Learn D3, 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

In this course we will learn and demonstrate the fundamental concepts and APIs of D3, on our way to building several common chart types. We’ll start with the building blocks upon which all D3 visualizations are built; things like scales, interpolators, and selections. We’ll see how to load data with D3, and how to put its full name, Data Driven Documents, into action. We’ll build column and bar charts, scatter plots, and area charts. Most importantly, we’ll gain an understanding of the conventions and patterns that underlie all D3 projects and examples, enabling you to dissect, understand, and build virtually any D3 visualization on the internet.

D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation.

Build Interactive JavaScript Charts with D3 v4

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

This course covers D3 v3. If you'd like to learn about v4 of D3 (the latest version) [click here for the course on that](https://egghead.io/courses/build-interactive-javascript-charts-with-d3-v4).

This series will gently guide you through the first steps in getting to know D3. The curve can be steep, but with a few simple tools, you'll be charting your buns off in no time flat.

Use D3 (v3) to Build Interactive Charts with JavaScript

Supabase is an open-source tool for rapidly building application backends with real-time data and authentication. In this course, you'll learn the fundamentals of Supabase. You'll learn how to model complex data using PostgreSQL and SQL, build authentication flows with Oauth, and add real-time data to a Next.js application to build a fully-featured chat application, similar to Discord.

The chat app will have different "channels," which will be powered by data inside of Supabase. Users can visit these channels and write messages, which will be sent in real-time to other current users of the platform.

After this course, you will be able to model real-world scenarios in actual relational databases, understand how to manage real-time data, and leverage key functionality provided by Supabase, such as authentication, instant APIs, and real-time subscriptions.

Supabase is an open source Firebase alternative. A service to:

- Listen to database changes.
- Query your tables, including filtering, pagination, and deeply nested relationships (like GraphQL).
- Create, update, and delete rows.
- Manage your users and their permissions.
- Interact with your database using a simple UI.

Supabase is not a 1-to-1 mapping of Firebase. Most notably, Supabase uses Postgres rather than a NoSQL store. 

supabase

Build a Real-Time Data Syncing Chat Application with Supabase and Next.js

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

Misunderstanding scope can cause problems in your application. Watch this lesson to learn how Python scope works and the hidden implications it presents. Local scope, nonlocal scope, and global scope variables are demonstrated and explained.

Understand Scope in Python

Most of D3’s native selection APIs also return the selection (or a new selection), to enable multiple method calls to be chained together. Since the code you write isn’t on the selection prototype, chaining your methods would take some extra work. `selection.call()` will call any function reference you give it, providing the selection as the first parameter, and then it returns the selection for you to ensure chaining is supported.

Better Code Organization with selection.call() with D3 v4

A module is a function extracted to a file. This allows you to import the function and use it in any other code you may write. You’ll learn how to create modules, import them, and make them stand-alone as you learn what `if __name__ == “__main__”` means in Python.

Reuse Code in Multiple Projects with Python Modules

Making SVGs responsive is unfortunately not as simple as adding some media queries. This lesson introduces the `viewBox` attribute, which is used to control how SVGs scale. We’ll also examine a reusable function that can be used to make nearly any visualization responsive.

Make D3 v4 Charts Responsive with the viewBox attribute

Why should you have to write all the code? Why can’t you leverage code written by others? I’ve got great news: you can. You’ll learn how to search pip, Python’s package installer, to find and install packages to add new features and functionality to your application.

Manage packages with pip in Python

Object oriented classes work much like classes in other languages. Learn how to create them and use them, learn the difference between class variables and instance variables, creating class methods, and learn how to create classes that inherit from other classes.

Use Python Classes

In D3, the General Update Pattern is the name given to what happens when a data join is followed by operations on the enter, update, and exit selections. When a chart's data changes over time and each update can both create new elements and destroy existing ones, the General Update pattern can help convey meaning to users. This lesson demonstrates the pattern using animated transitions on a column chart.

Animate with the General Update Pattern in D3 v4

What if you want aggregations that aren’t running totals? What if you want the sum total for the whole frame? Or the minimum value across the frame? Unbounded preceding, unbounded following, and current row help you define your frame of reference.

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