Introduction to Python: Learn How to Program Today with Python by Pearson
8h 48mBeginner2026-02-23
Authors
Pearson
Course details
What is this course about?
Business leaders are under pressure from their boards and competitors to innovate and boost outcomes using AI. But this can quickly lead to starting AI projects without clearly defined, measurable objectives or exit criteria. In this course, instructor Andreas Welsch outlines the risks of sunk costs and missed opportunities, with practical insights on overcoming complex business challenges related to AI adoption. Learn how to implement proven risk mitigation strategies for starting, measuring, and managing AI projects. Along the way, get tips and techniques to optimize resourcing for projects that are more likely to succeed.
This course includes AI-powered Role Play. Role Play allows you to practice what you’ve learned in interactive simulations of real-world conversations.
Objectives
What will I be able to do by the end of this course?
Recognize the importance of aligning AI projects with business goals to improve success rates.
Identify opportunities where AI can add measurable value to organizational goals.
Evaluate AI projects for strategic alignment and feasibility, ensuring investments are resource efficient.
Prioritize AI projects by establishing criteria based on potential business impact.
Implement a risk mitigation framework to monitor AI project progress, set KPIs, and ensure accountability for desired outcomes.
Audience
Who is this course for?
Senior leadership teams
Executives
Business decision-makers
AI strategy advisors
Business leaders are under pressure from their boards and competitors to innovate and boost outcomes using AI. But this can quickly lead to starting AI projects without clearly defined, measurable objectives or exit criteria. In this course, instructor Andreas Welsch outlines the risks of sunk costs and missed opportunities, with practical insights on overcoming complex business challenges related to AI adoption. Learn how to implement proven risk mitigation strategies for starting, measuring, and managing AI projects. Along the way, get tips and techniques to optimize resourcing for projects that are more likely to succeed.
This course includes AI-powered Role Play. Role Play allows you to practice what you’ve learned in interactive simulations of real-world conversations.
Objectives
What will I be able to do by the end of this course?
Recognize the importance of aligning AI projects with business goals to improve success rates.
Identify opportunities where AI can add measurable value to organizational goals.
Evaluate AI projects for strategic alignment and feasibility, ensuring investments are resource efficient.
Prioritize AI projects by establishing criteria based on potential business impact.
Implement a risk mitigation framework to monitor AI project progress, set KPIs, and ensure accountability for desired outcomes.
Audience
Who is this course for?
Senior leadership teams
Executives
Business decision-makers
AI strategy advisors
Concepts
Introduction
- Introduction to Python - Introduction
Introduction to Programming and Python
- Learning objectives
- Install Python
- Choose a code editor
- Run Python code
- Write an area calculator
- More context - Understand what programming is
Python and Programming Basics
- Learning objectives
- Learn about types
- Work with variables
- Debug errors
- Manipulate strings
- Write a Mad Libs program
- Use modules
- Write your own functions
- Write a timer game
- More context - Learn about Python
Control Flow with Conditionals
- Learning objectives
- More context - Useful skills for programmers
- Identify true and false statements
- Use conditional if statements
- Write a number-guessing game
Lists and Loops
- Learning objectives
- Use a while loop
- Improve the number-guessing game with while loops
- Create and manipulate lists
- Loop over sequences with for loops
- Write a quiz game
- More context - Refactor your code
Additional Language Topics
- Learning objectives
- More context - Discuss how to keep learning
- Look at more data structures
- Read files
- Write more complex functions
- Program with classes
- Import external packages
- Add type-hints
Introduction to Data Analysis in Python
- Learning objectives
- Look at the ecosystem
- Start a Jupyter notebook
- Import a dataset
- Clean some data
- Plot the data
- Find some statistics
Introduction to Web Development in Python
- Learning objectives
- Look at the anatomy of a web app
- Start a web app with Flask
- Create a static page
- Get content from the back-end
- Make the page dynamic
- Hook up a database
- Get more context - Databases and deployment
Summary
- Introduction to Python - Summary