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Modern Python LiveLessons: Big Ideas and Little Code in Python

Video Description

Overview
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. It does so using a number of relevant examples from current problems, including data analytics and social media.

Description
In this video training, Raymond Hettinger starts by introducing modern Python foundational skills, tools, and techniques in the first half of the lessons. In the second part he shows you how to apply the tools and techniques to a real application.

About the Instructor

Raymond Hettinger has been a Python Core Developer since 2001 and received the Python Software Foundation Distinguished Service Award in 2014. Currently, he runs an international Python training and consulting business. He is the author of many parts of Python, including itertools, collections, sets, soerted, enumerate, and reversed.

Skill Level
Intermediate

What You Will Learn
Core skills of modern Python that enable you to elegantly code powerful solutions succinctly and efficiently:

  • How to use continuous and discreet functions in the random module, collections.Counter(), lambda, list operations, chained comparisons, and f-strings
  • How to use random.choice() and random.sample(); do resampling, bootstrapping, and significance testing; and run simulations
  • How to run static analysis on code with type hints and use static type checking
  • How to use defaultdict for grouping, key functions for data ordering, and zip* to transpose data, and how to flatten 2D data with multiple loops and list comprehension
  • How to use k-means to implement unsupervised learning
  • More defaultdict skills with which to pivot and accumulate data and reverse a one-to-many mapping
  • How to use sorted, bisect, and merge and how to conserve memory with string interning
  • How to normalize text and use the hashing tools in hashlib
  • How to use Bottle to build REST APIs and web applications
  • How to test using pytest, itertools, Hypothesis, pyflakes, mypy, and data validators
Who Should Take This Course
Developers looking to improve their modern Pythons skills

Course Requirements
  • Basic understanding of programming and development
  • Familiarity with the Python language
About LiveLessons Video Training
The LiveLessons Video Training series publishes hundreds of hands-on, expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. This professional and personal technology video series features world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, IBM Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include IT Certification, Programming, Web Development, Mobile Development, Home and Office Technologies, Business and Management, and more. View all LiveLessons on InformIT at http://www.informit.com/livelessons.

Table of Contents

  1. Introduction
    1. Modern Python: Introduction 00:04:14
    2. Getting Set Up for the Course 00:05:26
  2. Lesson 1: Building Foundational Python Skills for Data Analytics
    1. Topics 00:00:31
    2. 1.1 Building Foundational Python Skills for Data Analytics, Part 1 00:32:28
    3. 1.2 Building Foundational Python Skills for Data Analytics, Part 2 00:12:51
  3. Lesson 2: Analyzing Data Using Simulations and Resampling
    1. Topics 00:00:57
    2. 2.1 Analyzing Data Using Simulations and Resampling, Part 1 00:27:24
    3. 2.2 Analyzing Data Using Simulations and Resampling, Part 2 00:29:46
  4. Lesson 3: Improving Reliability with MyPy and Type Hinting
    1. Topics 00:01:03
    2. 3.1 Improving Reliability with MyPy and Type Hinting, Part 1 00:19:49
    3. 3.2 Improving Reliability with MyPy and Type Hinting, Part 2 00:17:32
  5. Lesson 4: Implementing k-means Unsupervised Machine Learning
    1. Topics 00:00:36
    2. 4.1 Implementing k-means Unsupervised Machine Learning, Part 1 00:24:59
    3. 4.2 Implementing k-means Unsupervised Machine Learning, Part 2 00:25:41
  6. Lesson 5: Building Additional Skills for Data Analysis
    1. Topics 00:00:44
    2. 5.1 Building Additional Skills for Data Analysis, Part 1 00:10:56
    3. 5.2 Building Additional Skills for Data Analysis, Part 2 00:16:50
  7. Lesson 6: Applying Cluster Analysis to a Real Dataset
    1. Topics 00:00:40
    2. 6.1 Applying Cluster Analysis to a Real Dataset, Part 1 00:23:33
    3. 6.2 Applying Cluster Analysis to a Real Dataset, Part 2 00:21:44
  8. Lesson 7: Gearing-up for a Publisher/Subscriber Application
    1. Topics 00:01:00
    2. 7.1 Gearing-up for a Publisher/Subscriber Application, Part 1 00:17:11
    3. 7.2 Gearing-up for a Publisher/Subscriber Application, Part 2 00:15:57
  9. Lesson 8: Implementing a Publisher/Subscriber Application
    1. Topics 00:01:01
    2. 8.1 Implementing a Publisher/Subscriber Application, Part 1 00:24:29
    3. 8.2 Implementing a Publisher/Subscriber Application, Part 2 00:24:08
  10. Lesson 9: Using Bottle to Build REST APIs and Web Applications
    1. Topics 00:00:46
    2. 9.1 Using Bottle to Build REST APIs and Web Applications, Part 1 00:23:07
    3. 9.2 Using Bottle to Build REST APIs and Web Applications, Part 2 00:26:08
  11. Lesson 10: Building a Web Application for the PubSub Service
    1. Topics 00:00:28
    2. 10.1 Building a Web Application for the PubSub Service, Part 1 00:19:08
    3. 10.2 Building a Web Application for the PubSub Service, Part 2 00:15:45
  12. Lesson 11: Testing with PyTest, Itertools, Hypothesis, Pyflakes, MyPy and Data Validators
    1. Topics 00:01:41
    2. 11.1 Testing with PyTest, Itertools, Hypothesis, Pyflakes, MyPy and Data Validators, Part 1 00:26:10
    3. 11.2 Testing with PyTest, Itertools, Hypothesis, Pyflakes, MyPy and Data Validators, Part 2 00:27:06
  13. Summary
    1. Modern Python: Summary 00:01:24