The Python Workshop course focuses on building up your practical skills so that you can build up your machine learning skills as a data scientist, write scripts that help automate your life and save you time, or even create your own games and desktop applications. You will learn from real examples that lead to real results. It contains interactive lessons with knowledge checks and quizzes, videos covering detailed exercises, activities, and their guided solutions and hands-on labs to build and iterate on your code like a software developer.
Lessons 1: Preface
- About the Course
Lessons 2: Vital Python – Math, Strings, Conditionals and Loops
- 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
Lessons 3: Python Structures
- The Power of Lists
- Matrix Operations
- List Methods
- Dictionary Keys and Values
- Dictionary Methods
- Tuples
- A Survey of Sets
- Choosing Types
Lessons 4: Executing Python – Programs, Algorithms and Functions
- Python Scripts and Modules
- Python Algorithms
- Basic Functions
- Iterative Functions
- Recursive Functions
- Dynamic Programming
- Helper Functions
- Variable Scope
- Lambda Functions
Lessons 5: Extending Python, Files, Errors and Graphs
- Reading Files
- Writing Files
- Preparing for Debugging (Defensive Code)
- Plotting Techniques
- The Don'ts of Plotting Graphs
Lessons 6: Constructing Python – Classes and Methods
- Classes and Objects
- Defining Classes
- The __init__ method
- Methods
- Properties
- Inheritance
Lessons 7: The Standard Library
- The Importance of the Standard Library
- Dates and Times
- Interacting with the OS
- Using the subprocess Module
- Logging
- Collections
- Functools
Lessons 8: Becoming Pythonic
- Using List Comprehensions
- Set and Dictionary Comprehensions
- Default Dictionary
- Iterators
- Itertools
- Generators
- Regular Expressions
Lessons 9: Software Development
- Debugging
- Automated Testing
- Creating a PIP Package
- Creating Documentation the Easy Way
- Source Management
Lessons 10: Practical Python – Advanced Topics
- Developing Collaboratively
- Dependency Management
- Deploying Code into Production
- Multiprocessing
- Parsing Command-Line Arguments in Scripts
- Performance and Profiling
- Profiling
Lessons 11: Data Analytics with pandas and NumPy
- NumPy and Basic Stats
- Matrices
- The pandas Library
- Data
- Null Values
- Visual Analysis
Lessons 12: Machine Learning
- Introduction to Linear Regression
- Cross-Validation
- Regularisation: Ridge and Lasso
- K-Nearest Neighbours, Decision Trees and Random Forests
- Classification Models
- Boosting Methods
Hands-on LAB Activities
Vital Python – Math, Strings, Conditionals and Loops
- Assigning Values to a Variable
- Determining the Pythagorean Distance Between Three Points
- Displaying Strings
- Using the input() Function
- Using the if-else Syntax
- Finding the LCM (Least Common Multiple)
- Using the for Loop
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
Executing Python – Programs, Algorithms, and Functions
- Writing and Executing a Script
- Finding the Maximum Number Using Pseudocode
- Using Bubble Sort in Python
- Implementing Linear Search in Python
- Implementing Binary Search in Python
- Checking Whether a Number is Prime
- Finding the Factorial of a Number Using Recursion
Extending Python, Files, Errors and Graphs
- Reading a Text File Using Python
- Drawing a Scatter Plot to Study the Data
- Creating a Pie Chart
- Generating a Density Plot
- Visualising the Titanic Dataset Using a Pie Chart and Bar Plot
Constructing Python – Classes and Methods
- Creating a Class
- Using the init Method
- Implementing Inheritance
The Standard Library
- Comparing datetime across Time Zones
- Calculating the Time Delta between Two datetime Objects
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
Software Development
- Debugging Sample Python Code for an Application
- Checking Sample Code with Unit Testing
Practical Python – Advanced Topics
- Using the Multiprocessing Package
- Introducing argparse to Accept Input from the User
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
Machine Learning
- Using Linear Regression to Predict the Accuracy of the Median Values of a Dataset
- Using Machine Learning to Predict Customer Return Rate Accuracy
Exam FAQs
FAQ's are not Available for this course.Summary
Standard:
Python Workshop
Lessons:
12+ Lessons
Delivery Method:
Online
Language:
English