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Python Workshop

(PYTHON-WRK.AJ2) / ISBN : 978-1-64459-600-5
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About This Course

Skills You’ll Get

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1

Introduction 

  • About the Course
2

Vital Python – Math, Strings, Conditionals, and Loops

  • Introduction
  • Vital Python
  • Numbers: Operations, Types, and Variables
  • Python as a Calculator
  • Strings: Concatenation, Methods, and input()
  • String Interpolation
  • String Indexing and Slicing
  • Slicing
  • Booleans and Conditionals
  • Loops
  • Summary
3

Python Structures

  • Introduction
  • The Power of Lists
  • Matrix Operations
  • List Methods
  • Dictionary Keys and Values
  • Dictionary Methods
  • Tuples
  • A Survey of Sets
  • Choosing Types
  • Summary
4

Executing Python – Programs, Algorithms, and Functions

  • Introduction
  • Python Scripts and Modules
  • Python Algorithms
  • Basic Functions
  • Iterative Functions
  • Recursive Functions
  • Dynamic Programming
  • Helper Functions
  • Variable Scope
  • Lambda Functions
  • Summary
5

Extending Python, Files, Errors, and Graphs

  • Introduction
  • Reading Files
  • Writing Files
  • Preparing for Debugging (Defensive Code)
  • Plotting Techniques
  • The Don'ts of Plotting Graphs
  • Summary
6

Constructing Python – Classes and Methods

  • Introduction
  • Classes and Objects
  • Defining Classes
  • The __init__ method
  • Methods
  • Properties
  • Inheritance
  • Summary
7

The Standard Library

  • Introduction
  • The Importance of the Standard Library
  • Dates and Times
  • Interacting with the OS
  • Using the subprocess Module
  • Logging
  • Collections
  • Functools
  • Summary
8

Becoming Pythonic

  • Introduction
  • Using List Comprehensions
  • Set and Dictionary Comprehensions
  • Default Dictionary
  • Iterators
  • Itertools
  • Generators
  • Regular Expressions
  • Summary
9

Software Development

  • Introduction
  • Debugging
  • Automated Testing
  • Creating a PIP Package
  • Creating Documentation the Easy Way
  • Source Management
  • Summary
10

Practical Python – Advanced Topics

  • Introduction
  • Developing Collaboratively
  • Dependency Management
  • Deploying Code into Production
  • Multiprocessing
  • Parsing Command-Line Arguments in Scripts
  • Performance and Profiling
  • Profiling
  • Summary
11

Data Analytics with pandas and NumPy

  • Introduction
  • NumPy and Basic Stats
  • Matrices
  • The pandas Library
  • Data
  • Null Values
  • Visual Analysis
  • Summary
12

Machine Learning

  • Introduction
  • Introduction to Linear Regression
  • Cross-Validation
  • Regularization: Ridge and Lasso
  • K-Nearest Neighbors, Decision Trees, and Random Forests
  • Classification Models
  • Boosting Methods
  • Summary

1

Vital Python – Math, Strings, Conditionals, and Loops

  • Finding the LCM
  • Assigning Values to a Variable
  • Calculating the Pythagorean Distance between Three Points
  • Displaying Strings in Python
  • Using the input() Function
  • Using the if-else Syntax
  • Using the for Loop
2

Python Structures

  • Using a Nested List to Store Employee Data
  • Implementing Matrix Operations
  • Accessing an Item from a List
  • Adding Items to a List
  • Storing Company Employee Table Data Using a List and a Dictionary
  • Implementing Set Operations
3

Executing Python – Programs, Algorithms, and Functions

  • Writing and Executing a Script
  • Implementing Linear Search
  • Implementing Binary Search
  • Using Bubble Sort
  • Finding the Maximum Number Using Pseudocode
  • Checking Whether a Number is Prime
  • Finding the Factorial of a Number Using Recursion
4

Extending Python, Files, Errors, and Graphs

  • Reading a Text File
  • Generating a Density Plot
  • Creating a Pie Chart
  • Drawing a Scatter Plot to Study the Data
  • Visualizing the Titanic Dataset Using a Pie Chart and Bar Plot
5

Constructing Python – Classes and Methods

  • Creating a Class
  • Using the init Method
  • Implementing Inheritance
6

The Standard Library

  • Comparing datetime across Time Zones
  • Calculating the Time Delta between Two datetime Objects
7

Becoming Pythonic

  • Building a Scorecard Using Dictionary Comprehension and Multiple Lists
  • Implementing the __iter__() Method
  • Using Regular Expressions to Replace Text
  • Using Regular Expressions to Find Winning Customers
8

Software Development

  • Debugging a Sample Python Code for an Application
  • Checking Sample Code with Unit Testing
9

Practical Python – Advanced Topics

  • Using the Multiprocessing Package
  • Using the Argparse Library
10

Data Analytics with pandas and NumPy

  • Finding the Mean and Median from a Collection of Income Data
  • Using DataFrames to Manipulate Data
  • Reading and Viewing the Boston Housing Dataset
  • Performing Visual Data Analysis
11

Machine Learning

  • Using Machine Learning to Predict Customer Return Rate Accuracy
  • Using Linear Regression to Predict the Accuracy of the Median Values of a Dataset

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