How to upskill teams with Python training for all skill levels

Whether you’re cross-skilling employees in AI, reskilling them for new roles, or bringing new hires up to speed, Python is a powerful skill that can boost employee productivity and problem solving.

While you could just throw Python training courses at your people, a thoughtful approach to upskilling will empower them to learn and apply new skills faster.

In this article, we explain how to help your teams learn Python skills and recommend courses, hands-on labs, and learning paths to start building their skills today.

From data analysis, malware detection, and network automation to creating full-stack AI applications, Python is a popular, versatile language with wide applications. It’s also relatively easy to learn because it uses syntax similar to natural English language.

That being said, learning any new skill is still a challenge. If you truly want your team to learn Python for critical company initiatives, you need to give them proper support. Here’s how to get started.

Python is one of the most popular programming languages—but why do the people in your organization specifically need Python skills? 

You need to be able to answer this question to build an effective upskilling strategy, solid business case for training, and excitement among employees.

First, identify a goal. What will your teams be able to do once they’ve learned Python? This goal should map to larger organizational objectives, like enhancing the customer experience, enabling product innovation, or increasing revenue.

(Good news: If you’re reading this article, you probably already have a goal in mind.)

Next, consider who needs to learn Python to achieve that goal. Are you upskilling junior developers to fill critical skills gaps following an organizational restructure? Do your developers need more advanced Python skills to build AI agents? Do business professionals need Python skills to build data dashboards and visualizations? 

Understanding who needs what skills will allow you to develop relevant upskilling strategies for each team.

If you’re lucky, Python training may be a directive from your leaders or tech team. In that case, you don’t need to justify upskilling to executives.

But if that’s not the case, you need to convince learners and leaders that upskilling teams in Python will enhance productivity, make work easier, or lead to revenue growth down the line.

Start with the leaders. You need their buy-in to roll out your upskilling program. This is where your goal from step one comes in handy. You might start with something like this: 

For example, let’s say your organization wants to improve the customer experience by reducing downtime. By investing in upskilling for your software engineers, they’ll learn how to debug Python programs. This will empower them to find and fix issues faster, improving mean time to resolution in the process.

Cost is also a major factor for leaders. Strengthen your business case for Python upskilling by bringing in the numbers. Quantify the cost of doing nothing or compare the cost of hiring vs. upskilling. (Hiring costs $8,000+ more than upskilling for IT employees.)

Managers are the ones who actually control whether people have time to upskill. Gaining their support is crucial to employee engagement. In essence, you’ll want to show managers how learning directly impacts team goals and give them resources to coach their teams.

Not sure where to start? We covered the basics of manager enablement in another article.

Once you’ve got the go-ahead from leaders, it’s time to get learners on board. Instead of focusing on broad organizational goals and revenue, explain how upskilling will make their lives easier.

Take a tedious task and show them how they could use Python to automate it. For example, simple scripts could let them automate email reports, schedule tasks, and auto-reply to emails. Or they could build AI agents that draft follow-up emails for sales reps based on their notes.

Whatever it is, practical examples tailored to their role can show learners the real value of learning Python skills. And when everyone is working from the same baseline knowledge, it improves collaboration and communication between teams.

Any upskilling initiative should start with skill assessments. Assessments help you identify your team’s existing Python knowledge and conduct a skills gap analysis. When you understand the difference between their current skills and the skills they need, you can tailor learning to fill that gap.

You may be tempted to upskill everyone in your organization at once. We encourage you to start with a pilot or small team before expanding your efforts to the rest of your organization. This will prevent you from becoming overwhelmed. It also gives you time to gather feedback and improve your strategy as you go.

Explore Python Skill IQs to benchmark your team’s skills. (You will need to make a Pluralsight account to check these out, but there’s a free trial option.)

• Python Essentials

• Python: Cleaning Data

• Data Science with Python

• Python for Data Engineers

• Python for Cyber Defense

The most effective way to build your team's Python skills is to provide a range of learning materials tailored to their roles and skill levels. You’ll want to customize the journey for your organization’s unique needs, but these courses, paths, and labs can help you get started.

Explore all Pluralsight Python courses.

Foundational Python courses cover the basics. They’re ideal for beginners and people who may not need to be coding experts themselves but interact with Python programs or collaborate with tech teams.

• Python 3: The Big Picture: This course explains Python’s philosophy and use cases to help learners determine if it’s the right language for their organization or project.

• Python 3 Fundamentals: This course is truly Python for beginners. It covers the basics of programming in Python, including data types, conditionals, lists, and more.

• Python Data Essentials: Data Structures: Knowing how to structure data in Python can make it easier to solve complex problems. This course explains how to work with four built-in data structures: lists, tuples, sets, and dictionaries.

Python labs give learners hands-on experience with these concepts:

• Guided: Python Basics and Data Structures

• Guided: Building a Full-stack AI-powered Application with React and Python

Once someone has the basics down, intermediate Python courses take them one level deeper. Here, they learn to expand their knowledge and refine their existing skills.

• Python Data Essentials: Programming Fundamentals: Dive deeper into programming in Python.

• Python: Clean Code Practices: Knowing how to code in Python is different from knowing how to write good code. This course covers best practices for creating quality, maintainable Python projects.

• Classes and Object-oriented Programming in Python 3: In Python, everything is an object. This course explains object-oriented programming in Python to build enterprise level applications.

Help learners apply their skills with intermediate Python labs:

• Automate Tasks Using Python

• Guided: Python Development with LangChain

Advanced Python courses go even deeper into Python functionality and give learners more opportunities to apply their skills. 

• Functional Programming in Python 3: This course teaches learners to develop and support functional programming in Python in business settings.

• Python 3 Decorators: Decorators add new functionality to an object by wrapping it in another function. This course explains how to make apps more flexible with this capability.

• Python 3 Performance: Python applications can run slow. Learn how to speed them up using threads, asyncio, and more.

Even experts can benefit from hands-on practice to build their skills. Check out these advanced Python labs:

• Benchmarking Functions with Python Decorators

• Write an Automated Script to Perform a Vulnerability Scan and Log the Results

• Data Show and Tell: Crafting the Ideal Basketball Team with Python

If you want to upskill someone from beginner to advanced Python skills, consider learning paths. 

• Python Essentials: This learning path gives software developers the skills they need to write clean, efficient, and scalable Python code. They’ll start with the foundations, then learn how to work with data, debug and test environments, and maintain code quality.

• Python for Data Analysis: This learning path helps data analysts use Python to clean data, perform exploratory data analysis, and generate insights.

• Python for IT Pros: IT professionals can use Python to automate routine tasks. This learning path shows them how.

• Python for Cyber Defense: This learning path teaches security analysts to conduct defensive cyber operations. 

• Python for Cyber Offense: From creating pen testing tools to exploring packet manipulation, learn how to develop exploits using Python capabilities.

• Building Deep Learning Solutions with PyTorch: PyTorch is an open-source machine learning and deep learning library for Python. This learning path explains how to apply and deploy it for common issues.

Want more in-depth skill building for your teams? Prepare your teams with instructor-led training.

Your work isn’t over when you hand Python courses to your team. Once they’ve completed a learning path, reassess their skills. See where they’ve improved and where they might need extra practice. Take this data to your leaders to show upskilling’s impact.

As you focus on upskilling teams, take time to celebrate learning achievements. Recognize when someone completes a Python course or solves a problem with their new skills. Learning is its own type of work, and it deserves acknowledgement (especially to build a lasting culture of learning).

Develop your team’s Python expertise with Pluralsight’s hands-on tech skill development.