How to Learn Python

Python is one of the most in-demand programming languages among professionals today, with GitHub data ranking it in the top five alongside JavaScript and TypeScript.

With this, the Bureau of Labor Statistics (the BLS) predicts careers in related fields like Software Engineering will grow considerably faster than the average at 17% over the next decade. The growth is expected to produce 140,100 new job openings each year and highlights a growing need for Python-trained workers. 

If you’ve ever wondered how to learn coding or the best place to get started, Python is an ideal choice thanks to its straightforward syntax. The skills gained from learning this language are applicable to numerous industries, including finance and data science, and are incredibly valuable to those looking for new career opportunities.

How to Learn Python in 5 Simple Steps

1. Determine Your Goals for Learning Python

The first step within any learning journey is taking time to reflect on your goals and what your motivation is for seeking new skills. Do you want to make a career change to a new industry, or could you achieve more in your current role with stronger programming skills? No matter what drives you to learn Python, staying focused and developing a plan can help you stay on track while gaining valuable capabilities.

Taking the time to determine your goals for learning Python can also help you find what niche area you hope to land a job. There are numerous roles ideal for individuals with programming skills, though each has a different focus and responsibilities. Below is a snapshot of just a few of the roles those with Python training can consider.

• Data scientist: This role involves interpreting the results of data that they’ve processed and analyzed to create actionable plans. 
• Data analyst: These professionals work to collect and analyze data that is then transformed into insights for decision-making. 
• Full-stack developer: In this all-encompassing position, full-stack developers work with both the front and back-end of a website. 
• Software engineer: Another comprehensive role is that of a software engineer, who creates software for computers using programming languages, math, and computer science theory.
• Machine learning engineer: A role that focuses on utilizing artificial intelligence, algorithms, and data to automate tasks and improve efficiency.

Pro Tip

Ask yourself self-reflection questions before starting to learn Python, and along the way, to help you determine which careers meet your personal and professional goals. 

• What are my long-term career goals?
• How can I use Python skills in my current position?
• Do I have any existing skills that complement Python?
• How do I handle challenges and quick turn-around times?
• Does my work environment affect my productivity?

2. Master the Fundamentals of Python Programming

Once you’ve decided Python training is right for you, it’s time to dive in and learn the basics. It’s crucial to spend time on fundamental topics so you can build a strong knowledge base and be adequately prepared to tackle more complex processes. Here’s a quick glimpse into some of the topics you should focus on when first learning Python 

• Basic syntax: Syntax lays the groundwork for writing clean code and is one of the first steps in beginners' learning the language.
• Data types: The most common data types Python beginners should focus on include integers, floats, strings, and booleans, which are classifications for data items.
• Functions and methods: Understanding functions and methods helps users break down complex problems and make code easier to maintain.
• Object-oriented programming: A fundamental concept in which one can organize code by creating "objects" that have both data (attributes) and actions (methods).

Pro Tip

Utilize free resources like Python's official documentation and online seminars to help reinforce new concepts you’re learning and make sure you truly understand the basics before moving on to more advanced work.

3. Consider Taking a Training Course

Learning the basics on your own is plausible and an excellent way to become familiar with processes, but the benefits of attending a training course are hard to overlook. In fact, a report presented in the Proceedings of the National Academy of Sciences found that STEM students who participated in active learning had better retention of material than those who pursued other strategies. Interestingly, those who pursued more passive methods felt they were learning but, ultimately, showed a lower understanding than those associated with active learning.

So, what is this to say about learning Python independently versus in a structured program? It suggests that for some, engaging in active learning through hands-on projects and guided instruction can improve your grasp of the materials. There are several educational paths you can take when learning Python, though each will have its own set of benefits and drawbacks that are unique to each student.

Traditional degrees in an area like computer science or software engineering provide a well-rounded and thorough learning experience. Learning from a university, both public and private, comes with a hefty price tag and the need to dedicate years of time to mastering the full scope of this subject. 

Bootcamps and certificate courses are another way to learn Python, often with shorter timelines and lower tuition costs. These types of programs focus on teaching a specific topic without having to include general education courses in areas students may not need to know for their profession. It’s also common for individuals to pursue these programs as a means of continuing education or to refresh their skills.

Don’t forget to check out the Fortune list of best Python courses for beginners to learn more about the top programs.

Pro Tip

Try to prioritize courses that include benefits like mentoring and hands-on projects. This will help you create a portfolio of content to use during your job search and show potential employers the versatility of your Python skills.

4. Start with Small Projects and Continue Practicing

Once you’ve got a grasp on the basics, it’s time to put your new skills to the test by practicing and working on projects. Start with simple scripts that automate a small task, like renaming files or scraping data from a website, and be sure you are comfortable with all of the processes before building on this for more advanced challenges. A gradual approach to learning something complex like a programming language helps solidify the fundamentals and build your confidence in working with Python.

Even if you feel comfortable with most processes, practice is proven to be key for knowledge acquisition. The American Psychological Association reports that practice is crucial for retaining information and student automaticity. Repetition of tasks is simply not enough to boost performance, as one must be actively engaged to take advantage of the benefits of deliberate practice.

Pro Tip

Don’t forget about a project once you’ve completed it! Set aside time on a weekly or monthly basis to revisit your work. This is a great way to improve on any ideas and maintain code without the constraints of a professional deadline.

5. Build a Portfolio of Projects

A professional portfolio is a chance to take all of the impressive qualifications from your resume and bring them to life for potential employers. Many hiring managers focus on tech skills over specifics when it comes to training, but that’s not to say formal education lacks value. Instead, it’s a way for them to gain a visual understanding of how you’re able to apply your skills in a real-world context. 

A well-rounded Python portfolio should demonstrate your hard skills as well as soft skills like problem-solving abilities and adaptability in challenging scenarios. When you consider this, along with the data from an Adobe study showing over 59% of hiring managers prefer visual application materials like portfolios over text resumes, it’s clear these materials are paramount for any programming bprofessional-looking to pivot their career. 

Here’s some simple tips you can apply to your portfolio to help it stand out from other applicants:

• Showcase versatility: It’s best to include a selection of content that highlights your ability to work across fields. Adaptability and versatility are important characteristics in coding roles, so be sure to focus on your range of skills.
• Confirm relevance: Take the time to look at each project included and verify they align with the role or company you’re sending it to. 
• Keep it concise: An Adobe survey determined that 80% of hiring managers look at portfolio and resume content for less than three minutes per applicant. Use only the highest-quality and polished projects to show your abilities.
• Don't lose sight of objectives: Each entry in your portfolio should clearly identify the project and how your contributions addressed the challenges. 

Pro Tip

Be willing to reconfigure your portfolio based on the industry and the job you’re applying for. Choose your best work and tailor entries to highlight the skills most relevant to each role. 

Still Unsure If Learning Python is Right for You?

It’s not uncommon to feel hesitant when committing to learn something new, but you should remember change cannot happen if one remains stagnant in thoughts and actions. You may not know if a programming career suits your interests right away or feel limited working in certain field areas. 

There are virtually no limitations when it comes to how you can apply Python knowledge, meaning you’re never trapped in one specific role. Many use their skills to branch out into fields like full-stack development, software engineering, data analysis, machine learning, and countless other areas that provide something a bit different from what’s found in a traditional programming role.

For example, Python-trained professionals may enjoy the broad scope of tasks found in software engineering jobs. These roles usually involve overseeing and maintaining an entire system rather than just focusing on Python and coding practices.

There are also more specialized roles, like in full-stack or back-end development, both known for their lucrative salaries and faster-than-average growth rate that’s anticipated over the next decade. Embracing training in any programming language paves the way for aspiring tech professionals to examine what career sparks their interests, but the only way to do this is to take the leap and get started with learning Python as a foundation for professional growth.

Frequently Asked Questions

How Long Does It Take to Learn Python?

The time it takes to learn a programming language like Python will vary based on your learning style, the time you have to dedicate to training, and if you have any prior experience. Generally, Python bootcamps last anywhere from a few weeks to a few months and teach the fundamental skills needed to start a career. However, industry professionals must be willing to commit to continued learning. The tech industry and language itself are constantly evolving and require programmers to stay up-to-date with the latest processes and methodologies.

What Should I Expect During Python Classes?

Every course will have a different curriculum and outcomes, but overall, studying this field provides a mix of hands-on projects with lessons designed to teach the fundamentals of working with Python. The content covered should be the same between in-person and online classes, though methods for collaboration may vary depending on the learning modality. Some of the most important topics students will cover include fundamental concepts, as well as subjects in overlapping areas like machine learning, data science, data analysis, and web development. 

Can I Learn Python on My Own?

Yes, you can learn Python on your own, but this doesn’t mean the process will come without challenges. Following the steps in this guide, excluding professional training and opting for self-study instead, is a viable way to learn the basics of Python. Still, the volume of intermediate and advanced concepts may be difficult to grasp without professional guidance.