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-# Web Application Development with Flask
-
-In the modern day, most applications we interact with are *web* applications -
-client-server architectures delivered via HTTP and accessed with a web browser.
-As a data scientist we will generally not build such applications, but we do
-have to *understand* them and their components.
-
-## Learning Objectives
-
-- Distinguish between front-end, back-end, database, and what tasks are
-  appropriate for which
-- Create a simple Python web application that exposes an API to URL endpoints
-
-## Before Lecture
-
-Read up on [Flask](http://flask.pocoo.org/), a "microframework" for developing
-web applications. All this really means is that it's small and modular, and
-mostly just provides for URL routing and responses - for other things
-(templates, database, forms) you pick and choose your own other packages (and
-we'll give you some specific choices for this week).
-
-This is in contrast with [Django](https://www.djangoproject.com/), the most
-popular "industry-grade" Python web application framework. Django is
-*opinionated*, and comes with built-in modules for pretty much everything.
-
-There are more projects built with Django than Flask, but Flask is solidly
-second place in the Python web application ecosystem, and is a better minimal
-choice for a data scientist who just wants to get some results.
-
-## Live Lecture Task
-
-We're going to kick off with an overview of the major parts of a web application
-(front-end, back-end, database), and then develop our own prototype web
-application using Flask and SQLite. A summary of the overall picture:
-
-- Front-end: the "look-and-feel" of an application, as well as any logic that is
-  executed by client-side (browser) JavaScript (which has grown surprisingly
-  powerful)
-- Back-end: the routing and "business logic", where responses
-  (what the client sees) are built and APIs and databases are accessed
-- Database: the "source of truth", where data is persisted and updated
-
-## Assignment
-
-- Reproduce the steps from lecture: write and run a basic local Flask web
-  application
-- Create a model for `Tweet` and `User` as demonstrated in lecture, and populate
-  your local (SQLite) database with some invented data (at least 6 Tweets and 2
-  Users)
-- For tomorrow, make sure to sign up for the [Twitter Developer
-  API](https://developer.twitter.com/en/apply-for-access) and
-  [Basilica](https://www.basilica.ai/), as we will use both APIs in our
-  application to get more and real data!
-
-## Resources and Stretch Goals
-
-- [Jinja2](http://jinja.pocoo.org/) is the dominant template engine, which you
-  will use to build the look and layout of the pages in your application
-- [Flask-SQLAlchemy](http://flask-sqlalchemy.pocoo.org/2.3/) is an ORM
-  (object-relational mapping), and will let us both (a) not have to write SQL,
-  and (b) use OOP to interact with data with multiple backends (SQLite locally,
-  and PostgreSQL in the cloud)
-- [SQLAlchemy Data
-  Types](https://docs.sqlalchemy.org/en/latest/core/type_basics.html) are needed
-  to build usable models that will translate to SQL
-- Making the front-end look nice is very much a stretch goal throughout this
-  sprint - but if you are interested in it, [Picnic CSS](https://picnicss.com/)
-  and [Umbrella JS](https://umbrellajs.com/) are nice lightweight and modern
-  tools (alternatives to Bootstrap and JQuery respectively)
-- Experiment with SQLAlchemy models - you can add more fields of interest to the
-  `Tweet` and `User` models and/or add different models for other purposes