More Than 8 Hours of Video Instruction
Modern Python LiveLessons: Big Ideas and Little Code in Python provides developers with an approach to programming in Python that expresses big ideas succinctly, with the minimum of code, allowing the business logic to shine through.
What You Will Learn
· Use the core skills of modern Python that enable you to elegantly code powerful solutions succinctly and efficiently
· Use newer features from Python 3.6, including f-strings and type hinting
· Make statistics and data analysis easy using resampling and simulations
· ETL (extract-transform-load) techniques to prepare real-world data for analysis
· Build and apply k-means unsupervised machine learning to a real dataset
· Model and create a publisher/subscriber messaging application from scratch
· Build micro-webservice REST APIs using the Bottle micro-webframework
· Create a professional website for the messaging application
· Improve code reliability with pyflakes, py.test, mypy, hypothesis, data validators, and various testing techniques
Lesson 1: Building Foundational Python Skills for Data Analytics
Lesson 1 covers the Python tools commonly used for data analysis.
Lesson 2: Analyzing Data Using Simulations and Resampling
In Lesson 2 we apply the Python data analysis tools to building simulations and computing statistics. The techniques for resampling statistics are both powerful and easy to learn. They express big ideas with very little code.
Lesson 3: Improving Reliability with MyPy and Typing Hinting
In the first half of Lesson 3, we wade deeper into Python’s tools for organizing and analyzing data. Then, in the second half, we explore type hinting and static type checking, an exciting new capability in the world of Python.
Lesson 4: Implementing k-means Unsupervised Machine Learning
In Lesson 4, we apply the skills learned in Lesson 3 to unsupervised machine learning and implement a k-means cluster analysis tool. The end result is a clear and compact expression of business logic using modern Python.
Lesson 5: Building Additional Skills for Data Analysis
In Lesson 5, we build more sophistication in data extraction, transformation, and analysis. Modern Python provides a rich toolset for loading and preparing data for analytics.
Lesson 6: Applying Cluster Analysis to a Real Dataset
In Lesson 6, we apply the tools learned in Lesson 5 to a real dataset. We load the voting history for the 114th U.S. Congress and then apply k-means to identify voting blocks among the senators.
Lesson 7: Gearing Up for a Publisher/Subscriber Application
In Lesson 7, we show off Python’s best data structures for cleanly modeling data relationships.
Lesson 8: Implementing a Publisher/Subscriber Application
In Lesson 8, we apply the structures from Lesson 7 to a publish/subscribe application. The resulting code is fast, memory efficient, succinct, and crystal clear. This example really shows off modern Python’s capability to express big ideas with only a little code.
Lesson 9: Using Bottle to Build REST APIs and Web Applications
In Lesson 9, you learn how to build REST APIs and web applications using Bottle. We use Bottle because it is a typical modern micro-web framework. If you learn Bottle, it is easy to apply the same knowledge to other popular frameworks, such as Flask and Django.
Lesson 10: Building a Web Application for the PubSub Service
In Lesson 10, we apply the skills learned in Lesson 9 to build a website for the publisher/subscriber application we built in Lesson 7.
Lesson 11: Testing with py.test, itertools, Hypothesis, PyFlakes, MyPy, and Data Validators
In the final capstone lesson, we focus on improving quality and reliability. Lesson 11 covers testing using py.test, itertools, Hypothesis, PyFlakes, and MyPy. It also shows off a wonderful tool for preventing data corruption by validating data at the moment it is stored. The tools are notable for their brevity and clarity, again exemplifying the ability to express big ideas with only a little code.