState" 383 | } 384 | ] 385 | }, 386 | "data": [ 387 | { 388 | "0": "1", 389 | "1": "New York", 390 | "2": "New York" 391 | }, 392 | { 393 | "0": "2", 394 | "1": "Cincinnati", 395 | "2": "Ohio" 396 | }, 397 | { 398 | "0": "3", 399 | "1": "Portland", 400 | "2": "Oregon" 401 | }, 402 | { 403 | "0": "4", 404 | "1": "Seattle", 405 | "2": "Washington" 406 | }, 407 | { 408 | "0": "5", 409 | "1": "Kansas City", 410 | "2": "Missouri" 411 | }, 412 | { 413 | "0": "6", 414 | "1": "Orlando", 415 | "2": "Florida" 416 | }, 417 | { 418 | "0": "7", 419 | "1": "Boston", 420 | "2": "Massachusetts" 421 | }, 422 | { 423 | "0": "8", 424 | "1": "Philadelphia", 425 | "2": "Pennsylvania" 426 | }, 427 | { 428 | "0": "9", 429 | "1": "Richmond", 430 | "2": "Virginia" 431 | }, 432 | { 433 | "0": "10", 434 | "1": "Arlington", 435 | "2": "Virginia" 436 | } 437 | ] 438 | }, 439 | "text/html": "
Prop_IDPropertyCityPropertyState
1New YorkNew York
2CincinnatiOhio
3PortlandOregon
4SeattleWashington
5Kansas CityMissouri
6OrlandoFlorida
7BostonMassachusetts
8PhiladelphiaPennsylvania
9RichmondVirginia
10ArlingtonVirginia
" 440 | }, 441 | "metadata": {} 442 | } 443 | ], 444 | "execution_count": 10 445 | } 446 | ] 447 | } -------------------------------------------------------------------------------- /SQL_Top10_SO/101_Inner_Outer_join.sql: -------------------------------------------------------------------------------- 1 | -- Quarter 1 training 2 | select * from dbo.Q1_Training 3 | 4 | -- Quarter 2 training 5 | select * from dbo.Q2_Training 6 | 7 | -- Consecutive Attendees 8 | select * 9 | from dbo.Q1_Training q1 10 | inner join dbo.Q2_Training q2 on q1.Name = q2.Name 11 | 12 | -- Create New Table 13 | select * into dbo.Consecutive from 14 | ( 15 | select * 16 | from dbo.Q1_Training q1 17 | inner join dbo.Q2_Training q2 on q1.Name = q2.Name 18 | ) temp 19 | 20 | -- Select Required Columns and Rename Duplicate ones 21 | select q1.Name 22 | , q1.Trainings as Q1_Trainings 23 | , q2.Trainings as Q2_Trainings 24 | from dbo.Q1_Training q1 25 | inner join dbo.Q2_Training q2 on q1.Name = q2.Name 26 | 27 | -- Create table with Unique Names 28 | drop table dbo.Consecutive 29 | select * into dbo.Consecutive from 30 | ( 31 | select q1.Name 32 | , q1.Trainings as Q1_Trainings 33 | , q2.Trainings as Q2_Trainings 34 | from dbo.Q1_Training q1 35 | inner join dbo.Q2_Training q2 on q1.Name = q2.Name 36 | ) temp 37 | 38 | --Check Data 39 | select Name 40 | , Q1_Trainings + Q1_Trainings as Total_Trainings 41 | from dbo.Consecutive 42 | -------------------------------------------------------------------------------- /SQL_Top10_SO/104_Finding_Duplicates.sql: -------------------------------------------------------------------------------- 1 | -- Finding Duplicate values using SQL table 2 | 3 | -- Check Data 4 | Select * 5 | FROM dbo.Data_wDuplicates 6 | 7 | -- Find/Identify Duplicates (with Group by) 8 | Select Name, Location, COUNT(*) as CNT 9 | FROM dbo.Data_wDuplicates 10 | GROUP BY Name, Location 11 | HAVING COUNT(*) > 1 12 | 13 | -- Removing Duplicates from Data (without Group by) 14 | select ID, Name, Location from ( 15 | Select * 16 | , ROW_NUMBER() OVER (PARTITION BY Name, Location ORDER BY ID DESC) as CNT 17 | FROM dbo.Data_wDuplicates 18 | ) temp 19 | where CNT = 1 20 | 21 | 22 | 23 | 24 | 25 | -------------------------------------------------------------------------------- /SQL_Top10_SO/108_IF_THEN_CONDITION_SQL.sql: -------------------------------------------------------------------------------- 1 | -- Check Data 2 | Select * from dbo.eCommerce_Customer_Revenue 3 | 4 | -- IF THEN 5 | -- ** Use Single Quotes for creating Segements 6 | Select * 7 | , IIF(Revenue >= 7000, 'High', 'Rest') as Segement 8 | from dbo.eCommerce_Customer_Revenue 9 | 10 | -- Multiple IF THEN 11 | -- IIF is not valid for SQL Server 2008 R2 and any version before that. 12 | Select * 13 | , IIF(Revenue >= 7000, 'High', IFF(Revenue >= 4000 and Revenue < 7000, 'Medium', 'Low')) as Segement 14 | from dbo.eCommerce_Customer_Revenue 15 | 16 | -- CASE WHEN 17 | Select * 18 | , Case when Revenue >= 7000 then 'High' 19 | when Revenue >= 4000 and Revenue < 7000 then 'Medium' 20 | Else 'Low' 21 | end as Segment 22 | from dbo.eCommerce_Customer_Revenue 23 | -------------------------------------------------------------------------------- /SQL_Top10_SO/108_IF_THEN_CONDITION_SQL_working.sql: -------------------------------------------------------------------------------- 1 | -- Check Data 2 | Select * from dbo.eCommerce_Customer_Revenue 3 | 4 | 5 | 6 | 7 | -- IF THEN 8 | -- ** Use Single Quotes for creating Segements 9 | Select * 10 | , as Segment 11 | from dbo.eCommerce_Customer_Revenue 12 | 13 | 14 | 15 | 16 | -- Multiple IF THEN 17 | -- IIF is not valid for SQL Server 2008 R2 and any version before that. 18 | Select * 19 | , as Segment 20 | from dbo.eCommerce_Customer_Revenue 21 | 22 | 23 | 24 | 25 | -- CASE WHEN 26 | Select * 27 | , as Segment 28 | from dbo.eCommerce_Customer_Revenue 29 | -------------------------------------------------------------------------------- /SQL_Top10_SO/SQLQuery_1.sql: -------------------------------------------------------------------------------- 1 | Select 2 | ProductCategory 3 | , SUM(Sales) as Total_Sales 4 | from [dbo].[retail_OrderDetails] 5 | group by ProductCategory 6 | -------------------------------------------------------------------------------- /SQLite_Browser/DB.Browser.for.SQLite-3.11.2-win64.msi: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/SQLite_Browser/DB.Browser.for.SQLite-3.11.2-win64.msi -------------------------------------------------------------------------------- /Titanic SQL/Titanic_Data_Cleaning_23Aug2019.sql: -------------------------------------------------------------------------------- 1 | select * from train limit 10; 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Yes,CA-2014-152345 5 | Yes,CA-2015-156440 6 | Yes,US-2017-155999 7 | Yes,CA-2014-157924 8 | Yes,CA-2017-131807 9 | Yes,CA-2016-124527 10 | Yes,CA-2017-135692 11 | Yes,CA-2014-123225 12 | Yes,CA-2017-145772 13 | Yes,US-2014-105137 14 | Yes,CA-2017-101805 15 | Yes,CA-2016-111682 16 | Yes,CA-2017-131492 17 | Yes,CA-2015-104129 18 | Yes,CA-2017-117926 19 | Yes,US-2016-115952 20 | Yes,CA-2015-155761 21 | Yes,CA-2017-100111 22 | Yes,CA-2014-156349 23 | Yes,CA-2016-118899 24 | Yes,CA-2017-108294 25 | Yes,US-2017-123834 26 | Yes,CA-2015-168480 27 | Yes,CA-2017-122007 28 | Yes,CA-2017-128965 29 | Yes,CA-2015-169397 30 | Yes,CA-2015-168564 31 | Yes,CA-2014-102652 32 | Yes,CA-2016-112340 33 | Yes,CA-2016-114727 34 | Yes,US-2016-151827 35 | Yes,CA-2016-152814 36 | Yes,US-2016-114230 37 | Yes,CA-2015-146486 38 | Yes,CA-2015-116092 39 | Yes,CA-2017-118542 40 | Yes,CA-2015-140984 41 | Yes,CA-2017-127306 42 | Yes,CA-2017-119284 43 | Yes,CA-2017-150609 44 | Yes,CA-2017-136651 45 | Yes,CA-2017-136539 46 | Yes,CA-2014-110786 47 | Yes,CA-2014-126403 48 | Yes,CA-2016-157280 49 | Yes,CA-2016-162138 50 | Yes,CA-2016-114307 51 | Yes,CA-2014-123498 52 | Yes,CA-2016-142398 53 | Yes,CA-2017-161956 54 | Yes,CA-2017-134194 55 | Yes,CA-2015-134075 56 | Yes,US-2016-156986 57 | Yes,US-2016-105578 58 | Yes,US-2016-131149 59 | Yes,US-2017-147886 60 | Yes,CA-2014-126361 61 | Yes,CA-2017-141929 62 | Yes,CA-2016-145583 63 | Yes,CA-2015-154970 64 | Yes,CA-2016-167759 65 | Yes,CA-2017-109085 66 | Yes,CA-2014-142769 67 | Yes,US-2017-107888 68 | Yes,CA-2014-109918 69 | Yes,CA-2017-131618 70 | Yes,US-2017-109253 71 | Yes,CA-2017-130631 72 | Yes,US-2017-118087 73 | Yes,CA-2016-126732 74 | Yes,US-2016-144057 75 | Yes,CA-2016-168921 76 | Yes,CA-2016-109806 77 | Yes,CA-2014-104829 78 | Yes,CA-2016-147375 79 | Yes,CA-2014-134726 80 | Yes,CA-2017-117513 81 | Yes,US-2014-138758 82 | Yes,CA-2014-126522 83 | Yes,CA-2017-115994 84 | Yes,CA-2016-138282 85 | Yes,CA-2017-161459 86 | Yes,CA-2016-136483 87 | Yes,CA-2016-116547 88 | Yes,CA-2015-130785 89 | Yes,CA-2017-165008 90 | Yes,US-2015-137008 91 | Yes,CA-2017-169894 92 | Yes,CA-2014-105270 93 | Yes,CA-2016-165330 94 | Yes,CA-2014-111871 95 | Yes,CA-2015-157812 96 | Yes,CA-2016-145982 97 | Yes,CA-2017-166142 98 | Yes,CA-2014-127131 99 | Yes,CA-2017-143084 100 | Yes,CA-2015-151547 101 | Yes,CA-2015-138674 102 | Yes,CA-2014-169019 103 | Yes,CA-2014-100762 104 | Yes,US-2014-164406 105 | Yes,CA-2016-118500 106 | Yes,CA-2015-143490 107 | Yes,CA-2017-115427 108 | Yes,CA-2015-124058 109 | Yes,CA-2017-132346 110 | Yes,CA-2016-150077 111 | Yes,CA-2017-124401 112 | Yes,CA-2017-137414 113 | Yes,CA-2017-142867 114 | Yes,CA-2017-157196 115 | Yes,CA-2015-149342 116 | Yes,CA-2016-112123 117 | Yes,US-2017-147998 118 | Yes,CA-2015-103716 119 | Yes,CA-2015-144267 120 | Yes,CA-2016-109869 121 | Yes,CA-2017-166898 122 | Yes,CA-2017-169327 123 | Yes,CA-2017-169859 124 | Yes,CA-2017-137085 125 | Yes,CA-2017-154074 126 | Yes,CA-2016-130477 127 | Yes,CA-2016-134775 128 | Yes,US-2017-105046 129 | Yes,CA-2016-136924 130 | Yes,CA-2017-166093 131 | Yes,CA-2016-106950 132 | Yes,CA-2017-145128 133 | Yes,US-2016-114293 134 | Yes,CA-2017-137099 135 | Yes,CA-2016-133319 136 | Yes,CA-2014-103744 137 | Yes,CA-2015-161627 138 | Yes,CA-2015-142601 139 | Yes,CA-2015-112144 140 | Yes,CA-2014-164861 141 | Yes,US-2015-128090 142 | Yes,CA-2014-108609 143 | Yes,CA-2017-147452 144 | Yes,CA-2015-123568 145 | Yes,CA-2017-102519 146 | Yes,CA-2017-121853 147 | Yes,CA-2015-132374 148 | Yes,US-2014-150574 149 | Yes,CA-2014-148614 150 | Yes,CA-2017-165491 151 | Yes,CA-2016-133802 152 | Yes,CA-2017-112865 153 | Yes,CA-2016-130680 154 | Yes,CA-2015-114048 155 | Yes,CA-2017-108931 156 | Yes,CA-2017-136308 157 | Yes,CA-2016-126529 158 | Yes,CA-2014-135699 159 | Yes,CA-2015-146255 160 | Yes,CA-2017-140053 161 | Yes,CA-2014-143336 162 | Yes,CA-2017-159954 163 | Yes,CA-2016-105291 164 | Yes,CA-2017-158729 165 | Yes,CA-2017-101273 166 | Yes,CA-2017-123085 167 | Yes,CA-2017-131828 168 | Yes,CA-2017-113670 169 | Yes,CA-2015-135580 170 | Yes,US-2016-157490 171 | Yes,CA-2017-142888 172 | Yes,CA-2014-153150 173 | Yes,US-2016-152051 174 | Yes,CA-2014-103940 175 | Yes,CA-2016-159345 176 | Yes,CA-2017-162015 177 | Yes,CA-2015-148873 178 | Yes,CA-2017-167395 179 | Yes,US-2016-127425 180 | Yes,CA-2017-142342 181 | Yes,CA-2016-159212 182 | Yes,CA-2016-116736 183 | Yes,CA-2016-128671 184 | Yes,CA-2017-112753 185 | Yes,US-2016-148957 186 | Yes,CA-2015-110814 187 | Yes,CA-2014-164721 188 | Yes,US-2015-136987 189 | Yes,CA-2016-151372 190 | Yes,US-2017-151127 191 | Yes,US-2015-110569 192 | Yes,CA-2015-153220 193 | Yes,CA-2017-117212 194 | Yes,CA-2017-101574 195 | Yes,CA-2016-151323 196 | Yes,CA-2014-166744 197 | Yes,US-2016-119046 198 | Yes,CA-2015-105158 199 | Yes,CA-2017-103380 200 | Yes,CA-2016-161746 201 | Yes,CA-2015-111948 202 | Yes,CA-2017-138163 203 | Yes,CA-2015-134201 204 | Yes,US-2015-126214 205 | Yes,CA-2014-140816 206 | Yes,CA-2014-133690 207 | Yes,CA-2016-120873 208 | Yes,US-2017-103247 209 | Yes,CA-2017-155712 210 | Yes,US-2016-108455 211 | Yes,CA-2016-104689 212 | Yes,CA-2017-152660 213 | Yes,US-2015-136749 214 | Yes,CA-2015-143602 215 | Yes,CA-2016-134803 216 | Yes,CA-2016-139269 217 | Yes,CA-2014-123253 218 | Yes,CA-2016-162159 219 | Yes,US-2016-135720 220 | Yes,CA-2014-159338 221 | Yes,US-2017-136679 222 | Yes,CA-2015-113628 223 | Yes,CA-2017-107825 224 | Yes,CA-2014-160766 225 | Yes,CA-2016-118311 226 | Yes,CA-2015-130456 227 | Yes,CA-2014-148950 228 | Yes,CA-2015-119214 229 | Yes,CA-2015-119907 230 | Yes,CA-2017-137428 231 | Yes,CA-2014-108861 232 | Yes,CA-2016-130638 233 | Yes,CA-2015-143238 234 | Yes,CA-2015-109736 235 | Yes,CA-2014-124688 236 | Yes,CA-2016-133368 237 | Yes,CA-2017-168193 238 | Yes,CA-2014-135657 239 | Yes,CA-2016-105081 240 | Yes,CA-2017-154214 241 | Yes,CA-2015-107678 242 | Yes,CA-2017-144064 243 | Yes,CA-2017-101700 244 | Yes,CA-2016-113341 245 | Yes,CA-2015-139731 246 | Yes,CA-2015-132941 247 | Yes,CA-2015-146262 248 | Yes,CA-2016-115917 249 | Yes,US-2016-111528 250 | Yes,CA-2017-142328 251 | Yes,CA-2015-141593 252 | Yes,CA-2017-121258 253 | Yes,CA-2014-141726 254 | Yes,CA-2017-167003 255 | Yes,US-2015-160857 256 | Yes,CA-2016-145261 257 | Yes,CA-2017-112725 258 | Yes,CA-2016-145919 259 | Yes,CA-2017-140151 260 | Yes,CA-2015-164882 261 | Yes,CA-2017-123491 262 | Yes,CA-2014-127012 263 | Yes,CA-2016-123526 264 | Yes,CA-2017-161557 265 | Yes,CA-2015-150875 266 | Yes,CA-2015-150770 267 | Yes,US-2014-140452 268 | Yes,CA-2015-149650 269 | Yes,CA-2014-100867 270 | Yes,CA-2017-140186 271 | Yes,CA-2017-156391 272 | Yes,CA-2015-157770 273 | Yes,CA-2017-140963 274 | Yes,CA-2017-154949 275 | Yes,CA-2016-166275 276 | Yes,US-2014-143287 277 | Yes,CA-2014-151162 278 | Yes,US-2017-103828 279 | Yes,CA-2014-143840 280 | Yes,CA-2014-160773 281 | Yes,CA-2017-111556 282 | Yes,CA-2017-140585 283 | Yes,CA-2014-103373 284 | Yes,CA-2016-159023 285 | Yes,CA-2016-145492 286 | Yes,CA-2017-118122 287 | Yes,CA-2014-116785 288 | Yes,US-2014-164763 289 | Yes,CA-2017-122504 290 | Yes,CA-2017-150910 291 | Yes,CA-2015-162166 292 | Yes,US-2016-140172 293 | Yes,CA-2015-101910 294 | Yes,CA-2017-156958 295 | Yes,CA-2016-105585 296 | Yes,CA-2016-148796 297 | Yes,CA-2015-149636 298 | -------------------------------------------------------------------------------- /Z Sample Data/Super Store Returns/superstore_sales.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/Z Sample Data/Super Store Returns/superstore_sales.csv -------------------------------------------------------------------------------- /Z Sample Data/TR_Data.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/Z Sample Data/TR_Data.zip -------------------------------------------------------------------------------- /Z Sample Data/TR_Data/TR_Products.csv: -------------------------------------------------------------------------------- 1 | ProductID,ProductName,ProductCategory,Price 2 | 1,Large Towel,Housekeeping,9 3 | 2,Hand Towel,Housekeeping,5 4 | 3,Washcloth,Housekeeping,3 5 | 4,Shampoo,Housekeeping,40 6 | 5,Moisturizer,Housekeeping,40 7 | 6,Conditioner,Housekeeping,40 8 | 7,Hand Soap,Housekeeping,35 9 | 8,Bath Soap,Housekeeping,35 10 | 9,Tissues,Housekeeping,14 11 | 10,Toilet Paper,Housekeeping,19 12 | 11,Shower Cap,Housekeeping,10 13 | 12,Bed Sheet (King),Housekeeping,29 14 | 13,Bed Sheet (Double),Housekeeping,20 15 | 14,Pillowcase,Housekeeping,15 16 | 15,Drinking Glass,Housekeeping,24 17 | 16,Mouthwash,Housekeeping,40 18 | 17,Clothes Hanger,Housekeeping,18 19 | 18,Coffee Cup,Housekeeping,24 20 | 19,Light Bulb,Housekeeping,17 21 | 20,Pens,Office Supplies,30 22 | 21,Printer Paper,Office Supplies,50 23 | 22,Paper Clips,Office Supplies,3 24 | 23,Printer Toner,Office Supplies,85 25 | 24,Envelopes (Letter),Office Supplies,24 26 | 25,Envelopes (Legal),Office Supplies,28 27 | 26,Envelopes (Catalog),Office Supplies,13 28 | 27,Invisible Tape,Office Supplies,8 29 | 28,Staples,Office Supplies,8 30 | 29,Erasable Markers,Office Supplies,6 31 | 30,Permanent Markers,Office Supplies,6 32 | 31,Sticky Notes,Office Supplies,20 33 | 32,Note Pads,Office Supplies,14 34 | 33,Towel Rack,Furnishings,10 35 | 34,Shower Curtain,Furnishings,5 36 | 35,Tissue Box,Furnishings,25 37 | 36,Armoire,Furnishings,115 38 | 37,Bed (King),Furnishings,300 39 | 38,Bed (Double),Furnishings,250 40 | 39,Alarm Clock,Furnishings,45 41 | 40,Coffee Maker,Furnishings,119 42 | 41,Office Chair,Furnishings,85 43 | 42,Reading Chair,Furnishings,70 44 | 43,Table Lamp,Furnishings,38 45 | 44,Floor Lamp,Furnishings,55 46 | 45,Computer Desk,Furnishings,102 47 | 46,Safe,Furnishings,115 48 | 47,Swivel Chair,Furnishings,75 49 | 48,Side Table,Furnishings,40 50 | 49,Large Vase,Furnishings,27 51 | 50,Small Vase,Furnishings,33 52 | 51,Painting,Furnishings,85 53 | 52,Ironing Board,Furnishings,18 54 | 53,Iron,Furnishings,20 55 | 54,Chest of Drawers,Furnishings,150 56 | 55,Television,Furnishings,200 57 | 56,Telephone,Furnishings,38 58 | 57,Wireless Router,Furnishings,70 59 | 58,Mirror,Furnishings,85 60 | 59,Screws,Maintenance,14 61 | 60,Portable Drill,Maintenance,195 62 | 61,Tape Measure,Maintenance,8 63 | 62,Leaf Blower,Maintenance,202 64 | 63,Duct Tape,Maintenance,27 65 | 64,Gaffer's Tape,Maintenance,27 66 | 65,Ladder,Maintenance,89 67 | 66,Wood Glue,Maintenance,21 68 | 67,Adjustable Wrench,Maintenance,10 69 | 68,Window Screen,Maintenance,30 70 | 69,Door Knob,Maintenance,41 71 | 70,Door Lock,Maintenance,117 72 | 71,Hammer,Maintenance,17 73 | 72,Nails,Maintenance,12 74 | 73,Mop,Maintenance,15 75 | 74,Large Plant,Public Areas,35 76 | 75,Small Plant,Public Areas,25 77 | 76,Ficus,Public Areas,80 78 | 77,Large Area Rug,Public Areas,40 79 | 78,Small Area Rug,Public Areas,20 80 | 79,Coffee Table,Public Areas,103 81 | 80,Poster Stand,Public Areas,27 82 | 81,Flyer Holder,Public Areas,3 83 | 82,Business Cards,Public Areas,60 84 | 83,Bar Stool,Public Areas,60 85 | 84,Tall Chair,Public Areas,75 86 | 85,Low Chair,Public Areas,80 87 | 86,Sofa,Public Areas,215 88 | 87,End Table,Public Areas,30 89 | 88,Serving Tray,Public Areas,6 90 | 89,Photograph,Public Areas,45 91 | 90,Luggage Cart,Public Areas,138 92 | 91,Small Sign,Public Areas,14 93 | 92,Large Sign,Public Areas,20 94 | 93,Terminal,Public Areas,85 95 | 94,Monitor,Public Areas,70 96 | -------------------------------------------------------------------------------- /Z Sample Data/TR_Data/TR_PropertyInfo.csv: -------------------------------------------------------------------------------- 1 | Prop ID,PropertyCity,PropertyState 2 | 1,New York,New York 3 | 2,Cincinnati,Ohio 4 | 3,Portland,Oregon 5 | 4,Seattle,Washington 6 | 5,Kansas City,Missouri 7 | 6,Orlando,Florida 8 | 7,Boston,Massachusetts 9 | 8,Philadelphia,Pennsylvania 10 | 9,Richmond,Virginia 11 | 10,Arlington,Virginia 12 | 11,Atlanta,Georgia 13 | 12,Dallas,Texas 14 | 13,New Orleans,Louisiana 15 | 14,Nashville,Tennessee 16 | 15,Chicago,Illinois 17 | 16,Denver,Colorado 18 | 17,Las Vegas,Nevada 19 | 18,Phoenix,Arizona 20 | 19,Los Angeles,California 21 | 20,San Francisco,California 22 | -------------------------------------------------------------------------------- /Z Sample Data/cricket-world-cup-2019-players-data.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/Z Sample Data/cricket-world-cup-2019-players-data.zip -------------------------------------------------------------------------------- /Z Sample Data/fifa-world-cup.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/KunaalNaik/YT_SQL_for_DataScience/6d520d7c438949243d0b9c8e393e07db91eb5351/Z Sample Data/fifa-world-cup.zip -------------------------------------------------------------------------------- /Z Sample Data/fifa-world-cup/WorldCups.csv: -------------------------------------------------------------------------------- 1 | Year,Country,Winner,Runners-Up,Third,Fourth,GoalsScored,QualifiedTeams,MatchesPlayed,Attendance 2 | 1930,Uruguay,Uruguay,Argentina,USA,Yugoslavia,70,13,18,590.549 3 | 1934,Italy,Italy,Czechoslovakia,Germany,Austria,70,16,17,363.000 4 | 1938,France,Italy,Hungary,Brazil,Sweden,84,15,18,375.700 5 | 1950,Brazil,Uruguay,Brazil,Sweden,Spain,88,13,22,1.045.246 6 | 1954,Switzerland,Germany FR,Hungary,Austria,Uruguay,140,16,26,768.607 7 | 1958,Sweden,Brazil,Sweden,France,Germany FR,126,16,35,819.810 8 | 1962,Chile,Brazil,Czechoslovakia,Chile,Yugoslavia,89,16,32,893.172 9 | 1966,England,England,Germany FR,Portugal,Soviet Union,89,16,32,1.563.135 10 | 1970,Mexico,Brazil,Italy,Germany FR,Uruguay,95,16,32,1.603.975 11 | 1974,Germany,Germany FR,Netherlands,Poland,Brazil,97,16,38,1.865.753 12 | 1978,Argentina,Argentina,Netherlands,Brazil,Italy,102,16,38,1.545.791 13 | 1982,Spain,Italy,Germany FR,Poland,France,146,24,52,2.109.723 14 | 1986,Mexico,Argentina,Germany FR,France,Belgium,132,24,52,2.394.031 15 | 1990,Italy,Germany FR,Argentina,Italy,England,115,24,52,2.516.215 16 | 1994,USA,Brazil,Italy,Sweden,Bulgaria,141,24,52,3.587.538 17 | 1998,France,France,Brazil,Croatia,Netherlands,171,32,64,2.785.100 18 | 2002,Korea/Japan,Brazil,Germany,Turkey,Korea Republic,161,32,64,2.705.197 19 | 2006,Germany,Italy,France,Germany,Portugal,147,32,64,3.359.439 20 | 2010,South Africa,Spain,Netherlands,Germany,Uruguay,145,32,64,3.178.856 21 | 2014,Brazil,Germany,Argentina,Netherlands,Brazil,171,32,64,3.386.810 22 | -------------------------------------------------------------------------------- /Z Sample Data/ipl.zip: 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