English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 8h 23m | 4.47 GB
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.
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.
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
Table of Contents
01 Modern Python – Introduction
02 Getting Set Up for the Course
03 Topics
04 1.1 Building Foundational Python Skills for Data Analytics, Part 1
05 1.2 Building Foundational Python Skills for Data Analytics, Part 2
06 Topics
07 2.1 Analyzing Data Using Simulations and Resampling, Part 1
08 2.2 Analyzing Data Using Simulations and Resampling, Part 2
09 Topics
10 3.1 Improving Reliability with MyPy and Type Hinting, Part 1
11 3.2 Improving Reliability with MyPy and Type Hinting, Part 2
12 Topics
13 4.1 Implementing k-means Unsupervised Machine Learning, Part 1
14 4.2 Implementing k-means Unsupervised Machine Learning, Part 2
15 Topics
16 5.1 Building Additional Skills for Data Analysis, Part 1
17 5.2 Building Additional Skills for Data Analysis, Part 2
18 Topics
19 6.1 Applying Cluster Analysis to a Real Dataset, Part 1
20 6.2 Applying Cluster Analysis to a Real Dataset, Part 2
21 Topics
22 7.1 Gearing-up for a Publisher_Subscriber Application, Part 1
23 7.2 Gearing-up for a Publisher_Subscriber Application, Part 2
24 Topics
25 8.1 Implementing a Publisher_Subscriber Application, Part 1
26 8.2 Implementing a Publisher_Subscriber Application, Part 2
27 Topics
28 9.1 Using Bottle to Build REST APIs and Web Applications, Part 1
29 9.2 Using Bottle to Build REST APIs and Web Applications, Part 2
30 Topics
31 10.1 Building a Web Application for the PubSub Service, Part 1
32 10.2 Building a Web Application for the PubSub Service, Part 2
33 Topics
34 11.1 Testing with PyTest, Itertools, Hypothesis, Pyflakes, MyPy and Data Validators, Part 1
35 11.2 Testing with PyTest, Itertools, Hypothesis, Pyflakes, MyPy and Data Validators, Part 2
36 Modern Python – Summary
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