Watch Modern Python: Big Ideas and Little Code in Python LiveLessons

2017
1 Season

Modern Python: Big Ideas and Little Code in Python LiveLessons is an instructional series from Pearson that explores the fundamentals of Python programming. The course is designed for individuals who are new to coding or have some experience but want to learn more about this popular language. This program is taught by Raymond Hettinger, a well-known software engineer and developer from the Python community, and offers a comprehensive look at the key concepts, tools, and techniques used in Python.

The course consists of seven lessons, each of which is broken down into several bite-sized modules. Each module is designed to be completed in about 15-20 minutes and offers a clear and concise explanation of a specific topic. The course begins with an introduction to Python and its syntax before moving on to more advanced topics such as object-oriented programming, decorators, and metaclasses.

Throughout the course, Hettinger emphasizes the importance of writing clear, concise, and efficient code. He demonstrates how to use Python's built-in functions, data structures, and libraries to write more efficient code and avoid common programming pitfalls. He also explains how to use Python's dynamic typing and duck typing features to make code more readable and easier to update.

One of the main themes of the course is big ideas, which refers to the larger concepts and patterns that underpin Python, such as functional programming, list comprehensions, and generators. Hettinger shows how these ideas can be used to write elegant, expressive, and powerful code. The course also emphasizes the importance of understanding how Python works under the hood, including its interpreter, garbage collector, and memory management.

Another key concept of the course is little code, which refers to the idea that less code is often better. Hettinger stresses that it's important to write code that is simple, readable, and easy to understand. He shows how to use Python's built-in functions and libraries to write shorter, more expressive code that is easier to maintain and update.

Overall, Modern Python: Big Ideas and Little Code in Python LiveLessons is an excellent course for anyone looking to improve their Python coding skills. Hettinger's clear and engaging teaching style, combined with the practical examples and exercises, make this course both informative and entertaining. Whether you're a beginner or an experienced programmer, this course is sure to help you write better, more efficient code in Python.

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Seasons

11. Lesson 11: Testing with PyTest, Itertools, Hypothesis, Pyflakes, MyPy and Data Validators

May 5, 2017

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.

10. Lesson 10: Building a Web Application for the PubSub Service

May 5, 2017

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.

9. Lesson 9: Using Bottle to Build REST APIs and Web Applications

May 5, 2017

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.

8. Lesson 8: Implementing a Publisher/Subscriber Application

May 5, 2017

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.

6. Lesson 6: Applying Cluster Analysis to a Real Dataset

May 5, 2017

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.

5. Lesson 5: Building Additional Skills for Data Analysis

May 5, 2017

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.

4. Lesson 4: Implementing k-means Unsupervised Machine Learning

May 5, 2017

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.

3. Lesson 3: Improving Reliability with MyPy and Type Hinting

May 5, 2017

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.

2. Lesson 2: Analyzing Data Using Simulations and Resampling

May 5, 2017

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.

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