One of the most popular programming languages, many people want to learn python. But where to start? In this guide, we explore everything you need to know to get started on your learning journey, including a step-by-step guide and study plan, as well as some of the most helpful resources to help you succeed.
What is Python?
Python is a highly interpreted programming language developed by Guido van Rossum was first released in 1991. It was developed with an emphasis on code readability, and its syntax allows programmers to express concepts in fewer lines than would be possible in languages like C++ or Java.
Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming. Simply put, this means that it is flexible and allows you to write code in a variety of ways, whether it is by giving the computer a to-do list (how-to), by creating digital models of things or concepts (object-oriented), or by treating your code like a math problem. (functional).
What makes Python so popular?
As of July 2023, Python remains the most popular programming language according to the TIOBE index. Over the years, Python has become one of the most popular programming languages due to its simplicity, versatility, and wide range of applications.
These reasons also mean that it is an extremely popular language for data science, as it allows data scientists to focus more on interpreting the data rather than the complexity of the language.
Let’s examine these factors in more detail.
Main features of Python
Let’s look at some of the features of Python that make it such a versatile and widely used programming language:
- Readability : Python is known for its clear and readable syntax, somewhat similar to English.
- Easy to learn : Python’s readability makes it relatively easy for beginners to learn the language and understand what the code does.
- Versatility : Python isn’t limited to one type of activity; It can be used in many fields. Whether you’re interested in web development, task automation, or data science, Python has the tools to help you get there.
- Extensive library support : It comes with an extensive standard library containing pre-written code for various tasks, saving you time and effort. Additionally, the vibrant Python community has developed thousands of third-party packages that further extend Python’s functionality.
- Platform independence : The great thing about the language is that you can write your code once and run it on any operating system. This feature makes Python a good choice when working on a computer with several operating systems.
- Interpreted language : Python is an interpreted language, which means that the code runs line by line. This can make debugging easier as you can test small snippets of code without having to compile the entire program.
- Free and open source : It is also an open source language, which means that its source code is freely available and can be distributed and modified. This has led to a large community of developers contributing to its development and creating a large ecosystem of Python libraries.
- Dynamic type : Python is dynamically typed, which means you don’t have to declare the data type of a variable when you create it. The Python interpreter infers the type, which makes the code more flexible and easier to modify.
Why is learning Python so useful?
Learning Python is useful for several reasons. Along with its massive popularity, Python has applications across many industries, from technology to finance to healthcare and more. Learning Python opens up many career opportunities and ensures better professional results. Here’s how:
Python has a variety of uses
We’ve already mentioned Python’s versatility, but let’s look at some examples in which you can use it. or:
- Data science: Python is commonly used in data analysis and visualization, with libraries such as Pandas, NumPy and Matplotlib being especially useful.
- Web Development: Frameworks such as Django and Flask are used for back-end web development.
- Software development: You can use Python in software development for scripting, automation, and testing.
- Game development: You can even use it for game development using libraries like PyGame and tkinter.
- Machine learning and artificial intelligence: Libraries like TensorFlow, PyTorch, and Scikit-learn make Python a popular choice in this space. Learn how to learn AI in a separate guide.
Python skills are needed
With the rise of data science, machine learning and intelligence artificial, there is a great demand for Python knowledge. According to a 2022 GitHub report, Python usage grew 22.5% year-over-year, making it the third most used language on the platform.
Organizations across many industries are looking for professionals who can use Python to extract insights from data, build machine learning models, and automate tasks.
The learning Python can enhance their employability and openness to various career opportunities. A quick search for “Python” on Indeed’s job site reveals nearly 10,000 jobs in the US that require this skill.
How long does it take to learn Python?
Although Python is one of the easiest programming languages to learn, it still requires dedication and practice. The time it takes to learn Python can vary greatly, depending on your previous programming experience, the complexity of the concepts you want to understand, and the time you can devote to learning.
However, with a structured study schedule and consistent effort, you can often learn the ropes in weeks and become reasonably proficient in months.
Online resources can provide a solid foundation for your skills and can vary in size. For example, our Python Programming Skills course, which covers the skills needed to code proficiently, is approximately 24 hours long, while our Python Data Analyst Career is approximately 36 hours. Of course, becoming a true pythonist is a long-term process and much of your endeavor will require self-study and more structured methods. you need to learn Python over other languages:
How to Learn Python: 6 Steps to Success
Let’s see how you can learn Python. This step-by-step guide assumes you’re learning Python from scratch, which means you should start with the basics and work your way up.
1. Understand why you’re learning Python
First of all, it’s important to understand why you want to learn Python. It’s a generic language with all kinds of uses. Then, once you understand why you want to learn Python, you can develop a customized learning plan.
Whether you’re working on automating tasks, analyzing data or development. If you are interested in software, having a clear goal in mind will keep you motivated and focused on your learning journey. Here are some questions to ask yourself:
- What are my career goals? Are you looking for a career in data science, web development, software development, or any other field where Python is commonly used?
- What problems am I trying to solve? Do you want to automate tasks, analyze data, build a website or build a machine learning model? Python can be used for all these tasks and more.
- What interests me? Are you interested in working with data or building apps? Or are you fascinated by artificial intelligence? Your interests can guide your learning journey.
- What is my current level of knowledge? If you are a beginner, Python is a great native language due to its simplicity and readability. If you are an experienced programmer, you might be interested in Python because of its powerful libraries and frameworks.
The answers to these questions will determine how you structure your learning path, which is especially important for the next one. phase .
Python is one of the easiest programming languages to learn. What’s really nice is that you’re not locked into a domain when learning Python; Python is so versatile that it has applications in software development, data science, artificial intelligence, and virtually any programming-related role.
2. Getting started with Python basics
Understanding Python basics
Python values code readability and allows you to express concepts in fewer lines of code. You should start by understanding basic concepts such as variables, data types, and operators.
Our introductory Python course covers the fundamentals of Python for data analysis. so that you can familiarize yourself with these concepts.
Install Python and configure your environment
To start programming in Python, you need to install Python and set up your development environment. You can download Python from the official website, use Anaconda Python, or get started with DataCamp Workspace to use Python in your browser. Installing Python for configuration information.
Write Your First Python Program
First, write a simple Python program, such as a classic “Hello World!” scenario. This process helps you understand the syntax and structure of Python code. Our Python tutorial for beginners walks you through some of these fundamentals.
Python Data Structures
Python provides several data structures. integrates data sources such as lists, tuples, sets, and dictionaries. These data structures are used to store and manipulate data in programs. We have a Python Data Structures and Algorithms course that covers a wide range of these topics.
Python Control Flow
Control Flow control statements how how . Things like if statements, for loops, and while loops allow your program to make decisions and repeat actions. We have a tutorial on if, while, and for statements.
Functions in Python
Functions in Python are reusable blocks of code that perform a specific task. You can define your own functions and use Python’s built-in functions. We offer a Python function writing course that covers best practices for writing complex functions that can be maintained and reused.
3. Master advanced Python concepts
Once you’ve mastered the basics, you can move on to more advanced topics. Again, they are important in developing your understanding of Python and will help you deal with a variety of problems and situations you may encounter when using the programming language.
Error and Exception Handling
Python provides tools for handling errors and exceptions in your code. To write robust Python programs, it is important to understand how to use try/except blocks and how to throw exceptions. We have a special guide to exception and error handling in Python that can help you troubleshoot problems with your code.
Working with libraries in Python
Working with libraries in Python Python’s power comes from its vast ecosystem of libraries. Learn to import and use popular libraries such as NumPy for numerical calculations, Pandas for data manipulation and Matplotlib for data visualization. In a separate article, we cover key Python libraries for data science, which can provide more context for these tools.
Object-Oriented Programming in Python
Object-Oriented Programming in Python Python supports object-oriented programming (OOP), a paradigm that allows you to structure your code around objects and classes. Understanding OOP concepts such as classes, objects, inheritance, and polymorphism will help you write more organized and efficient code.
Learn more about object-oriented OOP programming in Python Check out Check out our online course on how to create classes and use techniques like inheritance and polymorphism to reuse and optimize your code.
4. Learning by doing
One of the most effective ways to learn Python is to use it actively. You want to minimize the time spent learning syntax and working on projects as quickly as possible. This learning-based approach involves applying concepts learned in college to real-life projects and exercises.
Fortunately, many DataCamp Learn features take advantage of this learning . hands-on approach, but there are other ways to practice your skills:
- Keep track of projects that interest you. Work on projects that interest you. It could be a simple script to automate a task, a data analysis project, or even a web application.
- Join webinars and coding sessions. You’ll find many DataCamp webinars and online events where you can schedule with the instructor. This method can be a great way to learn new concepts and see them applied in real time.
- Apply what you’ve learned to your ideas and projects. Try recreating existing projects or tools that you find useful. It can be a great learning experience as it forces you to understand how something works and how to implement it yourself.
5. Create a Portfolio
Embed them in a portfolio as you complete projects. This portfolio should reflect your skills and interests and be relevant to the job or industry that interests you. Try to make your projects original and show your problem solving skills.
We have a list of 60+ Python projects for all levels in a separate article , but here are some suggested design ideas for different levels:
- Beginner. Simple projects like a guessing game, a to-do list app, or basic data analysis using a dataset that interests you.
- Intermediate. More complex projects like a web scraper, a blog with Django, or a machine learning model with scikit-learn.
- Intermediate. Large-scale projects such as a complete web application, a complex data analysis project, or a deep learning model using TensorFlow or PyTorch.
We have a complete guide to building a great data science portfolio . , which covers a variety of different examples. And don’t forget; You can build your portfolio with DataCamp to showcase your expertise.
6. Keep challenging yourself
Never stop learning. Once you’ve mastered the basics, look into more challenging missions and projects. Specialize in areas relevant to your career goals or personal interests. Whether it’s data science, web development, or machine learning, there’s always more to learn in the world of Python. Remember that the Python learning journey is a marathon, not a sprint. Keep practicing, stay curious, and don’t be afraid to make mistakes.
An example Python learning plan
Then let’s create a plan potential learning curve that describes where to focus your time and effort if you’re new to Python. Remember that timings, areas and progress depend on a myriad of variables. Since we want this plan to be as convenient as possible, we recommend that you work on projects while on the go.
Months 1–3: Basic Python and Manipulating data
Master basic and advanced programming concepts. Start by completing foundational projects in your area of expertise. For example, if you’re interested in data science, you might start by analyzing a dataset with pandas and visualizing the data with Matplotlib.
- Python basics. Get started with the basics of Python. This includes understanding syntax, data types, control structures, functions, and more.
- Data manipulation. Learn how to manage and manipulate data using Python libraries like Pandas and NumPy. This is an essential skill for any Python-related job, especially in data science and machine learning.
4 to 6 months: Intermediate Python
Now that you have a solid foundation, you can start learning more advanced topics.
- Advanced Python: Once you’ve mastered the basics, move on to more advanced Python topics. This includes understanding object-oriented programming, error handling, and more complex data structures. Explore more advanced topics like decorators, context managers, metaclasses, and more.
- More specific topics: For example, if you are interested in machine learning, you can start machine learning fundamentals with the Python track. Keep working on the projects, but make them more complex. For example, you can build a machine learning model to predict house prices or rank images.
Month 7+: Advanced Python and Specialization
By this point, you should have a good understanding of Python and its applications in your area of interest. Now is the time to specialize.
- Specialization: Depending on your interests and career aspirations, you may specialize in one area. It could be data science, machine learning, web development, automation or any other field. For example, if you are interested in natural language processing, you may be familiar with libraries such as NLTK and SpaCy. Keep working on projects and discover new developments in your field.