Learn Angularfire, Postgres and Python

You've got some practice with using SQL to select, update, and join database tables.

But perhaps you've found yourself with a sneaking suspicion that you could be more efficient with your PostgreSQL.

Does your query seem slow?

A lesson on how to profile queries will point you in the direction of a fix.

Are you performing multi-step operations one at a time that are susceptible to errors?

The "all or nothing" transaction is the cure.

These are just a couple of examples of the techniques, tips, and tricks that Tyler Clark has learned in his years of experience working on large scale production applications.

Other topics in this course include working with CSV files, different techniques for aggregating & filtering, and more!

Watch Tyler's course, and continue your journey toward SQL mastery.

Check out the [**Community Notes for this course.**](https://github.com/eggheadio-projects/advanced-sql-for-professional-developers)

PostgreSQL is a powerful, open source object-relational database system.

postgres

Advanced SQL for Professional Developers

Most applications rely on databases to store data. Understanding how to create a database and communicate with it unlocks a critical power in your web development career. 

In this course, Tyler presents all the building blocks of creating and interacting with a PostgreSQL database. From creating a brand new table, up through organizing and aggregating data across multiple tables, you will learn the fundamental skills of SQL — and you’ll be prepared to apply them right away.

After completing this course, you’ll be ready to tackle more challenging SQL topics like proper data modeling, normalizing data, and exploring different database types.


Check out the [**Community Notes for this course.**](https://github.com/eggheadio-projects/sql-fundamentals)

SQL Fundamentals

Neural networks, with Keras, bring powerful machine learning to Python applications. They can answer questions like “How much traffic will hit my website tonight?” or answer classification questions like “Will this customer buy our product?” or “Will the stock price go up or down tomorrow?”

In this course, we’ll build a fully connected neural network with Keras. This is the most basic type of neural network you can create, but it’s powerful in application and can jumpstart your exploration of other frameworks. 

We’ll start the course by creating the primary network. Then we’ll:

- build and configure the network, then evaluate and test the accuracy of each
- save the model and learn how to load it and use it to make predictions in the future
- expose the model as part of a tiny web application that can be used to make predictions

You don’t need to know a lot of Python for this course, but some basic Python knowledge will be helpful. Enjoy!

Python is a widely used dynamically typed programming language. It allows for many different programming paradigms including object-oriented, imperative, functional programming, and procedural styles.

python

Fully Connected Neural Networks with Keras

In Introduction to Python, we will start at the beginning: Installing python 3 and using virtual environments. With a working environment, we will learn how to manipulate strings and numbers, and gain working knowledge of data structures and flow control. We will build on that by introducing modules and classes and learn how to interact with our python applications using command line switches, console input and files.

At the end of this course, you’ll have a great working knowledge of Python, fully capable of creating your own Python projects from scratch or jumping into an existing application with the capability to read, understand, and contribute to the application.

Check out these [**community notes for this course on Github**](https://github.com/eggheadio-projects/introduction-to-the-python-3-programming-language-notes).

Introduction to the Python 3 Programming Language

Artificial intelligence. Machine learning. Bots. Computers learning and communicating with us to do our bidding. But, where do you start? How do you get a machine to even begin to understand what you speak or type at it? There are several common machine learning algorithms that will help us begin to answer these questions.

In this course we’ll learn about common machine learning algorithms that don’t require implementing a neural network. We will not be going too much into the math behind them, but instead learn what each algorithm is good for, and how to train them. We'll also learn about a few metrics for evaluating models.

We’ll implement these in Python using `scikit-learn` using `scikit-learn`’s built-in data sets. The focus of this course is on implementation and a high-level understanding of these algorithms. 

We'll look at a few ways to evaluate our models, for both classification and regression models. We'll touch on mean squared error and coefficient of determination (for regression), and accuracy score, logarithmic loss, confusion matrices, and classification reports (for classification). 

Python 2.7 is used in the lesson videos but the code provided has Python 3 available. The only breaking change is the `print` statement API.

For additional information on installation, vocabulary, and common errors visit the `README.md` to the course code attached to each lesson.

Simple and efficient tools for data mining and data analysis

scikit-learn

Introductory Machine Learning Algorithms in Python with scikit-learn

You know the basics of Postgres, but most meaningful queries will require you to combine tables in novel ways to ask questions you couldn't ask with a single table alone. Joins and unions come up over and over again in interview questions -- because of how fundamental they are. This course will make you a master of your tables, and give you the power to answer questions you couldn't before. 

Understand Joins and Unions in Postgres

In this course we will show you how to wield the power behind the elusive window function. Which five movies were the highest grossing each year? Who are the highest performers in each department relative to their peers? These questions ask us to partition records into subgroups and answer questions about those groups -- that’s the power of a window function. 

Many programmers will reach for their favorite scripting language to solve problems like these, but window functions allow you to answer them with the speed and efficiency of the Postgres query planner.

Plus, with the power to further slice and dice this derivative information, there’s no limit to the questions you can answer with raw SQL.

If you’re interested in the Postgres client that is used in this course you can [download it here](https://macpostgresclient.com/?source=brett) to check it out and follow along.

Using Postgres Window Functions

It’s amazing how much you can accomplish with a basic knowledge of SQL. In this course we’ll learn how to store lots of information (many millions of things!) and answer nearly any question we might have about that information (on the order of milliseconds!) 

We’ll learn enough to make you say you “know SQL” -- table creation, inserts, selects, updates, deletes, aggregations, indexes, joins, and constraints. Along the way we’ll model real world problems so that you can see just how powerful SQL will make you!

If you’re interested in the Postgres client that is used in this course you can [download it here](https://macpostgresclient.com/?source=brett) to check it out and follow along.

Get Started With PostgreSQL

AWS Amplify allows developers to create, configure, and deploy Serverless APIs and functions directly from their front-end environment.In this collection, you'll learn how to deploy various types of APIs and functions using the Amplify CLI. You'll also learn how to share code among various functions using Lambda layers and schedule functions to run at an interval to implement Serverless cron jobs.

JavaScript® (often shortened to JS) is a lightweight, interpreted, object-oriented language with first-class functions, most known as the scripting language for Web pages, but used in many non-browser environments as well such as node.js or Apache CouchDB. It is a prototype-based, multi-paradigm scripting language that is dynamic, and supports object-oriented, imperative, and functional programming styles.

javascript

Up and Running with AWS Amplify Functions

Learn how to create tuples in Python. The videos in this collection will also cover more advanced features of tuples

The Ins and Outs of Python Tuples

Creating tables is actually quite simple. However before we go creating tables we need to first understand the data we want to store. To make sure our data scales appropriately we need to break down the data into its smallest form so we can easily add data and read from it. 

### Other DB Types
https://dev.mysql.com/doc/refman/8.0/en/data-types.html
https://docs.oracle.com/cd/B28359_01/server.111/b28318/datatype.htm
https://docs.microsoft.com/en-us/sql/t-sql/data-types/data-types-transact-sql?view=sql-server-2017
https://www.postgresql.org/docs/9.5/datatype.html

Create a Table with SQL Create

Tables in databases are not worth much value if they do not actually hold data. In this lesson we will use the insert statement to add rows of data to our table. We'll break down each component of the statement and review what is required for a successful insert. 


Add Data to a Table with SQL Insert

Databases are not really good for anything if we are not able to get the data out. By using the select statement, we can pull out all of the data within our database table. Let's work with different queries that pulls out all of our columns, just some of the columns, and even filters out duplicate column values. 

Query Data with the Select Command in SQL

One of the core commands in SQL is update. This is how we can modify the data that is already in our database table. Let’s update our users table and change one of our user's duplicated user handle to a new UUID. We'll even use a postgres extension to generate new UUIDs.  




**UUIDs / GUIDs**

*Postgres* - http://www.postgresqltutorial.com/postgresql-uuid/

*SQL Server*- https://docs.microsoft.com/en-us/sql/t-sql/functions/newid-transact-sql?view=sql-server-2017

*MySQL* - https://dev.mysql.com/doc/refman/8.0/en/miscellaneous-functions.html#function_uuid

*Oracle* - https://docs.oracle.com/javase/8/docs/api/java/util/UUID.html




Update Data in a Table with SQL Update

Deleting data within our table is easily done by using ether the delete, truncate, or just dropping the table altogether from our database. Let’s remove a user from our table using the delete command, remove everyone with truncate, then drop the entire table with the drop command. 

Removing Data with SQL Delete, Truncate, and Drop

SQL gives us the power to choose what data we pull out of our table. We will use the where clause within our select statement with many operators. These include the greater than, less than, equal, and not equal. We will also use some special operators such as "in", "between", and "is". 

Conditionally Select out Filtered Data with SQL Where

Another powerful SQL skill is understanding how to group rows together by column values.  Once the data is grouped together, it can be difficult to understand how to actually work with the groupings. In this lesson, we will use the group by clause, as well as the count, sum, avg, min, and max aggregate functions. 

### Counts with create date and first name example: 
```sql
SELECT u.total, u.create_date, first_name 
FROM Users us 
INNER JOIN (SELECT count(create_date) AS total, create_date 
   FROM Users
   GROUP BY create_date) u ON u.create_date = us.create_date;
```

### Min create date with first name example: 
```sql
SELECT create_date, first_name 
FROM Users 
WHERE create_date = (select min(create_date) FROM Users);
```
### Aggregate functions: 

https://www.postgresql.org/docs/9.5/functions-aggregate.html
https://dev.mysql.com/doc/refman/8.0/en/group-by-functions.html
https://docs.microsoft.com/en-us/sql/t-sql/functions/aggregate-functions-transact-sql
https://docs.oracle.com/database/121/SQLRF/functions003.htm

Select Grouped and Aggregated Data with SQL

When working with database tables, we want to make sure we keep our data as simple as possible. This can sometimes mean having multiple tables that share some data columns. Using the join statement, we can combine data between two tables on similar column data to gather more information in one query. Let’s work through some common join statements like left, right, inner, and cross join. 


Combining Tables Together with SQL Join Statements

This course is designed to take someone that has no experience with relational databases and teach them how to at least create, read, update, and delete. We'll explore many different aspects of SQL and what makes working in relational databases easy. This video also goes over how to install postgres on your machine so you can follow along. 


### Install Homebrew for Mac
https://brew.sh/

### Windows postgres installer
https://www.postgresql.org/download/windows/

Setup and Introduction to SQL Fundamentals

Relational databases can organize their data in a way that makes it more efficient to add/update/read/delete from. Similar to books, using SQL we can create indexes which defines how to our organize data behind the scenes. 

### More Info 
https://www.postgresql.org/docs/9.1/indexes.html

https://dev.mysql.com/doc/refman/8.0/en/mysql-indexes.html

https://docs.oracle.com/cd/E11882_01/server.112/e40540/indexiot.htm#CNCPT721

https://docs.microsoft.com/en-us/sql/relational-databases/indexes/clustered-and-nonclustered-indexes-described?view=sql-server-2017

Organize Table Data with Indexes

SQL is dynamic enough to handle queries within queries. These inner queries are called subqueries and they can be used in many different sections of another query. In this video we will use subqueries within a where clause, a join statement, and as a column value. 

### Answer to video challenge
```
select user_handle, sku, (select avg(quantity) from Purchases where user_handle = p.user_handle and sku = p.sku) from Purchases p group by user_handle, sku;
```

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