├── .gitignore ├── README.md ├── course-content ├── assets │ ├── 8-week-sql-challenge.png │ ├── avatar.png │ ├── bull.jpeg │ ├── crypto-erd.png │ ├── dwd-banner.png │ ├── hodl.jpeg │ └── trader.jpeg ├── step1.md ├── step10.md ├── step11.md ├── step12.md ├── step13.md ├── step2.md ├── step3.md ├── step4.md ├── step5.md ├── step6.md ├── step7.md ├── step8.md ├── step9.md └── the-end.md ├── data ├── MySQL │ ├── init-mysql.sql │ └── schema-mysql.sql ├── Postgres │ ├── init-postgres.sql │ └── schema-postgres.sql ├── members.csv ├── prices.csv └── transactions.csv ├── docker-compose.yml ├── images ├── badges │ ├── click-these-badges-to-view-content!.svg │ ├── go-to-next-tutorial.svg │ ├── go-to-previous-tutorial.svg │ ├── raw-data.svg │ ├── sql-interface-db-fiddle.svg │ ├── sql-interface-play-with-docker.svg │ ├── sql-masterclass-v-1.0.svg │ ├── start-here.svg │ └── version-1.0.svg ├── crypto.png └── sql-masterclas-banner.png └── slides └── sql-masterclass-odsc-apac-2021.pdf /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | template.md -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

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4 | 5 | [![forthebadge](images/badges/version-1.0.svg)]() 6 | [![forthebadge](https://forthebadge.com/images/badges/powered-by-coffee.svg)]() 7 | [![forthebadge](https://forthebadge.com/images/badges/built-with-love.svg)]() 8 | [![forthebadge](https://forthebadge.com/images/badges/ctrl-c-ctrl-v.svg)]() 9 | 10 | --- 11 | 12 | **Welcome to the SQL Masterclass Free GitHub Course!!!** 13 | 14 | You can find all of the content and slides for Danny Ma's SQL Masterclass held at the ODSC Asia Pacific 2021 virtual conference! 15 | 16 | # Table of Contents 17 | 18 | - [📚 Course Content](#course-content) 19 | - [📊 Accessing the Data](#accessing-the-data) 20 | - [🙏 Conclusion](#thank-you) 21 | - [😎 About the Author](#about-danny) 22 | 23 | # 👋 Introduction 24 | 25 | This free GitHub course is sorted into multiple tutorials which were actually delivered using O'Reilly Katacoda during the live training (which is totally ah-mazing 🤩 ) 26 | 27 | To avoid any legal issues - all of the SQL live training material is available directly here on GitHub as a companion course which you can learn from at your leisure 👌 28 | 29 | You can also see the presentation slides for the live training [here!](https://github.com/datawithdanny/sql-masterclass/tree/main/slides/sql-masterclass-odsc-apac-2021.pdf) 30 | 31 | # 📚 Course Content 32 | 33 | Click the navigation badge below to get started - all of the course tutorials can be found in the `/course-content` folder! 34 | 35 | **CLICK ON THE BADGE BELOW TO GET STARTED!!!** 36 | 37 | [![forthebadge](./images/badges/start-here.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step1.md) 38 | 39 | # 📊 Accessing the Data 40 | 41 | Although all of the code examples show the outputs directly inside the training materials for this masterclass, you can also play with the data locally or on a few different hosted services provided below! 42 | 43 | My recommendations are to use any one of the `Docker` solutions to best replicate the SQLPad environment used for the actual live training session. 44 | 45 | You can also access the free `DB-Fiddle` instances below or you can also access the raw data [here!](#raw-data) 46 | 47 | ## Docker Solutions 48 | 49 | This is the preferred setup to access all of the course data in the desired SQLPad environment! 50 | 51 | This same Docker SQLPad interface is featured in the Data With Danny [Serious SQL course](https://www.datawithdanny.com) but with a lot more data included - you should check it out! 😉 52 | 53 | We even include instructions on how to install Docker if you haven't used it before inside the course :) 54 | 55 | ### Docker Compose 56 | 57 | If you have Docker installed on your machine with Docker-Compose, you can directly use the `docker-compose.yml` file included in the root of this repo to spin up the required SQLPad infrastructure. 58 | 59 | ```bash 60 | docker-compose up 61 | ``` 62 | 63 | Once the initialisation is complete - you can visit `localhost:3000` in your favourite browser to access the SQL interface with all the data ready to go. 64 | 65 | Note that you may want to save all of your code in a separate text editor and copy & paste it directly into the SQLPad so you don't lose all of your code should something go wrong! 🥵 66 | 67 | ### Free Play With Docker Instance 68 | 69 | You can use this live Docker PostgreSQL environment to copy and paste SQL code from the `code/` folder directly here to see the outputs generated for the session. 70 | 71 | Click on the buttons below to launch a Play-With-Docker stack - DockerHub login/signup is required but all batteries should be included. 72 | 73 | It's totally free to create your own DockerHub account and highly recommended too if you want to create your own Docker images in the future! 74 | 75 | **👇👇👇 Make sure you right click and open the link in a new tab if you're viewing this on GitHub!** 76 | 77 | [![Try in PWD](images/badges/sql-interface-play-with-docker.svg)](https://labs.play-with-docker.com/?stack=https://raw.githubusercontent.com/datawithdanny/sql-masterclass/main/docker-compose.yml) 78 | 79 | Once the initialisation steps are complete and you see `Your session is ready at the bottom of the screen` - there is a chance you might need to refresh your browser or click just outside the initial popout window and press your esc key to close this window as the `Close` button is sometimes not working! 80 | 81 | Next you can either hit port `3000` to use a SQLPad GUI or you can run the following to enter a `psql` terminal instance if you are so inclined! 82 | 83 | ```bash 84 | docker exec -it `docker ps -aqf "expose=5432"` psql -U postgres 85 | ``` 86 | 87 | ### Local Docker Commands 88 | 89 | If you are happy to use the `psql` shell instead of the SQLPad GUI because you love the terminal - you can simply run the following commands to access the data directly too. By default, port `5432` will be used for the PostgreSQL database. 90 | 91 | Feel free to change the `name` argument to anything you like! 92 | 93 | ```bash 94 | docker run -d --rm --name="psql-masterclass" dannyma/psql-crypto:latest 95 | docker exec -it psql-masterclass psql -U postgres 96 | ``` 97 | 98 | ## DB Fiddle 99 | 100 | You can also access the raw data directly in a live browser interface called DB Fiddle. 101 | 102 | Click on the badge below to get access to this free service - it takes a little bit longer to run each query when compared to the Docker solutions, so please keep this in mind! 103 | 104 | [![forthebadge](images/badges/sql-interface-db-fiddle.svg)](https://www.db-fiddle.com/f/cnLCK4ChsNfr5ViG6vzePg/7) 105 | 106 | ## Raw Data 107 | 108 | You can also find all of the raw data inside the `/data` folder 109 | 110 | [![forthebadge](images/badges/raw-data.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/data) 111 | 112 | Update: There are now SQL scripts for both PostgreSQL and MySQL - however all of the code inside the course contents is for PostgreSQL only. 113 | 114 | The majority of the code snippets should still work across SQL flavours but you may need to tweak some of the queries slightly! 115 | 116 | * `Postgres/init-postgres.sql` and `MySQL/init-mysql.sql` can be ran with your favourite SQL IDE like PgAdmin4 or MySQL Workbench directly to create the required database tables 117 | * `Postgres/schema-postgres.sql` and `MySQL/schema-mysql.sql` contain the table definitions in case you want to use them also and load in the CSV files 118 | * `.csv` files containing the raw data for each table if you want to import the data to another tool 119 | 120 | Note that the two flavour init scripts are essentially the same - but MySQL has backticks instead of double quotes for table creation steps! 121 | 122 | --- 123 | 124 | # Thank You & Next Steps 125 | 126 | Thank you for your taking this free SQL Masterclass GitHub course! If you've enjoyed this - please feel free to share this with your friends and leave a review! ⭐ 127 | 128 | Here are some ways you can support the author and the Data With Danny team below 🙏 129 | 130 | ## Data With Danny Virtual Data Apprenticeship 131 | 132 | 133 |

134 | 135 |
136 | 137 | If you're interested in learning valuable data science skills with Danny directly - you can checkout the [Data With Danny](https://www.datawithdanny.com) website for more details! 138 | 139 | Join our private student community with over 1,000 data professionals, join a local study group, get help from our team of 12+ data mentors and learn more about our personalized mentorship initiatives! 140 | 141 | The 1st part of the Data With Danny program is Serious SQL which is like this SQL course but on an entirely differently level. If you're serious about learning SQL you won't want to miss this course! 142 | 143 | Our first live SQL training cohort begins in November 2021 - you can gain all access to course content, recorded videos, our private Discord and more for a one off payment of only $49 - find out more [here!](https://www.datawithdanny.com/courses/serious-sql) 144 | 145 | ## 8 Week SQL Challenge Case Studies 146 | 147 | 148 |

149 | 150 |
151 | 152 | Want to test your SQL skills and tackle 8 realistic SQL case studies and get access to a collection of free SQL learning resources? 153 | 154 | Join the Data With Danny [8 Week SQL Challenge](https://www.8weeksqlchallenge.com/getting-started) for free today! 155 | 156 | Solve all 8 realistic SQL case studies designed to simulate real work scenarios and interview questions across multiple analytics domains including customer analytics, digital, banking, retail and subscriptions! 157 | 158 | ## About the Author: Danny Ma 159 | 160 | 161 |

162 | 163 | 164 | Danny is the Chief Data Mentor at Data With Danny and the Founder & CEO of Sydney Data Science, a boutique data consultancy based out of Sydney, Australia 🇦🇺 165 | 166 | After spending the last 10 years working in almost every single role in the data ecosystem, Danny is now focused on solving difficult problems at scale re-imagining data education and recruitment, and mentoring the next generation of data professionals. 167 | 168 | He provides specialist data consultancy services: 169 | 170 | * Digital customer analytics and experimentation 171 | * Data and machine learning strategy 172 | * Data engineering and systems design 173 | * Team building for analytics and data science functions 174 | * Technical training for practitioners and management 175 | 176 | Danny is a regular speaker at global data conferences, meetups and podcasts where he shares the importance of mentorship for all data professionals. He is 177 | also a technical author and instructor for O'Reilly. 178 | 179 | Danny believes that he is living proof that dispels the myth that you need higher level education to be successful in the data science space, 180 | and he wants to share his experiences with others so they can do the same. 181 | -------------------------------------------------------------------------------- /course-content/assets/8-week-sql-challenge.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datawithdanny/sql-masterclass/bb87f9be59a9fd828e75a8984e29537f10f12ec5/course-content/assets/8-week-sql-challenge.png -------------------------------------------------------------------------------- /course-content/assets/avatar.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datawithdanny/sql-masterclass/bb87f9be59a9fd828e75a8984e29537f10f12ec5/course-content/assets/avatar.png -------------------------------------------------------------------------------- /course-content/assets/bull.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datawithdanny/sql-masterclass/bb87f9be59a9fd828e75a8984e29537f10f12ec5/course-content/assets/bull.jpeg -------------------------------------------------------------------------------- /course-content/assets/crypto-erd.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datawithdanny/sql-masterclass/bb87f9be59a9fd828e75a8984e29537f10f12ec5/course-content/assets/crypto-erd.png -------------------------------------------------------------------------------- /course-content/assets/dwd-banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datawithdanny/sql-masterclass/bb87f9be59a9fd828e75a8984e29537f10f12ec5/course-content/assets/dwd-banner.png -------------------------------------------------------------------------------- /course-content/assets/hodl.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datawithdanny/sql-masterclass/bb87f9be59a9fd828e75a8984e29537f10f12ec5/course-content/assets/hodl.jpeg -------------------------------------------------------------------------------- /course-content/assets/trader.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datawithdanny/sql-masterclass/bb87f9be59a9fd828e75a8984e29537f10f12ec5/course-content/assets/trader.jpeg -------------------------------------------------------------------------------- /course-content/step1.md: -------------------------------------------------------------------------------- 1 |

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4 | 5 | [![forthebadge](./../images/badges/version-1.0.svg)]() 6 | [![forthebadge](https://forthebadge.com/images/badges/powered-by-coffee.svg)]() 7 | [![forthebadge](https://forthebadge.com/images/badges/built-with-love.svg)]() 8 | [![forthebadge](https://forthebadge.com/images/badges/ctrl-c-ctrl-v.svg)]() 9 | 10 | # Step 1 - Introduction 11 | 12 | ## Our Database 13 | 14 | All of our data lives within a PostgreSQL database and contains a single schema called `trading`. 15 | 16 | In PostgreSQL, a database can contain multiple schemas, and a schema is a collection of tables and other database objects. 17 | 18 | ## Copy and Run a SQL Query 19 | 20 | To run our first query together - you can click on the right hand corner of the following code snippet below to copy a basic `SELECT` query to your clipboard. 21 | 22 | You can then paste it into your SQLPad interface and click on the `Run` button in the top right corner or hit `cmd` + `enter` on Mac or `control` + `enter` on Windows to run the query. 23 | 24 | ```sql 25 | SELECT * FROM trading.members; 26 | ``` 27 | 28 | This `SELECT` query above will return all of the records from the `members` table inside the `trading` schema. 29 | 30 | # Our Crypto Case Study 31 | 32 | For this entire SQL Simplified course we will focus on our Cryptocurrency Trading SQL Case Study! 33 | 34 | ## Setting the Context 35 | 36 | In our fictitious (but realistic) case study - my team of trusted data mentors from the Data With Danny team have been dabbling in the crypto markets since 2017. 37 | 38 | Our main purpose for this case study is to analyse the performance of the DWD mentors over time and to "slice and dice" the data in various ways to investigate other questions we might want answers to! 39 | 40 | ## Our Datasets 41 | 42 | All of our data for this case study exists within the `trading` schema as we mentioned in the previous tutorial. 43 | 44 | There are 3 data tables available to us in this schema which we can use to run our SQL queries with: 45 | 46 | 1. `members` 47 | 2. `prices` 48 | 3. `transactions` 49 | 50 | You can inspect each dataset by copying the following code snippet below and running it directly in the SQLPad GUI - please make sure to overwrite any previous queries which are already in the SQL interface! 51 | 52 | ```sql 53 | SELECT * FROM trading.members; 54 | ``` 55 | 56 | | member_id | first_name | region | 57 | | --------- | ---------- | ------------- | 58 | | c4ca42 | Danny | Australia | 59 | | c81e72 | Vipul | United States | 60 | | eccbc8 | Charlie | United States | 61 | | a87ff6 | Nandita | United States | 62 | | e4da3b | Rowan | United States | 63 | | 167909 | Ayush | United States | 64 | | 8f14e4 | Alex | United States | 65 | | c9f0f8 | Abe | United States | 66 | | 45c48c | Ben | Australia | 67 | | d3d944 | Enoch | Africa | 68 | | 6512bd | Vikram | India | 69 | | c20ad4 | Leah | Asia | 70 | | c51ce4 | Pavan | Australia | 71 | | aab323 | Sonia | Australia | 72 |
73 | 74 | ```sql 75 | SELECT * FROM trading.prices LIMIT 5; 76 | ``` 77 | 78 | | ticker | market_date | price | open | high | low | volume | change | 79 | | ------ | ----------- | ------- | ------- | ------- | ------- | ------- | ------ | 80 | | ETH | 2021-08-29 | 3177.84 | 3243.96 | 3282.21 | 3162.79 | 582.04K | -2.04% | 81 | | ETH | 2021-08-28 | 3243.90 | 3273.78 | 3284.58 | 3212.24 | 466.21K | -0.91% | 82 | | ETH | 2021-08-27 | 3273.58 | 3093.78 | 3279.93 | 3063.37 | 839.54K | 5.82% | 83 | | ETH | 2021-08-26 | 3093.54 | 3228.03 | 3249.62 | 3057.48 | 118.44K | -4.17% | 84 | | ETH | 2021-08-25 | 3228.15 | 3172.12 | 3247.43 | 3080.70 | 923.13K | 1.73% | 85 |
86 | 87 | ```sql 88 | SELECT * FROM trading.transactions LIMIT 5; 89 | ``` 90 | 91 | | txn_id | member_id | ticker | txn_date | txn_type | quantity | percentage_fee | txn_time | 92 | | ------ | --------- | ------ | ---------- | -------- | -------- | -------------- | ------------------- | 93 | | 1 | c81e72 | BTC | 2017-01-01 | BUY | 50 | 0.30 | 2017-01-01 00:00:00 | 94 | | 2 | eccbc8 | BTC | 2017-01-01 | BUY | 50 | 0.30 | 2017-01-01 00:00:00 | 95 | | 3 | a87ff6 | BTC | 2017-01-01 | BUY | 50 | 0.00 | 2017-01-01 00:00:00 | 96 | | 4 | e4da3b | BTC | 2017-01-01 | BUY | 50 | 0.30 | 2017-01-01 00:00:00 | 97 | | 5 | 167909 | BTC | 2017-01-01 | BUY | 50 | 0.30 | 2017-01-01 00:00:00 | 98 | 99 | 100 | Note: the `LIMIT 5` in the above queries will return us only the first 5 rows from each dataset. 101 | 102 | It is a good practice to always `LIMIT` your queries just in case the tables are huge - you don't want to be trying to return all 5 million rows from a huge table when you are just inspecting the data for the first time! 103 | 104 | ## A Note on Schemas 105 | 106 | Notice above how the "`trading.`" is included before each of our available tables. 107 | 108 | If we were to remove this - our database will be unable to find our tables. 109 | 110 | This query below will return you an error when ran: 111 | 112 | ```sql 113 | SELECT * FROM members; 114 | ``` 115 | 116 | > relation "members" does not exist 117 | 118 | In realistic scenarios - physical tables will almost always live within a schema and we'll need to reference the schema name to run our queries properly! 119 | 120 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step2.md) -------------------------------------------------------------------------------- /course-content/step10.md: -------------------------------------------------------------------------------- 1 |

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Click here to reveal the solution!

76 | 77 | ```sql 78 | SELECT 79 | SUM(transactions.quantity * prices.price) AS initial_investment, 80 | SUM(transactions.quantity * prices.price * transactions.percentage_fee / 100) AS fees 81 | FROM vikram_bull_strategy AS transactions 82 | INNER JOIN trading.prices 83 | ON transactions.ticker = prices.ticker 84 | AND transactions.txn_date = prices.market_date; 85 | ``` 86 | 87 |

88 | 89 | | initial_investment | fees | 90 | | --------------------------- | --------------------------- | 91 | | 50730451.023400136384298882 | 128821.14163246531801672694 | 92 |
93 | 94 | ### Question 3 95 | 96 | > What is the average cost per unit of BTC and ETH purchased by Vikram 97 | 98 |

Click here to reveal the solution!

119 | 120 | | ticker | dollar_cost_average | 121 | | ------ | ----------------------- | 122 | | BTC | 12190.13846337579877423 | 123 | | ETH | 538.402092304626902638 | 124 |
125 | 126 | ### Question 4 127 | 128 | > Calculate profitability by using final portfolio value divided by the investment amount 129 | 130 |

Click here to reveal the solution!

151 | 152 | | profitability | 153 | | -------------------- | 154 | | 4.019204544489789883 | 155 | 156 | ### Question 5 157 | 158 | > Calculate Vikram's profitability split by BTC and ETH 159 | 160 |

Click here to reveal the solution!

184 | 185 | | ticker | profitability | 186 | | ------ | -------------------- | 187 | | BTC | 3.95852763649714995 | 188 | | ETH | 5.902354477110731653 | 189 |
190 | 191 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step9.md) 192 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step11.md) -------------------------------------------------------------------------------- /course-content/step11.md: -------------------------------------------------------------------------------- 1 |

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4 | 5 | [![forthebadge](./../images/badges/version-1.0.svg)]() 6 | [![forthebadge](https://forthebadge.com/images/badges/powered-by-coffee.svg)]() 7 | [![forthebadge](https://forthebadge.com/images/badges/built-with-love.svg)]() 8 | [![forthebadge](https://forthebadge.com/images/badges/ctrl-c-ctrl-v.svg)]() 9 | 10 | # Step 11 - The Trader Strategy 11 | 12 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step10.md) 13 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step12.md) 14 | 15 | # Scenario 3: The Trader 16 | 17 | ![trader](assets/trader.jpeg) 18 | 19 | Nandita is the Queen of crypto trading - she wants to follow the popular trader's adage of **BUY LOW, SELL HIGH** 20 | 21 | ## Nandita's Transaction History 22 | 23 | * She also starts out with a 50 BTC and ETH purchase just like Leah and Vikram 24 | * She continues to buy more crypto over the 4 years 25 | * She starts selling some of her crypto portfolio to realise gains 26 | 27 | ## Nandita's Data 28 | 29 | This final scenario 3 is actually exactly the same as our real `trading.transactions` dataset! 30 | 31 | To complete our individual scenarios before we calculate all our metrics for all mentors - let's also prepare another temp table called `nandita_trading_strategy` 32 | 33 | ```sql 34 | CREATE TEMP TABLE nandita_trading_strategy AS 35 | SELECT * FROM trading.transactions 36 | WHERE member_id = 'a87ff6'; 37 | ``` 38 | 39 | You can inspect the data by running the following query after creating the temp table above: 40 | 41 | ```sql 42 | SELECT * FROM nandita_trading_strategy LIMIT 10; 43 | ``` 44 | 45 | | txn_id | member_id | ticker | txn_date | txn_type | quantity | percentage_fee | txn_time | 46 | | ------ | --------- | ------ | ---------- | -------- | ---------------- | -------------- | -------------------------- | 47 | | 3 | a87ff6 | BTC | 2017-01-01 | BUY | 50 | 0.00 | 2017-01-01 00:00:00 | 48 | | 19 | a87ff6 | ETH | 2017-01-01 | BUY | 50 | 0.20 | 2017-01-01 00:00:00 | 49 | | 41 | a87ff6 | ETH | 2017-01-01 | BUY | 1.98666102006509 | 0.30 | 2017-01-01 17:39:10.894181 | 50 | | 49 | a87ff6 | ETH | 2017-01-02 | BUY | 8.78673520720906 | 0.30 | 2017-01-02 04:48:50.044665 | 51 | | 53 | a87ff6 | BTC | 2017-01-02 | BUY | 5.95980481918755 | 0.30 | 2017-01-02 09:55:27.347188 | 52 | | 60 | a87ff6 | BTC | 2017-01-02 | BUY | 9.01117722642621 | 0.30 | 2017-01-02 17:16:29.062839 | 53 | | 64 | a87ff6 | ETH | 2017-01-02 | BUY | 1.37715908309016 | 0.01 | 2017-01-02 20:49:33.771818 | 54 | | 77 | a87ff6 | BTC | 2017-01-03 | BUY | 3.80769453794553 | 0.30 | 2017-01-03 12:30:20.779105 | 55 | | 89 | a87ff6 | BTC | 2017-01-04 | BUY | 5.68677206948404 | 0.00 | 2017-01-04 08:13:07.752195 | 56 | | 93 | a87ff6 | BTC | 2017-01-04 | BUY | 8.13772499730359 | 0.30 | 2017-01-04 12:25:48.367139 | 57 |
58 | 59 | ## Final Evaluation Metrics 60 | 61 | By the end of our assessment period on the 29th of August 2021 - we can calculate Nandita's metrics as follows for each individual BTC and ETH portfolio: 62 | 63 | * Count of buy and sell transactions 64 | * Total investment amount of purchases 65 | * The dollar amount of fees for purchase transactions 66 | * Dollar cost average of purchases 67 | * Total gross revenue of sell transactions 68 | * Average sell price for each unit sold 69 | * Final portfolio value and quantity 70 | * Profitability measured as (final portfolio value + gross sales revenue - purchase fees - sales fees) / initial investment amount 71 | 72 | **Bonus Question** 73 | 74 | We also want to calculate the difference if Nandita didn't sell any of her crypto and compare it to the final value at the end of August - how much does this impact her overall profitability? 75 | 76 | ## Solutions 77 | 78 | ### Question 1 79 | 80 | > Calculate Nandita's purchase metrics for each of her BTC and ETH portfolios: 81 | > 82 | > * Count of purchase transactions 83 | > * Initial investment 84 | > * Purchase fees 85 | > * Dollar cost average of purchases 86 | 87 |

Click here to reveal the solution!

115 | 116 | | ticker | purchase_count | purchase_quantity | initial_investment | purchase_fees | dollar_cost_average | 117 | | ------ | -------------- | ----------------------- | ---------------------------- | ---------------------------- | ------------------------- | 118 | | BTC | 954 | 5023.705687783492459935 | 63735345.6973630892576024850 | 162919.943377863222128081502 | 12686.9187126851255182628 | 119 | | ETH | 756 | 3822.0371970017654265 | 2287096.578215583047801140 | 5783.32678170688531189239 | 598.397257883758534604 | 120 |
121 | 122 | ### Question 2 123 | 124 | > Calculate Nandita's sales metrics for each of her BTC and ETH portfolios: 125 | > 126 | > * Count of sales transactions 127 | > * Gross revenue amount 128 | > * Sales fees 129 | > * Average selling price 130 | 131 |

Click here to reveal the solution!

159 | 160 | | ticker | sales_count | sales_quantity | gross_revenue | sales_fees | average_selling_price | 161 | | ------ | ----------- | -------------------- | -------------------------- | -------------------------- | ----------------------- | 162 | | BTC | 167 | 863.4858182768507102 | 10975745.05336688201117242 | 29522.09286188312984442411 | 12710.97315213559195557 | 163 | | ETH | 70 | 318.1506358514526923 | 172591.915512909206341725 | 447.93810830446683009024 | 542.484898862480594053 | 164 |
165 | 166 | ### Question 3 167 | 168 | > What is Nandita's final BTC and ETH portfolio value and quantity? 169 | 170 |

Click here to reveal the solution!

198 | 199 | | ticker | final_quantity | final_portfolio_value | 200 | | ------ | ----------------------- | ----------------------------- | 201 | | BTC | 4160.219869506641749735 | 200751409.8030429976334624250 | 202 | | ETH | 3503.8865611503127342 | 11134790.869485909819250128 | 203 |
204 | 205 | ### Question 4 & 5 (bonus!) 206 | 207 | > What is Nandita's overall profitability and theoretical profitability if she didn't sell any of her portfolio? 208 | 209 | We will try to minimise how many times we access the temp table `nandita_trading_strategy` to optimise our query performance! 210 | 211 |

Click here to reveal the solution!

287 | 288 | | ticker | final_portfolio_value | actual_profitability | theoretical_profitability | dollar_cost_average | average_selling_price | sales_revenue | purchase_fees | sales_fees | initial_investment | purchase_quantity | sales_quantity | purchase_transactions | sales_transactions | 289 | | ------ | ----------------------------- | ----------------------- | ------------------------- | ------------------------- | ----------------------- | -------------------------- | ---------------------------- | -------------------------- | ---------------------------- | ----------------------- | -------------------- | --------------------- | ------------------ | 290 | | BTC | 200751409.8030429976334624250 | 3.318954506414827841503 | 3.800967820444996477195 | 12686.9187126851255182628 | 12710.97315213559195557 | 10975745.05336688201117242 | 162919.943377863222128081502 | 29522.09286188312984442411 | 63735345.6973630892576024850 | 5023.705687783492459935 | 863.4858182768507102 | 954 | 167 | 291 | | ETH | 11134790.869485909819250128 | 4.94126554503705553061 | 5.30805715638391209991 | 598.397257883758534604 | 542.484898862480594053 | 172591.915512909206341725 | 5783.32678170688531189239 | 447.93810830446683009024 | 2287096.578215583047801140 | 3822.0371970017654265 | 318.1506358514526923 | 756 | 70 | 292 |
293 | 294 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step10.md) 295 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step12.md) -------------------------------------------------------------------------------- /course-content/step12.md: -------------------------------------------------------------------------------- 1 |

2 | 3 |

Click here to reveal the solution!

126 | 127 | Make sure to checkout the results from the temp table by running a `SELECT` query before tackling the following questions! 128 | 129 | ```sql 130 | SELECT * FROM mentor_performance; 131 | ``` 132 | 133 | **Warning - this is a very very wide table!!!** 134 | 135 | | first_name | region | ticker | final_portfolio_value | actual_profitability | theoretical_profitability | dollar_cost_average | average_selling_price | sales_revenue | purchase_fees | sales_fees | initial_investment | purchase_quantity | sales_quantity | purchase_transactions | sales_transactions | 136 | | ---------- | ------------- | ------ | ----------------------------- | ----------------------- | ------------------------- | ------------------------- | ------------------------ | --------------------------- | ---------------------------- | -------------------------- | ---------------------------- | ----------------------- | --------------------- | --------------------- | ------------------ | 137 | | Vipul | United States | ETH | 13102554.8171483815588462536 | 4.672465959057919903182 | 5.314621992146962886758 | 597.6673234944262953386 | 626.24202540473186303 | 456793.90734028892884625 | 7104.569887725402883402542 | 1188.37948073671816727685 | 2900193.5795488220132164898 | 4852.52157101720575379 | 729.420717245970714 | 991 | 149 | 138 | | Vikram | India | ETH | 6276426.482786365114210656 | 4.31181306610612708721 | 5.89972140484864266246 | 538.402092304626902638 | 576.741885087371095795 | 557225.943027915290149790 | 4169.62855809858209505223 | 1467.03920970122463238123 | 1583560.245623696053185812 | 2941.2223099752008596 | 966.1617396549943312 | 577 | 204 | 139 | | Sonia | Australia | ETH | 11320688.2256349723009004896 | 4.478791700263700984631 | 5.292086399343751113029 | 600.1977155852074903438 | 547.8389664223296338686 | 444504.2206952554872967260 | 6743.608268140600050792052 | 1074.114521166652869369938 | 2625122.0218275999023003858 | 4373.76210149024236043 | 811.37751773682107399 | 864 | 161 | 140 | | Rowan | United States | ETH | 4678642.5109952568421167048 | 5.074269504377240055910 | 6.70449304368406104767 | 473.813548833111016754 | 451.5349656200992805727 | 262663.3359667433250122461 | 2383.35566531251972219524 | 617.385725289504123294627 | 973205.128602539867412883 | 2053.9833252960165058 | 581.71206211245256233 | 398 | 127 | 141 | | Pavan | Australia | ETH | 4188486.703924433521762992 | 3.85446785580589944402 | 6.16497270013013441338 | 515.248248322169372184 | 531.294867143263816005 | 572045.492969362533176448 | 3229.56956929045240213967 | 1350.07976580743016281956 | 1233880.453924375664710254 | 2394.7300314796354124 | 1076.7005825695475786 | 462 | 215 | 142 | | Nandita | United States | ETH | 11134790.869485909819250128 | 4.94126554503705553061 | 5.30805715638391209991 | 598.397257883758534604 | 542.484898862480594053 | 172591.915512909206341725 | 5783.32678170688531189239 | 447.93810830446683009024 | 2287096.578215583047801140 | 3822.0371970017654265 | 318.1506358514526923 | 756 | 70 | 143 | | Leah | Asia | ETH | 5011670.9776990206825808176 | 4.270686580003484588095 | 5.48302943975337301171 | 579.309226305712103035 | 661.5229873200303442848 | 403560.1542523780850137170 | 3216.06018209301609128188 | 986.188509194701148571051 | 1267016.153467224471656408 | 2187.1154401373141792 | 610.04706108140806606 | 435 | 126 | 144 | | Enoch | Africa | ETH | 2183933.3382704268238606128 | 2.927688747446586597299 | 5.826836991147052000975 | 545.1397546539025353837 | 571.75035531306719678 | 604577.95275612948938510 | 2441.664983053686080567615 | 1602.26015552138487694330 | 951080.3934730024378543018 | 1744.65425673609669642 | 1057.415963344853958 | 351 | 209 | 145 | | Danny | Australia | ETH | 11138441.431815681783446160 | 5.08249463426467766581 | 6.23008931789695811035 | 509.872366169852376539 | 566.702626000625094273 | 573254.802885147022737177 | 5824.68526547388391619532 | 1455.00530622275440216891 | 2302888.126083087786695727 | 4516.5972484100717280 | 1011.5619313973581290 | 904 | 200 | 146 | | Charlie | United States | ETH | 5655595.638497926206310488 | 4.38490746540649869678 | 5.82323930041798717653 | 545.48335086195787859 | 636.245983138421001829 | 505215.393166603619467180 | 3500.08393571387478289503 | 1162.34054359444856996608 | 1403940.36949523667356874 | 2573.754757641582429 | 794.0567116424364033 | 501 | 158 | 147 | | Ben | Australia | ETH | 13428404.7308956467466546296 | 5.566492182258915860530 | 5.798153939300467470194 | 547.8409611043090877031 | 575.24111102831531919 | 124538.44970426124410866 | 6102.488840463623164634342 | 311.46640129618281001108 | 2433584.5235770879150933104 | 4442.13685422790551869 | 216.497825549417380 | 877 | 44 | 148 | | Ayush | United States | ETH | 1311604.529255899622334984 | 2.68703356830932432729 | 5.81625080031458481996 | 546.136841365536146123 | 472.423274839163292495 | 334003.599653997130003622 | 1535.33554277335488666741 | 874.60265072177019376644 | 611528.717056667941945237 | 1119.7353314008790779 | 707.0007288859948728 | 222 | 145 | 149 | | Alex | United States | ETH | 8166074.5514961468370127832 | 5.160548701564200259435 | 6.195710408303723678880 | 512.7005436032844769009 | 594.10347686987495653 | 394356.04132861779977087 | 4190.280933001530243794585 | 1055.12264813874726085400 | 1657805.3389265534531313035 | 3233.47685039578173973 | 663.783426089917745 | 651 | 136 | 150 | | Abe | United States | ETH | 6639149.360373732402400560 | 6.30636732390684560266 | 6.96696762485071066549 | 455.956218714819773258 | 402.869591682089677282 | 102436.515287599786845294 | 2830.40882752809559852513 | 280.26871378724081389402 | 1068519.299942312419013546 | 2343.4690790139731866 | 254.2671807517156776 | 452 | 49 | 151 | | Vipul | United States | BTC | 78502964.072588956226743050 | 2.68714530854748256805 | 3.68294320587067762687 | 13093.31447811552297915 | 13475.453907836528677637 | 13126372.591344438019363620 | 86003.42201666670006452538 | 33121.33857697519500985826 | 34054805.89838476266109091 | 2600.9308762349498788 | 974.09502352354264169 | 491 | 202 | 152 | | Vikram | India | BTC | 166813181.437258245953618100 | 3.52715101148526165567 | 3.95599133132907749521 | 12190.13846337579877423 | 11616.444334417796046131 | 6676932.386251731199555320 | 124651.51307436673592167471 | 16956.79224576098138040873 | 49146890.77777644033111307 | 4031.6925788360780822 | 574.78279876648434158 | 792 | 115 | 153 | | Sonia | Australia | BTC | 124284811.35069950050073250 | 3.22239151512076129431 | 3.98017619235346804918 | 12116.19259536080282520 | 13301.41484742083517680 | 12188130.03561228407096717 | 106218.63998275465210197183 | 32557.52213358600760727724 | 42308380.15320625283012337 | 3491.8873912094965336 | 916.3032786678013621 | 692 | 192 | 154 | | Rowan | United States | BTC | 123967563.642518772902551350 | 3.21604584843623520833 | 3.99480787192328942564 | 12071.72576961881153034 | 14698.463685865612609155 | 14674630.646730677455969795 | 109785.90179561532962420871 | 34896.99704422013042418676 | 43064532.63337412145429443 | 3567.3882471515063849 | 998.37853535959315513 | 731 | 192 | 155 | | Pavan | Australia | BTC | 74876626.12728888362311500 | 2.96170097119995459091 | 4.19446871610550698321 | 11497.41992360170303301 | 12736.34639275082929004 | 13102135.31594895329161890 | 75946.04432629785510284083 | 35021.69278578816478913785 | 29668016.64332961950593339 | 2580.4064599247725600 | 1028.7200828179673870 | 526 | 197 | 156 | | Nandita | United States | BTC | 200751409.8030429976334624250 | 3.318954506414827841503 | 3.800967820444996477195 | 12686.9187126851255182628 | 12710.97315213559195557 | 10975745.05336688201117242 | 162919.943377863222128081502 | 29522.09286188312984442411 | 63735345.6973630892576024850 | 5023.705687783492459935 | 863.4858182768507102 | 954 | 167 | 157 | | Leah | Asia | BTC | 195244116.218934723675250200 | 3.20713866637997563156 | 3.69585020094976729586 | 13047.716421455726604292 | 14163.27115481523942743 | 13167023.04333994792518748 | 162182.80997147155924445895 | 34511.92424266569639230177 | 64922183.350145074994503074 | 4975.75064119164404784 | 929.6597445190769838 | 985 | 178 | 158 | | Enoch | Africa | BTC | 170580708.903959882263220550 | 3.32210366299759732651 | 3.81349609220645770901 | 12645.17491200523085792 | 12221.728791595667220353 | 8993630.724672519050054694 | 139549.78578174917338018370 | 22127.73733517252665241326 | 54005738.62395010934056671 | 4270.8573823425313401 | 735.87222217343213249 | 820 | 146 | 159 | | Danny | Australia | BTC | 159477000.13128947514105900 | 3.15215421180060014193 | 3.86323307663085347221 | 12482.56627031336321059 | 12136.89819062786950087 | 13053993.84143928202879456 | 139898.65835627135429570787 | 33921.86482473383782701913 | 54679169.18667898299926584 | 4380.4429315724604872 | 1075.5626055691556454 | 841 | 216 | 160 | | Charlie | United States | BTC | 122534214.004535973744886950 | 3.72521619894663216642 | 4.00865603400732590012 | 12029.849955058029497028 | 16533.3629303089026886 | 5048387.7946428438527447 | 89519.44751889309135946118 | 13459.650465263838174873 | 34220731.332920134486598673 | 2844.65155099725936589 | 305.345489355233177 | 557 | 51 | 161 | | Ben | Australia | BTC | 174495457.85993726210654700 | 3.58080995618084189922 | 3.72643936325482578725 | 12940.57274048096941435 | 12007.5028729516103273 | 2379058.8188878469437270 | 124879.65939037520247690716 | 6292.82529374197896891580 | 49358482.11912615398782341 | 3814.2424689381731354 | 198.131022250011036 | 738 | 38 | 162 | | Ayush | United States | BTC | 190375533.507735667440266700 | 3.60665921157546573899 | 3.94587234371446856760 | 12221.424291634983319324 | 11627.47349802419417012 | 5849092.67497492474683032 | 137094.21663401017780783864 | 15782.14185896643545309336 | 54363813.801684160086909672 | 4448.23880624893711454 | 503.0407229884321422 | 879 | 95 | 163 | | Alex | United States | BTC | 106225322.51226045757462950 | 3.17134902464366229781 | 3.64732289428635720234 | 13221.31584224600059279 | 10912.7808704054692250 | 4864305.0963092239886611 | 86273.62341244515836663134 | 12991.807119280435744693 | 34997838.87410784294077066 | 2647.0768334782105019 | 445.743862547520261 | 534 | 84 | 164 | | Abe | United States | BTC | 179533509.46040851816270350 | 3.45022907603633744257 | 4.02573821957975606285 | 11978.89286334087078326 | 11297.72474955164239817 | 9629772.49687926822047538 | 142276.80367018421615985439 | 24216.72648756990733881614 | 54778040.60600280511777383 | 4572.8800842388871361 | 852.3638794847991004 | 900 | 171 | 165 |
166 | 167 | ## Question 1 168 | 169 | > Which mentors have the greatest actual profitability for each ticker? 170 | 171 |

Click here to reveal the solution!

187 | 188 | | first_name | ticker | actual_profitability | profitability_rank | 189 | | ---------- | ------ | ---------------------- | ------------------ | 190 | | Charlie | BTC | 3.72521619894663216642 | 1 | 191 | | Abe | ETH | 6.30636732390684560266 | 1 | 192 |
193 | 194 | ## Question 2 195 | 196 | > Which mentors have the greatest difference in actual vs theoretical profitability for each ticker? 197 | 198 |

Click here to reveal the solution!

217 | 218 | | first_name | ticker | difference | difference_rank | 219 | | ---------- | ------ | ---------------------- | --------------- | 220 | | Pavan | BTC | 1.23276774490555239230 | 1 | 221 | | Ayush | ETH | 3.12921723200526049267 | 1 | 222 |
223 | 224 | ## Question 3 225 | 226 | > What is the total amount of sales revenue made by all mentors for each region? (combined BTC and ETH) 227 | 228 |

Click here to reveal the solution!

229 | 230 | ```sql 231 | SELECT 232 | region, 233 | SUM(sales_revenue) AS total_sales 234 | FROM mentor_performance 235 | GROUP BY region 236 | ORDER BY total_sales DESC; 237 | ``` 238 | 239 |

240 | 241 | | region | total_sales | 242 | | ------------- | ---------------------------- | 243 | | United States | 66396367.0625050180915045221 | 244 | | Australia | 42437660.9781423926224266410 | 245 | | Asia | 13570583.1975923260102011970 | 246 | | Africa | 9598208.677428648539439794 | 247 | | India | 7234158.329279646489705110 | 248 |
249 | 250 | ## Question 4 251 | 252 | > What is the average actual profitability for each region for each ticker? 253 | 254 |

Click here to reveal the solution!

255 | 256 | ```sql 257 | SELECT 258 | region, 259 | ticker, 260 | AVG(actual_profitability) AS avg_profitability 261 | FROM mentor_performance 262 | GROUP BY region, ticker 263 | ORDER BY ticker, avg_profitability DESC; 264 | ``` 265 | 266 |

267 | 268 | | region | ticker | avg_profitability | 269 | | ------------- | ------ | ----------------------- | 270 | | India | BTC | 3.52715101148526165567 | 271 | | Africa | BTC | 3.32210366299759732651 | 272 | | United States | BTC | 3.310799882085806180525 | 273 | | Australia | BTC | 3.22926416357553948159 | 274 | | Asia | BTC | 3.20713866637997563156 | 275 | | United States | ETH | 4.746694009665583482267 | 276 | | Australia | ETH | 4.745561593148298488748 | 277 | | India | ETH | 4.31181306610612708721 | 278 | | Asia | ETH | 4.270686580003484588095 | 279 | | Africa | ETH | 2.927688747446586597299 | 280 |
281 | 282 | ## Question 5 283 | 284 | > Which mentors have the largest initial investment in each ticker? 285 | 286 |

Click here to reveal the solution!

302 | 303 | | first_name | ticker | initial_investment | investment_rank | 304 | | ---------- | ------ | --------------------------- | --------------- | 305 | | Leah | BTC | 64922183.350145074994503074 | 1 | 306 | | Vipul | ETH | 2900193.5795488220132164898 | 1 | 307 |
308 | 309 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step11.md) 310 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step13.md) -------------------------------------------------------------------------------- /course-content/step13.md: -------------------------------------------------------------------------------- 1 |

2 | 3 |

4 | 5 | [![forthebadge](./../images/badges/version-1.0.svg)]() 6 | [![forthebadge](https://forthebadge.com/images/badges/powered-by-coffee.svg)]() 7 | [![forthebadge](https://forthebadge.com/images/badges/built-with-love.svg)]() 8 | [![forthebadge](https://forthebadge.com/images/badges/ctrl-c-ctrl-v.svg)]() 9 | 10 | # SQL Masterclass Summary 11 | 12 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step12.md) 13 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/the-end.md) 14 | 15 | Congratulations you have reached the end of this SQL Masterclass! 16 | 17 | Here are some of the topics which you covered in this short but sweet SQL Masterclass course: 18 | 19 | > Step 2: Inspecting the `trading.members` table 20 | 21 | * Selecting rows and columns from database tables with `SELECT` 22 | * Use `LIMIT` to only return a set number of rows from a query 23 | * Counting the number of records using `COUNT(*)` 24 | * Counting the number of unique column and table records using `COUNT(DISTINCT) 25 | * Filtering data using a `WHERE` filter 26 | * Selecting `DATE` ranges using `BETWEEN`, `>`, `>=`, `<`, `<=` 27 | * Using the `IN` and `NOT IN` filter conditions to remove and keep records 28 | * Use `CASE WHEN` to apply simple if-else logic to an existing column 29 | 30 | > Step 3: Analyzing daily BTC and ETH prices in the `trading.prices` table 31 | 32 | * Finding the `MIN` and `MAX` dates 33 | * Use `GROUP BY` to aggregate data at different levels for analysis 34 | * Extracting information from dates using `DATE_TRUNC` and `EXTRACT` 35 | * Using `DATE_TRUNC` to obtain the begining date of month for a `DATE` 36 | * Using `AVG` to find the average price 37 | * Casting float data types to an exact `NUMERIC` to use with the `ROUND` function 38 | * Using `AND` conditions to apply multiple logical rules for `WHERE` filters 39 | * Using `SUM CASE WHEN` to aggregate logical values similar to a COUNTIF in Excel 40 | * Casting `INTEGER` data types to a `NUMERIC` to avoid integer floor division errors 41 | 42 | > Step 4: View all transaction histories in the `trading.transactions` table 43 | 44 | * More advanced usage of `SUM CASE WHEN` to replicate SUMIF functionality in Excel 45 | * How to filter records from a `GROUP BY` result using the `HAVING` clause 46 | * Using CTEs and subqueries to perform the same filtering of results 47 | * Use a `RANK` window function to perform custom ordering for a results set 48 | 49 | > Step 5: Starting data analysis 50 | 51 | * Interpreting entity relationship diagrams (ERDs) to visualize table joins 52 | * Analyzing ranges of data to make sure the analysis periods are aligned 53 | * Perform an `INNER JOIN` to combine datasets to select columns from both tables 54 | * Combining CTEs and joins for step-wise queries 55 | * Combining multiple aggregation functions to generate larger table outputs 56 | 57 | > Steps 6-7: Planning ahead and using base tables for data analysis 58 | 59 | * Drop and create a temporary table to re-use in future SQL queries 60 | * Add time `INTERVAL` to a date 61 | * Use `ALTER` and `UPDATE` statements to manipulate an existing temporary table 62 | * Use a custom `WINDOW FRAME` clause to specify a sliding window for cumulative metrics 63 | * Use a `SUM` window function to calculate a denominator value for percentage calculations 64 | * Use `MAX CASE WHEN` to pivot data from long to wide 65 | 66 | > Steps 8-12: Final Case Study Scenarios 67 | 68 | * Creating simplified data scenarios to better understand each question 69 | * Implementing a `SUM PRODUCT` aggregation to calculate initial investments 70 | * Performing multiple joins to the same tables with different joining conditions 71 | * Multiplying many columns to generate fees based off percentages 72 | * Calculating hypothetical scenarios and implementing complex logic using SQL 73 | * Creating a complete CTE workflow to generate a reporting dataset 74 | * Aggregating data at multiple levels to generate multiple insights 75 | 76 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step12.md) 77 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/the-end.md) -------------------------------------------------------------------------------- /course-content/step2.md: -------------------------------------------------------------------------------- 1 |

2 | 3 |

60 |

Click here to reveal the solution!

61 | 62 | ```sql 63 | SELECT * FROM trading.members 64 | LIMIT 5; 65 | ``` 66 | 67 |

68 |
69 | 70 | | member_id | first_name | region | 71 | | --------- | ---------- | ------------- | 72 | | c4ca42 | Danny | Australia | 73 | | c81e72 | Vipul | United States | 74 | | eccbc8 | Charlie | United States | 75 | | a87ff6 | Nandita | United States | 76 | | e4da3b | Rowan | United States | 77 |
78 | 79 | ### Question 2 80 | 81 | > Sort all the rows in the table by `first_name` in alphabetical order and show the top 3 rows 82 | 83 |

84 |

Click here to reveal the solution!

85 | 86 | ```sql 87 | SELECT * FROM trading.members 88 | ORDER BY first_name 89 | LIMIT 3; 90 | ``` 91 | 92 |

93 |
94 | 95 | | member_id | first_name | region | 96 | | --------- | ---------- | ------------- | 97 | | c9f0f8 | Abe | United States | 98 | | 8f14e4 | Alex | United States | 99 | | 167909 | Ayush | United States | 100 |
101 | 102 | ### Question 3 103 | 104 | > Which records from `trading.members` are from the United States region? 105 | 106 |

107 |

Click here to reveal the solution!

108 | 109 | ```sql 110 | SELECT * FROM trading.members 111 | WHERE region = 'United States'; 112 | ``` 113 | 114 |

115 |
116 | 117 | | member_id | first_name | region | 118 | | --------- | ---------- | ------------- | 119 | | c81e72 | Vipul | United States | 120 | | eccbc8 | Charlie | United States | 121 | | a87ff6 | Nandita | United States | 122 | | e4da3b | Rowan | United States | 123 | | 167909 | Ayush | United States | 124 | | 8f14e4 | Alex | United States | 125 | | c9f0f8 | Abe | United States | 126 |
127 | 128 | ### Question 4 129 | 130 | > Select only the `member_id` and `first_name` columns for members who are not from Australia 131 | 132 |

133 |

Click here to reveal the solution!

134 | 135 | ```sql 136 | SELECT 137 | member_id, 138 | first_name 139 | FROM trading.members 140 | WHERE region != 'Australia'; 141 | ``` 142 | 143 |

144 |
145 | 146 | | member_id | first_name | 147 | | --------- | ---------- | 148 | | c81e72 | Vipul | 149 | | eccbc8 | Charlie | 150 | | a87ff6 | Nandita | 151 | | e4da3b | Rowan | 152 | | 167909 | Ayush | 153 | | 8f14e4 | Alex | 154 | | c9f0f8 | Abe | 155 | | d3d944 | Enoch | 156 | | 6512bd | Vikram | 157 | | c20ad4 | Leah | 158 |
159 | 160 | ### Question 5 161 | 162 | > Return the unique `region` values from the `trading.members` table and sort the output by reverse alphabetical order 163 | 164 |

165 |

Click here to reveal the solution!

166 | 167 | ```sql 168 | SELECT DISTINCT region 169 | FROM trading.members 170 | ORDER BY region DESC; 171 | ``` 172 | 173 |

174 |
175 | 176 | | region | 177 | | ------------- | 178 | | United States | 179 | | India | 180 | | Australia | 181 | | Asia | 182 | | Africa | 183 |
184 | 185 | ### Question 6 186 | 187 | > How many mentors are there from Australia or the United States? 188 | 189 |

190 |

Click here to reveal the solution!

191 | 192 | ```sql 193 | SELECT 194 | COUNT(*) AS mentor_count 195 | FROM trading.members 196 | WHERE region IN ('Australia', 'United States'); 197 | ``` 198 | 199 |

200 |
201 | 202 | | mentor_count | 203 | | ------------- | 204 | | 11 | 205 |
206 | 207 | ### Question 7 208 | 209 | > How many mentors are not from Australia or the United States? 210 | 211 |

212 |

Click here to reveal the solution!

213 | 214 | ```sql 215 | SELECT 216 | COUNT(*) AS mentor_count 217 | FROM trading.members 218 | WHERE region NOT IN ('Australia', 'United States'); 219 | ``` 220 | 221 |

222 |
223 | 224 | | mentor_count | 225 | | ------------ | 226 | | 3 | 227 |
228 | 229 | ### Question 8 230 | 231 | > How many mentors are there per region? Sort the output by regions with the most mentors to the least 232 | 233 |

234 |

Click here to reveal the solution!

235 | 236 | ```sql 237 | SELECT 238 | region, 239 | COUNT(*) AS mentor_count 240 | FROM trading.members 241 | GROUP BY region 242 | ORDER BY mentor_count DESC; 243 | ``` 244 | 245 |

246 |
247 | 248 | | region | mentor_count | 249 | | ------------- | ------------ | 250 | | United States | 7 | 251 | | Australia | 4 | 252 | | India | 1 | 253 | | Africa | 1 | 254 | | Asia | 1 | 255 |
256 | 257 | ### Question 9 258 | 259 | > How many US mentors and non US mentors are there? 260 | 261 |

262 |

Click here to reveal the solution!

277 |
278 | 279 | | mentor_region | mentor_count | 280 | | ------------- | ------------ | 281 | | United States | 7 | 282 | | Non US | 7 | 283 |
284 | 285 | ### Question 10 286 | 287 | > How many mentors have a first name starting with a letter before `'E'`? 288 | 289 |

290 |

Click here to reveal the solution!

291 | 292 | ```sql 293 | SELECT 294 | COUNT(*) AS mentor_count 295 | FROM trading.members 296 | WHERE LEFT(first_name, 1) < 'E'; 297 | ``` 298 | 299 |

300 |
301 | 302 | | mentor_count | 303 | | ------------ | 304 | | 6 | 305 |
306 | 307 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step1.md) 308 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step3.md) 309 | 310 | ## Appendix 311 | 312 | > `SELECT *` 313 | 314 | In practice - always try to return specific columns which you are after and use `SELECT *` sparingly! 315 | 316 | > `LIMIT` 317 | 318 | Note that `LIMIT` is sometimes implemented as `TOP` in some database flavours. 319 | 320 | One must also be careful when using `LIMIT` with newer database tools such as BigQuery - although you will only return the number of rows you ask for, BQ is billed by the total number of rows scanned and a `LIMIT` will not avoid this! 321 | 322 | Best practice is to always apply `WHERE` filters on specific partitions where possible to narrow down the amount of data that must be scanned - reducing your query costs and speeding up your query execution! 323 | 324 | > `!=` or `<>` for "not equals" 325 | 326 | You might have noticed in questions 4 and 9 there are two different methods for showing "not equals" 327 | 328 | You can use both `!=` or `<>` in `WHERE` filters to exclude records. -------------------------------------------------------------------------------- /course-content/step3.md: -------------------------------------------------------------------------------- 1 |

2 | 3 |

76 |

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77 |
78 | 79 | ```sql 80 | SELECT 81 | COUNT(*) AS total_records 82 | FROM trading.prices; 83 | ``` 84 | 85 |

86 |
87 | 88 | | total_records | 89 | | ------------- | 90 | | 3404 | 91 |
92 | 93 | ### Question 2 94 | 95 | > How many records are there per `ticker` value? 96 | 97 |

98 |

Click here to reveal the solution!

99 |
100 | 101 | ```sql 102 | SELECT 103 | ticker, 104 | COUNT(*) AS record_count 105 | FROM trading.prices 106 | GROUP BY ticker; 107 | ``` 108 | 109 |

110 |
111 | 112 | | ticker | record_count | 113 | | ------ | ------------ | 114 | | BTC | 1702 | 115 | | ETH | 1702 | 116 |
117 | 118 | ### Question 3 119 | 120 | > What is the minimum and maximum `market_date` values? 121 | 122 |

123 |

Click here to reveal the solution!

124 |
125 | 126 | ```sql 127 | SELECT 128 | MIN(market_date) AS min_date, 129 | MAX(market_date) AS max_date 130 | FROM trading.prices; 131 | ``` 132 | 133 |

134 |
135 | 136 | | min_date | max_date | 137 | | ---------- | ---------- | 138 | | 2017-01-01 | 2021-08-29 | 139 |
140 | 141 | ### Question 4 142 | 143 | > Are there differences in the minimum and maximum `market_date` values for each ticker? 144 | 145 |

146 |

Click here to reveal the solution!

147 |
148 | 149 | ```sql 150 | SELECT 151 | ticker, 152 | MIN(market_date) AS min_date, 153 | MAX(market_date) AS max_date 154 | FROM trading.prices 155 | GROUP BY ticker; 156 | ``` 157 | 158 |

159 |
160 | 161 | | ticker | min_date | max_date | 162 | | ------ | ---------- | ---------- | 163 | | BTC | 2017-01-01 | 2021-08-29 | 164 | | ETH | 2017-01-01 | 2021-08-29 | 165 |
166 | 167 | ### Question 5 168 | 169 | > What is the average of the `price` column for Bitcoin records during the year 2020? 170 | 171 |

172 |

Click here to reveal the solution!

173 |
174 | 175 | ```sql 176 | SELECT 177 | AVG(price) 178 | FROM trading.prices 179 | WHERE ticker = 'BTC' 180 | AND market_date BETWEEN '2020-01-01' AND '2020-12-31'; 181 | ``` 182 | 183 |

184 |
185 | 186 | | avg | 187 | | ------------------ | 188 | | 11111.631147540984 | 189 |
190 | 191 | ### Question 6 192 | 193 | > What is the monthly average of the `price` column for Ethereum in 2020? Sort the output in chronological order and also round the average price value to 2 decimal places 194 | 195 |

Click here to reveal the solution!

210 |
211 | 212 | | month_start | average_eth_price | 213 | | ---------------------- | ----------------- | 214 | | 2020-01-01 00:00:00+00 | 156.65 | 215 | | 2020-02-01 00:00:00+00 | 238.76 | 216 | | 2020-03-01 00:00:00+00 | 160.18 | 217 | | 2020-04-01 00:00:00+00 | 171.29 | 218 | | 2020-05-01 00:00:00+00 | 207.45 | 219 | | 2020-06-01 00:00:00+00 | 235.92 | 220 | | 2020-07-01 00:00:00+00 | 259.57 | 221 | | 2020-08-01 00:00:00+00 | 401.73 | 222 | | 2020-09-01 00:00:00+00 | 367.77 | 223 | | 2020-10-01 00:00:00+00 | 375.79 | 224 | | 2020-11-01 00:00:00+00 | 486.73 | 225 | | 2020-12-01 00:00:00+00 | 622.35 | 226 |
227 | 228 | 229 | 230 | ### Question 7 231 | 232 | > Are there any duplicate `market_date` values for any `ticker` value in our table? 233 | 234 | As you inspect the output from the following SQL query - what is your final answer? 235 | 236 |

237 |

Click here to reveal the solution!

238 |
239 | 240 | ```sql 241 | SELECT 242 | ticker, 243 | COUNT(market_date) AS total_count, 244 | COUNT(DISTINCT market_date) AS unique_count 245 | FROM trading.prices 246 | GROUP BY ticker; 247 | ``` 248 | 249 |

250 |
251 | 252 | | ticker | total_count | unique_count | 253 | | ------ | ----------- | ------------ | 254 | | BTC | 1702 | 1702 | 255 | | ETH | 1702 | 1702 | 256 |
257 | 258 | ### Question 8 259 | 260 | > How many days from the `trading.prices` table exist where the `high` price of Bitcoin is over $30,000? 261 | 262 |

263 |

Click here to reveal the solution!

264 |
265 | 266 | ```sql 267 | SELECT 268 | COUNT(*) AS row_count 269 | FROM trading.prices 270 | WHERE ticker = 'BTC' 271 | AND high > 30000; 272 | ``` 273 | 274 |

275 |
276 | 277 | | row_count | 278 | | --------- | 279 | | 240 | 280 |
281 | 282 | ### Question 9 283 | 284 | > How many "breakout" days were there in 2020 where the `price` column is greater than the `open` column for each `ticker`? 285 | 286 |

287 |

Click here to reveal the solution!

288 |
289 | 290 | ```sql 291 | SELECT 292 | ticker, 293 | SUM(CASE WHEN price > open THEN 1 ELSE 0 END) AS breakout_days 294 | FROM trading.prices 295 | WHERE DATE_TRUNC('YEAR', market_date) = '2020-01-01' 296 | GROUP BY ticker; 297 | ``` 298 | 299 |

300 |
301 | 302 | | ticker | breakout_days | 303 | | ------ | ------------- | 304 | | BTC | 207 | 305 | | ETH | 200 | 306 |
307 | 308 | ### Question 10 309 | 310 | > How many "non_breakout" days were there in 2020 where the `price` column is less than the `open` column for each `ticker`? 311 | 312 |

313 |

Click here to reveal the solution!

314 |
315 | 316 | ```sql 317 | SELECT 318 | ticker, 319 | SUM(CASE WHEN price < open THEN 1 ELSE 0 END) AS non_breakout_days 320 | FROM trading.prices 321 | -- this another way to specify the year 322 | WHERE market_date >= '2020-01-01' AND market_date <= '2020-12-31' 323 | GROUP BY ticker; 324 | ``` 325 | 326 |

327 |
328 | 329 | | ticker | non_breakout_days | 330 | | ------ | ----------------- | 331 | | BTC | 159 | 332 | | ETH | 166 | 333 |
334 | 335 | ### Question 11 336 | 337 | > What percentage of days in 2020 were breakout days vs non-breakout days? Round the percentages to 2 decimal places 338 | 339 |

340 |

Click here to reveal the solution!

362 |
363 | 364 | | ticker | breakout_percentage | non_breakout_percentage | 365 | | ------ | ------------------- | ----------------------- | 366 | | BTC | 0.57 | 0.43 | 367 | | ETH | 0.55 | 0.45 | 368 |
369 | 370 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step2.md) 371 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step4.md) 372 | 373 | # Appendix 374 | 375 | > Date Manipulations 376 | 377 | We use a variety of date manipulations in questions [5](#question-5), [6](#question-6), [9](#question-9) and [11](#question-11) to filter the `trading.prices` for 2020 values only. 378 | 379 | These are all valid methods to qualify `DATE` or `TIMESTAMP` values within a range using a `WHERE` filter: 380 | 381 | * `market_date BETWEEN '2020-01-01' AND '2020-12-31'` 382 | * `EXTRACT(YEAR FROM market_date) = 2020` 383 | * `DATE_TRUNC('YEAR', market_date) = '2020-01-01'` 384 | * `market_date >= '2020-01-01' AND market_date <= '2020-12-31'` 385 | 386 | The only additional thing to note is that `DATE_TRUNC` returns a `TIMESTAMP` data type which can be cast back to a regular `DATE` using the `::DATE` notation when used in a `SELECT` query. 387 | 388 | > `BETWEEN` Boundaries 389 | 390 | An additional note for [question 5](#question-5) - the boundaries for the `BETWEEN` clause must be `earlier-date-first` AND `later-date-second` 391 | 392 | See what happens when you reverse the order of the `DATE` boundaries using the query below - does it match your expectation? 393 | 394 |

395 |

Click here to see the "wrong" code!

396 |
397 | 398 | ```sql 399 | SELECT 400 | AVG(price) 401 | FROM trading.prices 402 | WHERE ticker = 'BTC' 403 | AND market_date BETWEEN '2020-12-31' AND '2020-01-01'; 404 | ``` 405 | 406 |

407 |
408 | 409 | > Rounding Floats/Doubles 410 | 411 | In PostgreSQL - we cannot apply the `ROUND` function directly to approximate `FLOAT` or `DOUBLE PRECISION` data types. 412 | 413 | Instead we will need to cast any outputs from functions such as `AVG` to an exact `NUMERIC` data type before we can use it with other approximation functions such as `ROUND` 414 | 415 | In [question 6](#question-6) - if we were to remove our `::NUMERIC` from our query - we would run into this error: 416 | 417 | ``` 418 | ERROR: function round(double precision, integer) does not exist 419 | LINE 3: ROUND(AVG(price), 2) AS average_eth_price 420 | ^ 421 | HINT: No function matches the given name and argument types. You might need to add explicit type casts. 422 | ``` 423 | 424 | You can try this yourself by running the below code snippet with the `::NUMERIC` removed: 425 | 426 |

427 |

Click here to see the "wrong" code!

428 |
429 | 430 | ```sql 431 | SELECT 432 | DATE_TRUNC('MON', market_date) AS month_start, 433 | ROUND(AVG(price), 2) AS average_eth_price 434 | FROM trading.prices 435 | WHERE EXTRACT(YEAR FROM market_date) = 2020 436 | GROUP BY month_start 437 | ORDER BY month_start; 438 | ``` 439 | 440 |

441 |
442 | 443 | > Integer Floor Division 444 | 445 | In [question 5](#question-5) - when dividing values in SQL it is very important to consider the data types of the numerator (the number on top) and the denominator (the number on the bottom) 446 | 447 | When there is an `INTEGER` / `INTEGER` as there is in this case - SQL will default to `FLOOR` division in this case! 448 | 449 | You can try running the same query as the solution to question 5 above - but this time remove the 2 instances of `::NUMERIC` and the decimal place rounding to see what happens! 450 | 451 | This is a super common error found in SQL queries and we usually recommend casting either the numerator or the denominator as a `NUMERIC` type using the shorthand `::NUMERIC` syntax to ensure that you will avoid the dreaded integer floor division! 452 | 453 |

454 |

Click here to see the "wrong" code!

455 |
456 | 457 | ```sql 458 | SELECT 459 | ticker, 460 | SUM(CASE WHEN price > open THEN 1 ELSE 0 END) / COUNT(*) AS breakout_percentage, 461 | SUM(CASE WHEN price < open THEN 1 ELSE 0 END) / COUNT(*) AS non_breakout_percentage 462 | FROM trading.prices 463 | WHERE market_date >= '2019-01-01' AND market_date <= '2019-12-31' 464 | GROUP BY ticker; 465 | ``` 466 | 467 |

468 |
-------------------------------------------------------------------------------- /course-content/step4.md: -------------------------------------------------------------------------------- 1 |

2 | 3 |

51 |

Click here to reveal the solution!

52 |
53 | 54 | ```sql 55 | SELECT COUNT(*) FROM trading.transactions; 56 | ``` 57 | 58 |

59 |
60 | 61 | ### Question 2 62 | 63 | > How many unique transactions are there? 64 | 65 |

66 |

Click here to reveal the solution!

67 |
68 | 69 | ```sql 70 | SELECT COUNT(DISTINCT txn_id) FROM trading.transactions; 71 | ``` 72 | 73 |

74 |
75 | 76 | | count | 77 | | ----- | 78 | | 22918 | 79 |
80 | 81 | ### Question 3 82 | 83 | > How many buy and sell transactions are there for Bitcoin? 84 | 85 |

86 |

Click here to reveal the solution!

87 |
88 | 89 | ```sql 90 | SELECT 91 | txn_type, 92 | COUNT(*) AS transaction_count 93 | FROM trading.transactions 94 | WHERE ticker = 'BTC' 95 | GROUP BY txn_type; 96 | ``` 97 | 98 |

99 |
100 | 101 | | txn_type | transaction_count | 102 | | -------- | ----------------- | 103 | | SELL | 2044 | 104 | | BUY | 10440 | 105 |
106 | 107 | ### Question 4 108 | 109 | > For each year, calculate the following buy and sell metrics for Bitcoin: 110 | 111 | * total transaction count 112 | * total quantity 113 | * average quantity per transaction 114 | 115 | Also round the quantity columns to 2 decimal places. 116 | 117 |

118 |

Click here to reveal the solution!

135 |
136 | 137 | | txn_year | txn_type | transaction_count | total_quantity | average_quantity | 138 | | -------- | -------- | ----------------- | -------------- | ---------------- | 139 | | 2017 | BUY | 2261 | 12069.58 | 5.34 | 140 | | 2017 | SELL | 419 | 2160.22 | 5.16 | 141 | | 2018 | BUY | 2204 | 11156.06 | 5.06 | 142 | | 2018 | SELL | 433 | 2145.05 | 4.95 | 143 | | 2019 | BUY | 2192 | 11114.43 | 5.07 | 144 | | 2019 | SELL | 443 | 2316.24 | 5.23 | 145 | | 2020 | BUY | 2350 | 11748.76 | 5.00 | 146 | | 2020 | SELL | 456 | 2301.98 | 5.05 | 147 | | 2021 | BUY | 1433 | 7161.32 | 5.00 | 148 | | 2021 | SELL | 293 | 1478.00 | 5.04 | 149 |
150 | 151 | ### Question 5 152 | 153 | > What was the monthly total quantity purchased and sold for Ethereum in 2020? 154 | 155 |

156 |

Click here to reveal the solution!

172 |
173 | 174 | | calendar_month | buy_quantity | sell_quantity | 175 | | -------------- | ----------------- | ------------------ | 176 | | 2020-01-01 | 801.0541163041565 | 158.1272716986775 | 177 | | 2020-02-01 | 687.8912804600265 | 160.06533517839912 | 178 | | 2020-03-01 | 804.2368342042604 | 182.1895644691428 | 179 | | 2020-04-01 | 761.87446914631 | 203.16695745059786 | 180 | | 2020-05-01 | 787.4238801914097 | 149.08328330131854 | 181 | | 2020-06-01 | 787.4659503521506 | 208.3362898912813 | 182 | | 2020-07-01 | 890.7807530272569 | 117.01628097387932 | 183 | | 2020-08-01 | 800.6004484214079 | 178.5423079909115 | 184 | | 2020-09-01 | 767.654783160818 | 118.86826373014458 | 185 | | 2020-10-01 | 744.7913667867248 | 174.269279883162 | 186 | | 2020-11-01 | 698.0915637008526 | 163.74629299419385 | 187 | | 2020-12-01 | 752.4121935735661 | 212.77643601396653 | 188 |
189 | 190 | ### Question 6 191 | 192 | > Summarise all buy and sell transactions for each `member_id` by generating 1 row for each member with the following additional columns: 193 | 194 | * Bitcoin buy quantity 195 | * Bitcoin sell quantity 196 | * Ethereum buy quantity 197 | * Ethereum sell quantity 198 | 199 |

200 |

Click here to reveal the solution!

201 |
202 | 203 | ```sql 204 | SELECT 205 | member_id, 206 | SUM( 207 | CASE 208 | WHEN ticker = 'BTC' AND txn_type = 'BUY' THEN quantity 209 | ELSE 0 210 | END 211 | ) AS btc_buy_qty, 212 | SUM( 213 | CASE 214 | WHEN ticker = 'BTC' AND txn_type = 'SELL' THEN quantity 215 | ELSE 0 216 | END 217 | ) AS btc_sell_qty, 218 | SUM( 219 | CASE 220 | WHEN ticker = 'ETH' AND txn_type = 'BUY' THEN quantity 221 | ELSE 0 222 | END 223 | ) AS eth_buy_qty, 224 | SUM( 225 | CASE 226 | WHEN ticker = 'BTC' AND txn_type = 'SELL' THEN quantity 227 | ELSE 0 228 | END 229 | ) AS eth_sell_qty 230 | FROM trading.transactions 231 | GROUP BY member_id; 232 | ``` 233 | 234 |

235 |
236 | 237 | | member_id | btc_buy_qty | btc_sell_qty | eth_buy_qty | eth_sell_qty | 238 | | --------- | ----------------------- | --------------------- | ---------------------- | --------------------- | 239 | | d3d944 | 4270.8573823425313401 | 735.87222217343213249 | 1744.65425673609669642 | 735.87222217343213249 | 240 | | c20ad4 | 4975.75064119164404784 | 929.6597445190769838 | 2187.1154401373141792 | 929.6597445190769838 | 241 | | c9f0f8 | 4572.8800842388871361 | 852.3638794847991004 | 2343.4690790139731866 | 852.3638794847991004 | 242 | | eccbc8 | 2844.65155099725936589 | 305.345489355233177 | 2573.754757641582429 | 305.345489355233177 | 243 | | 167909 | 4448.23880624893711454 | 503.0407229884321422 | 1119.7353314008790779 | 503.0407229884321422 | 244 | | c81e72 | 2600.9308762349498788 | 974.09502352354264169 | 4852.52157101720575379 | 974.09502352354264169 | 245 | | e4da3b | 3567.3882471515063849 | 998.37853535959315513 | 2053.9833252960165058 | 998.37853535959315513 | 246 | | c51ce4 | 2580.4064599247725600 | 1028.7200828179673870 | 2394.7300314796354124 | 1028.7200828179673870 | 247 | | a87ff6 | 5023.705687783492459935 | 863.4858182768507102 | 3822.0371970017654265 | 863.4858182768507102 | 248 | | 8f14e4 | 2647.0768334782105019 | 445.743862547520261 | 3233.47685039578173973 | 445.743862547520261 | 249 | | 45c48c | 3814.2424689381731354 | 198.131022250011036 | 4442.13685422790551869 | 198.131022250011036 | 250 | | c4ca42 | 4380.4429315724604872 | 1075.5626055691556454 | 4516.5972484100717280 | 1075.5626055691556454 | 251 | | 6512bd | 4031.6925788360780822 | 574.78279876648434158 | 2941.2223099752008596 | 574.78279876648434158 | 252 | | aab323 | 3491.8873912094965336 | 916.3032786678013621 | 4373.76210149024236043 | 916.3032786678013621 | 253 |
254 | 255 | ### Question 7 256 | 257 | > What was the final quantity holding of Bitcoin for each member? Sort the output from the highest BTC holding to lowest 258 | 259 |

260 |

Click here to reveal the solution!

261 |
262 | 263 | ```sql 264 | SELECT 265 | member_id, 266 | SUM( 267 | CASE 268 | WHEN txn_type = 'BUY' THEN quantity 269 | WHEN txn_type = 'SELL' THEN -quantity 270 | ELSE 0 271 | END 272 | ) AS final_btc_holding 273 | FROM trading.transactions 274 | WHERE ticker = 'BTC' 275 | GROUP BY member_id 276 | ORDER BY final_btc_holding DESC; 277 | ``` 278 | 279 |

280 |
281 | 282 | | member_id | final_btc_holding | 283 | | --------- | ----------------------- | 284 | | a87ff6 | 4160.219869506641749735 | 285 | | c20ad4 | 4046.09089667256706404 | 286 | | 167909 | 3945.19808326050497234 | 287 | | c9f0f8 | 3720.5162047540880357 | 288 | | 45c48c | 3616.1114466881620994 | 289 | | d3d944 | 3534.98516016909920761 | 290 | | 6512bd | 3456.90978006959374062 | 291 | | c4ca42 | 3304.8803260033048418 | 292 | | aab323 | 2575.5841125416951715 | 293 | | e4da3b | 2569.00971179191322977 | 294 | | eccbc8 | 2539.30606164202618889 | 295 | | 8f14e4 | 2201.3329709306902409 | 296 | | c81e72 | 1626.83585271140723711 | 297 | | c51ce4 | 1551.6863771068051730 | 298 |
299 | 300 | ### Question 8 301 | 302 | > Which members have sold less than 500 Bitcoin? Sort the output from the most BTC sold to least 303 | 304 | We can actually do this in 3 different ways! 305 | 306 |

307 |

Click here to reveal the `HAVING` solution!

323 |
324 | 325 |

326 |

Click here to reveal the `CTE` solution!

345 |
346 | 347 |

348 |

Click here to reveal the `subquery` solution!

366 |
367 | 368 | | member_id | btc_sold_quantity | 369 | | --------- | ------------------- | 370 | | 8f14e4 | 445.743862547520261 | 371 | | eccbc8 | 305.345489355233177 | 372 | | 45c48c | 198.131022250011036 | 373 |
374 | 375 | ### Question 9 376 | 377 | > What is the total Bitcoin quantity for each `member_id` owns after adding all of the BUY and SELL transactions from the `transactions` table? Sort the output by descending total quantity 378 | 379 |

380 |

Click here to reveal the solution!

381 |
382 | 383 | ```sql 384 | SELECT 385 | member_id, 386 | SUM( 387 | CASE 388 | WHEN txn_type = 'BUY' THEN quantity 389 | WHEN txn_type = 'SELL' THEN -quantity 390 | END 391 | ) AS total_quantity 392 | FROM trading.transactions 393 | WHERE ticker = 'BTC' 394 | GROUP BY member_id 395 | ORDER BY total_quantity DESC; 396 | ``` 397 | 398 |

399 |
400 | 401 | | member_id | total_quantity | 402 | | --------- | ----------------------- | 403 | | a87ff6 | 4160.219869506641749735 | 404 | | c20ad4 | 4046.09089667256706404 | 405 | | 167909 | 3945.19808326050497234 | 406 | | c9f0f8 | 3720.5162047540880357 | 407 | | 45c48c | 3616.1114466881620994 | 408 | | d3d944 | 3534.98516016909920761 | 409 | | 6512bd | 3456.90978006959374062 | 410 | | c4ca42 | 3304.8803260033048418 | 411 | | aab323 | 2575.5841125416951715 | 412 | | e4da3b | 2569.00971179191322977 | 413 | | eccbc8 | 2539.30606164202618889 | 414 | | 8f14e4 | 2201.3329709306902409 | 415 | | c81e72 | 1626.83585271140723711 | 416 | | c51ce4 | 1551.6863771068051730 | 417 |
418 | 419 | ### Question 10 420 | 421 | > Which `member_id` has the highest buy to sell ratio by quantity? 422 | 423 |

424 |

Click here to reveal the solution!

425 |
426 | 427 | ```sql 428 | SELECT 429 | member_id, 430 | SUM(CASE WHEN txn_type = 'BUY' THEN quantity ELSE 0 END) / 431 | SUM(CASE WHEN txn_type = 'SELL' THEN quantity ELSE 0 END) AS buy_to_sell_ratio 432 | FROM trading.transactions 433 | GROUP BY member_id 434 | ORDER BY buy_to_sell_ratio DESC; 435 | ``` 436 | 437 |

438 |
439 | 440 | | member_id | buy_to_sell_ratio | 441 | | --------- | -------------------- | 442 | | 45c48c | 19.91269871111331881 | 443 | | a87ff6 | 7.486010484765204502 | 444 | | c9f0f8 | 6.2499141870956191 | 445 | | 8f14e4 | 5.30005322455443465 | 446 | | eccbc8 | 4.92850232946761881 | 447 | | c20ad4 | 4.65209743522270350 | 448 | | 167909 | 4.60147388258864127 | 449 | | aab323 | 4.55272149176427243 | 450 | | 6512bd | 4.52509140656952666 | 451 | | c81e72 | 4.37533523692905634 | 452 | | c4ca42 | 4.2628218979753569 | 453 | | e4da3b | 3.55762611425005570 | 454 | | d3d944 | 3.35445896964968774 | 455 | | c51ce4 | 2.3630130420937542 | 456 |
457 | 458 | ### Question 11 459 | 460 | > For each `member_id` - which month had the highest total Ethereum quantity sold`? 461 | 462 |

463 |

Click here to reveal the solution!

487 |
488 | 489 | | member_id | calendar_month | sold_eth_quantity | 490 | | --------- | -------------- | ------------------- | 491 | | c51ce4 | 2017-05-01 | 66.092440429535028 | 492 | | d3d944 | 2020-04-01 | 60.417369973983352 | 493 | | 6512bd | 2018-05-01 | 47.93285714951591 | 494 | | 167909 | 2020-12-01 | 45.92423664055218 | 495 | | c81e72 | 2018-08-01 | 41.26728177476413 | 496 | | aab323 | 2018-09-01 | 41.1750763370983665 | 497 | | c4ca42 | 2021-04-01 | 40.113474724022574 | 498 | | c20ad4 | 2017-12-01 | 37.71908498970638 | 499 | | eccbc8 | 2021-03-01 | 36.485934922948275 | 500 | | 8f14e4 | 2017-07-01 | 36.17383743681231 | 501 | | e4da3b | 2019-01-01 | 30.48442641077632 | 502 | | a87ff6 | 2020-07-01 | 27.28919836842423 | 503 | | 45c48c | 2020-01-01 | 20.21523406425370 | 504 | | c9f0f8 | 2020-11-01 | 15.931855129247867 | 505 | 506 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step3.md) 507 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step5.md) -------------------------------------------------------------------------------- /course-content/step5.md: -------------------------------------------------------------------------------- 1 |

2 | 3 |

Click here to reveal the solution!

40 | 41 | ```sql 42 | SELECT 43 | MIN(txn_date) AS earliest_date, 44 | MAX(txn_date) AS latest_date 45 | FROM trading.transactions; 46 | ``` 47 | 48 |

49 | 50 | | earliest_date | latest_date | 51 | | ------------- | ----------- | 52 | | 2017-01-01 | 2021-08-27 | 53 | 54 | ### Question 2 55 | 56 | > What is the range of `market_date` values available in the prices data? 57 | 58 |

Click here to reveal the solution!

59 | 60 | ```sql 61 | SELECT 62 | MIN(market_date) AS earliest_date, 63 | MAX(market_date) AS latest_date 64 | FROM trading.prices; 65 | ``` 66 | 67 |

68 | 69 | | earliest_date | latest_date | 70 | | ------------- | ----------- | 71 | | 2017-01-01 | 2021-08-29 | 72 |
73 | 74 | ## Joining our Datasets 75 | 76 | Now that we now our date ranges are from January 2017 through to almost the end of August 2021 for both our prices and transactions datasets - we can now get started on joining these two tables together! 77 | 78 | Let's make use of our ERD shown above to combine the `trading.transactions` table and the `trading.members` table to answer a few simple questions about our mentors! 79 | 80 | ### Question 3 81 | 82 | > Which top 3 mentors have the most Bitcoin quantity as of the 29th of August? 83 | 84 |

Click here to reveal the solution!

105 | 106 | | first_name | total_quantity | 107 | | ---------- | ----------------------- | 108 | | Nandita | 4160.219869506641749735 | 109 | | Leah | 4046.09089667256706404 | 110 | | Ayush | 3945.19808326050497234 | 111 |
112 | 113 | ## Calculating Portfolio Value 114 | 115 | Now let's combine all 3 tables together using only strictly `INNER JOIN` so we can utilise all of our datasets together. 116 | 117 | ### Question 4 118 | 119 | > What is total value of all Ethereum portfolios for each region at the end date of our analysis? Order the output by descending portfolio value 120 | 121 |

Click here to reveal the solution!

157 | 158 | | region | ethereum_value | avg_ethereum_value | 159 | | ------------- | ---------------------------- | ------------------------- | 160 | | Australia | 40076021.0922707343527642712 | 10752.8900167080049298064 | 161 | | United States | 50688412.2772532532882719016 | 10549.0972481276281626456 | 162 | | Asia | 5011670.9776990206825808176 | 8933.4598532959370421432 | 163 | | India | 6276426.482786365114210656 | 8036.397545181005116104 | 164 | | Africa | 2183933.3382704268238606128 | 3899.8809611971907658600 | 165 |
166 | 167 | ### Question 5 168 | 169 | > What is the average value of each Ethereum portfolio in each region? Sort this output in descending order 170 | 171 |

Click here to reveal the solution!

201 | 202 | | region | avg_ethereum_value | 203 | | ------------- | ------------------------- | 204 | | Australia | 10752.8900167080049298064 | 205 | | United States | 10549.0972481276281626456 | 206 | | Asia | 8933.4598532959370421432 | 207 | | India | 8036.397545181005116104 | 208 | | Africa | 3899.8809611971907658600 | 209 |
210 | 211 | Mmm hang on a second...does the output for the above query look correct to you? 212 | 213 | Let's try again - this time we will calculate the total sum of portfolio value and then manually divide it by the total number of mentors in each region! 214 | 215 |

Click here to reveal the solution!

253 | 254 | | region | ethereum_value | mentor_count | avg_ethereum_value | 255 | | ------------- | ---------------------------- | ------------ | ---------------------------- | 256 | | Australia | 40076021.0922707343527642712 | 4 | 10019005.2730676835881910678 | 257 | | United States | 50688412.2772532532882719016 | 7 | 7241201.7538933218983245574 | 258 | | India | 6276426.482786365114210656 | 1 | 6276426.482786365114210656 | 259 | | Asia | 5011670.9776990206825808176 | 1 | 5011670.9776990206825808176 | 260 | | Africa | 2183933.3382704268238606128 | 1 | 2183933.3382704268238606128 | 261 |
262 | 263 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step4.md) 264 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step6.md) -------------------------------------------------------------------------------- /course-content/step6.md: -------------------------------------------------------------------------------- 1 |

2 | 3 |

Click here to reveal the solution!

85 | 86 | ### Step 2 87 | 88 | Let's take a look at our base table now to see what data we have to play with - to keep things simple, let's take a look at Abe's data from our new temp table `temp_portfolio_base` 89 | 90 | > Inspect the `year_end`, `ticker` and `yearly_quantity` values from our new temp table `temp_portfolio_base` for Mentor Abe only. Sort the output with ordered BTC values followed by ETH values 91 | 92 |

Click here to reveal the solution!

93 | 94 | 95 | ```sql 96 | SELECT 97 | year_end, 98 | ticker, 99 | yearly_quantity 100 | FROM temp_portfolio_base 101 | WHERE first_name = 'Abe' 102 | ORDER BY ticker, year_end; 103 | ``` 104 | 105 |

106 | 107 | | year_end | ticker | yearly_quantity | 108 | | ---------- | ------ | -------------------- | 109 | | 2017-12-31 | BTC | 861.0106371411443039 | 110 | | 2018-12-31 | BTC | 755.1495855476883388 | 111 | | 2019-12-31 | BTC | 765.655338171040942 | 112 | | 2020-12-31 | BTC | 859.3718776810842491 | 113 | | 2017-12-31 | ETH | 543.2120486925716504 | 114 | | 2018-12-31 | ETH | 350.000100283493089 | 115 | | 2019-12-31 | ETH | 464.317705594980087 | 116 | | 2020-12-31 | ETH | 508.4673343549910666 | 117 |
118 | 119 | We can see from the output above that the yearly quantity is exactly the total portfolio quantity values that we need - we will need to create a cumulative sum of the `yearly_quantity` column that is separate for each mentor and ticker, using the `year_end` as the ordering column. 120 | 121 | We can do exactly this using a SQL window function! 122 | 123 | ### Step 3 124 | 125 | To create the cumulative sum - we'll need to apply a window function! 126 | 127 | Although we will only touch on this briefly in this course - the complete Data With Danny [Serious SQL course](https://www.datawithdanny.com/courses/serious-sql) covers this topic and many other SQL concepts in a lot of depth! 128 | 129 | > Create a cumulative sum for Abe which has an independent value for each ticker 130 | 131 |

Click here to reveal the solution!

132 | 133 | ```sql 134 | SELECT 135 | year_end, 136 | ticker, 137 | yearly_quantity, 138 | /* this is a multi-line comment! 139 | for this case we don't actually need first_name 140 | but we include it anyway to prepare for the next query! */ 141 | SUM(yearly_quantity) OVER ( 142 | PARTITION BY first_name, ticker 143 | ORDER BY year_end 144 | ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW 145 | ) AS cumulative_quantity 146 | FROM temp_portfolio_base 147 | WHERE first_name = 'Abe' 148 | ORDER BY ticker, year_end; 149 | ``` 150 | 151 |

152 | 153 | 154 | | year_end | ticker | yearly_quantity | cumulative_quantity | 155 | | ---------- | ------ | -------------------- | --------------------- | 156 | | 2017-12-31 | BTC | 861.0106371411443039 | 861.0106371411443039 | 157 | | 2018-12-31 | BTC | 755.1495855476883388 | 1616.1602226888326427 | 158 | | 2019-12-31 | BTC | 765.655338171040942 | 2381.8155608598735847 | 159 | | 2020-12-31 | BTC | 859.3718776810842491 | 3241.1874385409578338 | 160 | | 2017-12-31 | ETH | 543.2120486925716504 | 543.2120486925716504 | 161 | | 2018-12-31 | ETH | 350.000100283493089 | 893.2121489760647394 | 162 | | 2019-12-31 | ETH | 464.317705594980087 | 1357.5298545710448264 | 163 | | 2020-12-31 | ETH | 508.4673343549910666 | 1865.9971889260358930 | 164 |
165 | 166 | ### Step 4 167 | 168 | Now let's apply our same window function to the entire temporary dataset and start answering our questions. 169 | 170 | We can actually `ALTER` and `UPDATE` our temp table to add in an extra column with our new calculation 171 | 172 | > Generate an additional `cumulative_quantity` column for the `temp_portfolio_base` temp table 173 | 174 |

Click here to reveal the solution!

175 | 176 | ```sql 177 | -- add a column called cumulative_quantity 178 | ALTER TABLE temp_portfolio_base 179 | ADD cumulative_quantity NUMERIC; 180 | 181 | -- update new column with data 182 | UPDATE temp_portfolio_base 183 | SET (cumulative_quantity) = ( 184 | SELECT 185 | SUM(yearly_quantity) OVER ( 186 | PARTITION BY first_name, ticker 187 | ORDER BY year_end 188 | ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW 189 | ) 190 | ); 191 | ``` 192 | 193 |

194 | 195 | Now let's check that our updates to the temp table worked by inspecting Abe's records again! 196 | 197 |

Click here to reveal the solution!

212 | 213 | | year_end | ticker | yearly_quantity | cumulative_quantity | 214 | | ---------- | ------ | -------------------- | -------------------- | 215 | | 2017-01-01 | BTC | 861.0106371411443039 | 861.0106371411443039 | 216 | | 2018-01-01 | BTC | 755.1495855476883388 | 755.1495855476883388 | 217 | | 2019-01-01 | BTC | 765.655338171040942 | 765.655338171040942 | 218 | | 2020-01-01 | BTC | 859.3718776810842491 | 859.3718776810842491 | 219 | | 2021-01-01 | BTC | 479.3287662131302019 | 479.3287662131302019 | 220 | | 2017-01-01 | ETH | 543.2120486925716504 | 543.2120486925716504 | 221 | | 2018-01-01 | ETH | 350.000100283493089 | 350.000100283493089 | 222 | | 2019-01-01 | ETH | 464.317705594980087 | 464.317705594980087 | 223 | | 2020-01-01 | ETH | 508.4673343549910666 | 508.4673343549910666 | 224 | | 2021-01-01 | ETH | 223.204709336221616 | 223.204709336221616 | 225 |
226 | 227 | Wait a moment....it didn't work - the cumulative and the yearly quantity is exactly the same! 228 | 229 | This is because our `UPDATE` step only takes into account a single row at a time, which is exactly what we must not do with our window functions! 230 | 231 | We will need to create an additional temp table with our cumulative sum instead! 232 | 233 | **You must run this step for all following queries to work!** 234 | 235 |

Click here to reveal the solution!

255 | 256 | You can make sure this step is ran by checking the outputs from this query: 257 | 258 | ```sql 259 | SELECT * FROM temp_cumulative_portfolio_base LIMIT 20; 260 | ``` 261 | 262 | | first_name | region | year_end | ticker | yearly_quantity | cumulative_quantity | 263 | | ---------- | ------------- | ---------- | ------ | --------------------- | ---------------------- | 264 | | Abe | United States | 2017-12-31 | BTC | 861.0106371411443039 | 861.0106371411443039 | 265 | | Abe | United States | 2018-12-31 | BTC | 755.1495855476883388 | 1616.1602226888326427 | 266 | | Abe | United States | 2019-12-31 | BTC | 765.655338171040942 | 2381.8155608598735847 | 267 | | Abe | United States | 2020-12-31 | BTC | 859.3718776810842491 | 3241.1874385409578338 | 268 | | Abe | United States | 2017-12-31 | ETH | 543.2120486925716504 | 543.2120486925716504 | 269 | | Abe | United States | 2018-12-31 | ETH | 350.000100283493089 | 893.2121489760647394 | 270 | | Abe | United States | 2019-12-31 | ETH | 464.317705594980087 | 1357.5298545710448264 | 271 | | Abe | United States | 2020-12-31 | ETH | 508.4673343549910666 | 1865.9971889260358930 | 272 | | Alex | United States | 2017-12-31 | BTC | 453.4749593742834454 | 453.4749593742834454 | 273 | | Alex | United States | 2018-12-31 | BTC | 447.1910423241274346 | 900.6660016984108800 | 274 | | Alex | United States | 2019-12-31 | BTC | 490.959718475924108 | 1391.6257201743349880 | 275 | | Alex | United States | 2020-12-31 | BTC | 444.259179847377622 | 1835.8849000217126100 | 276 | | Alex | United States | 2017-12-31 | ETH | 678.274023865761511 | 678.274023865761511 | 277 | | Alex | United States | 2018-12-31 | ETH | 546.83620089990823574 | 1225.11022476566974674 | 278 | | Alex | United States | 2019-12-31 | ETH | 476.8692888907140746 | 1701.97951365638382134 | 279 | | Alex | United States | 2020-12-31 | ETH | 621.5582550264365449 | 2323.53776868282036624 | 280 | | Ayush | United States | 2017-12-31 | BTC | 794.5344541497821383 | 794.5344541497821383 | 281 | | Ayush | United States | 2018-12-31 | BTC | 955.3494738695000753 | 1749.8839280192822136 | 282 | | Ayush | United States | 2019-12-31 | BTC | 743.2345666894748266 | 2493.1184947087570402 | 283 | | Ayush | United States | 2020-12-31 | BTC | 954.85594846498402504 | 3447.97444317374106524 | 284 | 285 | 286 | Now that we've obtained our base table properly - let's start answering some of these questions! 287 | 288 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step5.md) 289 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step7.md) -------------------------------------------------------------------------------- /course-content/step7.md: -------------------------------------------------------------------------------- 1 |

2 | 3 |

Click here to reveal the entire data prep SQL script!

65 | 66 | Here is the ERD diagram below just in case you forgot about how all the tables are joined together! 67 | 68 | ![Crypto Case Study ERD](assets/crypto-erd.png) 69 | 70 | ## Question 1 71 | 72 | > What is the total portfolio value for each mentor at the end of 2020? 73 | 74 | We can now inner join our `trading.prices` table (I hope you haven't forgot about this one yet!) to our new temp table `temp_cumulative_portfolio_base` 75 | 76 | Let's also order our results by highest portfolio value to lowest rounded to 2 decimal places. 77 | 78 |

Click here to reveal the solution!

97 | 98 | | first_name | portfolio_value | 99 | | ---------- | --------------- | 100 | | Nandita | 105391731.03 | 101 | | Leah | 100724284.81 | 102 | | Ayush | 100071736.51 | 103 | | Abe | 95203693.60 | 104 | | Ben | 92722998.25 | 105 | | Enoch | 88346609.78 | 106 | | Vikram | 88000702.69 | 107 | | Danny | 84696448.83 | 108 | | Sonia | 67932183.73 | 109 | | Rowan | 67241367.69 | 110 | | Charlie | 66761769.49 | 111 | | Alex | 54857750.71 | 112 | | Vipul | 43123911.62 | 113 | | Pavan | 41764439.70 | 114 |
115 | 116 | ## Question 2 117 | 118 | > What is the total portfolio value for each region at the end of 2019? 119 | 120 | Let's also perform the same ordering and rounding for this query too. 121 | 122 |

Click here to reveal the solution!

141 | 142 | | region | portfolio_value | 143 | | ------------- | --------------- | 144 | | United States | 98795362.60 | 145 | | Australia | 52861452.00 | 146 | | Asia | 18305366.24 | 147 | | India | 16168831.76 | 148 | | Africa | 16078915.65 | 149 |
150 | 151 | ## Question 3 152 | 153 | > What percentage of regional portfolio values does each mentor contribute at the end of 2018? 154 | 155 | Let's make our percentages between 0 and 100, rounded to decimal places - order the output with the regions with highest portfolio value first and then by descending mentor contributions within each group. 156 | 157 |

Click here to reveal the solution!

193 | 194 | | region | first_name | contribution_percentage | 195 | | ------------- | ---------- | ----------------------- | 196 | | United States | Nandita | 20.00 | 197 | | United States | Ayush | 18.61 | 198 | | United States | Abe | 17.45 | 199 | | United States | Rowan | 14.34 | 200 | | United States | Charlie | 12.52 | 201 | | United States | Alex | 10.00 | 202 | | United States | Vipul | 7.08 | 203 | | Australia | Danny | 31.55 | 204 | | Australia | Ben | 30.41 | 205 | | Australia | Sonia | 24.53 | 206 | | Australia | Pavan | 13.51 | 207 | | Asia | Leah | 100.00 | 208 | | Africa | Enoch | 100.00 | 209 | | India | Vikram | 100.00 | 210 |
211 | 212 | ## Question 4 213 | 214 | > Does this region contribution percentage change when we look across both Bitcoin and Ethereum portfolios independently at the end of 2017? 215 | 216 | We can use a similar approach to question 3 - but we will need to avoid the first level of aggregation. 217 | 218 |

Click here to reveal the solution!

255 | 256 | | region | first_name | ticker | contribution_percentage | 257 | | ------------- | ---------- | ------ | ----------------------- | 258 | | Africa | Enoch | BTC | 100.00 | 259 | | Asia | Leah | BTC | 100.00 | 260 | | Australia | Ben | BTC | 32.96 | 261 | | Australia | Danny | BTC | 29.99 | 262 | | Australia | Sonia | BTC | 21.86 | 263 | | Australia | Pavan | BTC | 15.19 | 264 | | India | Vikram | BTC | 100.00 | 265 | | United States | Nandita | BTC | 20.99 | 266 | | United States | Abe | BTC | 17.69 | 267 | | United States | Ayush | BTC | 16.32 | 268 | | United States | Rowan | BTC | 14.65 | 269 | | United States | Charlie | BTC | 12.13 | 270 | | United States | Alex | BTC | 9.32 | 271 | | United States | Vipul | BTC | 8.91 | 272 | | Africa | Enoch | ETH | 100.00 | 273 | | Asia | Leah | ETH | 100.00 | 274 | | Australia | Ben | ETH | 34.34 | 275 | | Australia | Danny | ETH | 30.06 | 276 | | Australia | Sonia | ETH | 29.93 | 277 | | Australia | Pavan | ETH | 5.66 | 278 | | India | Vikram | ETH | 100.00 | 279 | | United States | Nandita | ETH | 21.21 | 280 | | United States | Vipul | ETH | 20.91 | 281 | | United States | Alex | ETH | 18.18 | 282 | | United States | Abe | ETH | 14.56 | 283 | | United States | Charlie | ETH | 12.36 | 284 | | United States | Rowan | ETH | 10.11 | 285 | | United States | Ayush | ETH | 2.66 | 286 |
287 | 288 | ## Bonus Question 5 289 | 290 | > Calculate the ranks for each mentor in the US and Australia for each year and ticker 291 | 292 | The final output we wish to generate looks like this: 293 | 294 | | region | first_name | BTC 2017 | BTC 2018 | BTC 2019 | BTC 2020 | ETH 2017 | ETH 2018 | ETH 2019 | ETH 2020 | 295 | | ------------- | ---------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | 296 | | Australia | Ben | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 297 | | Australia | Danny | 2 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 298 | | Australia | Sonia | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 299 | | Australia | Pavan | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 300 | | United States | Nandita | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 301 | | United States | Abe | 2 | 3 | 3 | 3 | 4 | 4 | 4 | 4 | 302 | | United States | Ayush | 3 | 2 | 2 | 2 | 7 | 7 | 7 | 7 | 303 | | United States | Rowan | 4 | 4 | 4 | 4 | 6 | 6 | 6 | 6 | 304 | | United States | Charlie | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 305 | | United States | Alex | 6 | 6 | 6 | 6 | 3 | 3 | 3 | 3 | 306 | | United States | Vipul | 7 | 7 | 7 | 7 | 2 | 1 | 1 | 1 | 307 |
308 | 309 | Our first step is to try and create a long table first with all of our ranks for each ticker and year end. 310 | 311 |

Click here to reveal the solution!

329 | 330 | Let's now pivote this long table to a slightly easier to read wide table 331 | 332 |

Click here to reveal the solution!

365 | 366 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step6.md) 367 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step8.md) -------------------------------------------------------------------------------- /course-content/step8.md: -------------------------------------------------------------------------------- 1 |

2 | 3 |

Click here to reveal the solution!

73 | 74 | ```sql 75 | SELECT 76 | SUM(transactions.quantity * prices.price) AS initial_value, 77 | SUM(transactions.quantity * prices.price * transactions.percentage_fee / 100) AS fees 78 | FROM leah_hodl_strategy AS transactions 79 | INNER JOIN trading.prices 80 | ON transactions.ticker = prices.ticker 81 | AND transactions.txn_date = prices.market_date; 82 | ``` 83 | 84 |

85 | 86 | | initial_value | fees | 87 | | ------------- | -------------------- | 88 | | 50180.00 | 150.5400000000000000 | 89 |
90 | 91 | ### Question 3 92 | 93 | > The final value of her portfolio on August 29th 2021 94 | 95 |

Click here to reveal the solution!

96 | 97 | ```sql 98 | SELECT 99 | SUM(transactions.quantity * prices.price) AS final_value 100 | FROM leah_hodl_strategy AS transactions 101 | INNER JOIN trading.prices 102 | ON transactions.ticker = prices.ticker 103 | WHERE prices.market_date = '2021-08-29'; 104 | ``` 105 | 106 |

107 | 108 | | final_value | 109 | | ----------- | 110 | | 2571642.00 | 111 | 112 | ### Question 4 113 | 114 | > Calculate the profitability by dividing Leah's final value by initial value 115 | 116 | We can actually do one better and combine all 4 metrics into a single query! 117 | 118 |

Click here to reveal the solution!

145 | 146 | | initial_value | fees | final_value | profitability | 147 | | ------------- | -------------------- | ----------- | ------------------- | 148 | | 50180.00 | 150.5400000000000000 | 2571642.00 | 51.2483459545635711 | 149 |
150 | 151 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step8.md) 152 | [![forthebadge](./../images/badges/go-to-next-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step10.md) -------------------------------------------------------------------------------- /course-content/the-end.md: -------------------------------------------------------------------------------- 1 |

2 | 3 |

25 | 26 |
27 | 28 | If you're interested in learning valuable data science skills with Danny directly - you can checkout the [Data With Danny](https://www.datawithdanny.com) website for more details! 29 | 30 | Join our private student community with over 1,000 data professionals, join a local study group, get help from our team of 12+ data mentors and learn more about our personalized mentorship initiatives! 31 | 32 | The 1st part of the Data With Danny program is Serious SQL which is like this SQL course but on an entirely differently level. If you're serious about learning SQL you won't want to miss this course! 33 | 34 | Our first live SQL training cohort begins in November 2021 - you can gain all access to course content, recorded videos, our private Discord and more for a one off payment of only $49 - find out more [here!](https://www.datawithdanny.com/courses/serious-sql) 35 | 36 | # 8 Week SQL Challenge Case Studies 37 | 38 | 39 |

40 | 41 |
42 | 43 | Want to test your SQL skills and tackle 8 realistic SQL case studies and get access to a collection of free SQL learning resources? 44 | 45 | Join the Data With Danny [8 Week SQL Challenge](https://www.8weeksqlchallenge.com/getting-started) for free today! 46 | 47 | Solve all 8 realistic SQL case studies designed to simulate real work scenarios and interview questions across multiple analytics domains including customer analytics, digital, banking, retail and subscriptions! 48 | 49 | # About the Instructor: Danny Ma 50 | 51 | 52 |

53 | 54 | 55 | Danny is the Chief Data Mentor at Data With Danny and the Founder & CEO of Sydney Data Science, a boutique data consultancy based out of Sydney, Australia 🇦🇺 56 | 57 | After spending the last 10 years working in almost every single role in the data ecosystem, Danny is now focused on solving difficult problems at scale re-imagining data education and recruitment, and mentoring the next generation of data professionals. 58 | 59 | He provides specialist data consultancy services: 60 | 61 | * Digital customer analytics and experimentation 62 | * Data and machine learning strategy 63 | * Data engineering and systems design 64 | * Team building for analytics and data science functions 65 | * Technical training for practitioners and management 66 | 67 | Danny is a regular speaker at global data conferences, meetups and podcasts where he shares the importance of mentorship for all data professionals. He is 68 | also a technical author and instructor for O'Reilly. 69 | 70 | Danny believes that he is living proof that dispels the myth that you need higher level education to be successful in the data science space, 71 | and he wants to share his experiences with others so they can do the same. 72 | 73 | [![forthebadge](./../images/badges/go-to-previous-tutorial.svg)](https://github.com/datawithdanny/sql-masterclass/tree/main/course-content/step13.md) -------------------------------------------------------------------------------- /data/MySQL/schema-mysql.sql: -------------------------------------------------------------------------------- 1 | CREATE SCHEMA trading; 2 | 3 | CREATE TABLE trading.members ( 4 | `member_id` VARCHAR(6), 5 | `first_name` VARCHAR(7), 6 | `region` VARCHAR(13) 7 | ); 8 | 9 | CREATE TABLE trading.prices ( 10 | `ticker` VARCHAR(3), 11 | `market_date` DATE, 12 | `price` FLOAT, 13 | `open` FLOAT, 14 | `high` FLOAT, 15 | `low` FLOAT, 16 | `volume` VARCHAR(7), 17 | `change` VARCHAR(7) 18 | ); 19 | 20 | CREATE TABLE trading.transactions ( 21 | `txn_id` INTEGER, 22 | `member_id` VARCHAR(6), 23 | `ticker` VARCHAR(3), 24 | `txn_date` DATE, 25 | `txn_type` VARCHAR(4), 26 | `quantity` FLOAT, 27 | `percentage_fee` FLOAT, 28 | `txn_time` TIMESTAMP 29 | ); 30 | 31 | /* 32 | -- Creating these indexes after loading data 33 | -- will make things run much faster!!! 34 | 35 | CREATE INDEX ON trading.prices (ticker, market_date); 36 | CREATE INDEX ON trading.transactions (txn_date, ticker); 37 | CREATE INDEX ON trading.transactions (txn_date, member_id); 38 | CREATE INDEX ON trading.transactions (member_id); 39 | CREATE INDEX ON trading.transactions (ticker); 40 | 41 | */ -------------------------------------------------------------------------------- /data/Postgres/schema-postgres.sql: -------------------------------------------------------------------------------- 1 | CREATE SCHEMA trading; 2 | 3 | CREATE TABLE trading.members ( 4 | "member_id" VARCHAR(6), 5 | "first_name" VARCHAR(7), 6 | "region" VARCHAR(13) 7 | ); 8 | 9 | CREATE TABLE trading.prices ( 10 | "ticker" VARCHAR(3), 11 | "market_date" DATE, 12 | "price" FLOAT, 13 | "open" FLOAT, 14 | "high" FLOAT, 15 | "low" FLOAT, 16 | "volume" VARCHAR(7), 17 | "change" VARCHAR(7) 18 | ); 19 | 20 | CREATE TABLE trading.transactions ( 21 | "txn_id" INTEGER, 22 | "member_id" VARCHAR(6), 23 | "ticker" VARCHAR(3), 24 | "txn_date" DATE, 25 | "txn_type" VARCHAR(4), 26 | "quantity" FLOAT, 27 | "percentage_fee" FLOAT, 28 | "txn_time" TIMESTAMP 29 | ); 30 | 31 | /* 32 | -- Creating these indexes after loading data 33 | -- will make things run much faster!!! 34 | 35 | CREATE INDEX ON trading.prices (ticker, market_date); 36 | CREATE INDEX ON trading.transactions (txn_date, ticker); 37 | CREATE INDEX ON trading.transactions (txn_date, member_id); 38 | CREATE INDEX ON trading.transactions (member_id); 39 | CREATE INDEX ON trading.transactions (ticker); 40 | 41 | */ -------------------------------------------------------------------------------- /data/members.csv: -------------------------------------------------------------------------------- 1 | member_id,first_name,region 2 | c4ca42,Danny,Australia 3 | c81e72,Vipul,United States 4 | eccbc8,Charlie,United States 5 | a87ff6,Nandita,United States 6 | e4da3b,Rowan,United States 7 | 167909,Ayush,United States 8 | 8f14e4,Alex,United States 9 | c9f0f8,Abe,United States 10 | 45c48c,Ben,Australia 11 | d3d944,Enoch,Africa 12 | 6512bd,Vikram,India 13 | c20ad4,Leah,Asia 14 | c51ce4,Pavan,Australia 15 | aab323,Sonia,Australia 16 | -------------------------------------------------------------------------------- /docker-compose.yml: -------------------------------------------------------------------------------- 1 | version: '3.5' 2 | 3 | services: 4 | db: 5 | image: dannyma/psql-crypto:latest 6 | environment: 7 | POSTGRES_HOST_AUTH_METHOD: 'trust' 8 | 9 | sqlpad: 10 | image: sqlpad/sqlpad:5.5 11 | depends_on: 12 | - db 13 | ports: 14 | - "3000:3000" 15 | environment: 16 | SQLPAD_AUTH_DISABLED: 'true' 17 | SQLPAD_AUTH_DISABLED_DEFAULT_ROLE: 'admin' 18 | SQLPAD_APP_LOG_LEVEL: debug 19 | SQLPAD_WEB_LOG_LEVEL: warn 20 | SQLPAD_CONNECTIONS__psql__name: psql 21 | SQLPAD_CONNECTIONS__psql__driver: postgres 22 | SQLPAD_CONNECTIONS__psql__host: db 23 | SQLPAD_CONNECTIONS__psql__username: postgres 24 | SQLPAD_CONNECTIONS__psql__multiStatementTransactionEnabled: 'true' 25 | SQLPAD_CONNECTIONS__psql__idleTimeoutSeconds: 86400 -------------------------------------------------------------------------------- /images/badges/click-these-badges-to-view-content!.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /images/badges/go-to-next-tutorial.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /images/badges/go-to-previous-tutorial.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /images/badges/raw-data.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /images/badges/sql-interface-db-fiddle.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /images/badges/sql-interface-play-with-docker.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /images/badges/sql-masterclass-v-1.0.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /images/badges/start-here.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /images/badges/version-1.0.svg: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /images/crypto.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datawithdanny/sql-masterclass/bb87f9be59a9fd828e75a8984e29537f10f12ec5/images/crypto.png -------------------------------------------------------------------------------- /images/sql-masterclas-banner.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datawithdanny/sql-masterclass/bb87f9be59a9fd828e75a8984e29537f10f12ec5/images/sql-masterclas-banner.png -------------------------------------------------------------------------------- /slides/sql-masterclass-odsc-apac-2021.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/datawithdanny/sql-masterclass/bb87f9be59a9fd828e75a8984e29537f10f12ec5/slides/sql-masterclass-odsc-apac-2021.pdf --------------------------------------------------------------------------------