Introduction:
Hello, coding enthusiasts!
Do you find it difficult to know where to start while learning to code? Having trouble deciding which programming language to use or where to start writing code? It’s not just you! I’ve experienced same difficulties as a fellow student as well. Feeling disoriented at first is quite normal, but the important thing is to take responsibility of your own learning. In this blog, I’ll walk you through the steps to get you started on your coding journey.
When we’re trying to find out where to begin, we all feel confused and maybe even frustrated. When you’re unsure of where to start, it’s simple to feel overwhelmed and think about giving up.
However, keep in mind that your teachers and institutions cannot guide you through this process; they can only point you in the right direction. Finally, it is your job to take the initial step and move forward confidently. Don’t blame your teachers or the organization where you study; it’s all part of the learning process, and you have power now!
Choosing a Programming Language:
A lot of beginners find it difficult to select the appropriate programming language. This resonates with me! I was similarly unsure about where to begin as a student of bioinformatics. I studied Python, HTML, CSS, JavaScript, and C++ during my bachelor’s degree. But the information didn’t truly stick by the conclusion of each semester. I chose to concentrate on only one language, after realizing that juggling many languages wasn’t helping.
Your area of interest determines the programming language you should use. For instance, languages like Python, R, SQL, Perl, and Ruby are frequently used in bioinformatics. I found that Python is the most approachable for beginners. Here’s why:
- Easy to learn and beginner-friendly.
Python is known for its simple and clear syntax. Unlike other programming languages that require complicated frameworks, Python allows newcomers to master programming concepts rather than syntax.
- Versatility: Employed in a Variety of Fields
Python’s broad range of applications is one of the main reasons I chose it. Python can be helpful in any career path you choose.
This implies that if I want to investigate other tech fields, I don’t have to change to a different language. Python is enough for me.
- Large community and resources.
Python is one of the most popular programming languages, thus there is a large global community willing to help. Whether you’re struggling with a problem or seeking for tutorials, there are thousands of free resources, courses, and forums available.
If I have any doubts, I can immediately Google it or check sites like:
You may also check them out, whenever feel stuck!
- Rich libraries and frameworks.
Python offers a large number of libraries that make development easier. For example:
Data science tools include Pandas, NumPy, and SciPy.
TensorFlow, PyTorch, and Scikit-learn are tools for AI and machine learning.
For web development, Flask, Django are used usually.
Since I work in bioinformatics, I frequently analyze biological data using Biopython and Pandas.
I picked Python not just because it’s easy but because it provides endless possibilities. Whether you want to work in AI, web development, automation, or bioinformatics, Python has you covered.
Don’t worry if you’re not from the same field! You can always utilize tools like ChatGPT to find out which programming languages are often used in your industry and what you should work on next.
Establishing Your Coding Environment
Now that you’ve decided on a programming language, it’s time to set up your development environment.
The coding environment is the platform on which you will develop and execute your code. As a novice, you can opt to work online or offline, depending on your preferences.
Choosing online platforms
For people who are new to coding, using online platforms is a great way to get started without the hassle of installation. Here’s some suggestions:
A. Google Colab (recommended for Python, excellent for beginners)
Google Colab is a free, browser-based program that allows you to execute Python in the cloud.
How to utilize it:
1. Go to [Google Colab] (https://colab.research.google.com).
2. Choose “New Notebook.”
B. Jupyter Notebook (For Python, Excellent for Learning)
Jupyter Notebook is a dynamic scripting platform widely used in data research and visualization. It is ideal for creating and running Python programs in smaller chunks (cells).
Access it online via Google Colab or Kaggle Notebooks. Alternatively, you can install it on your PC via Anaconda (as described below).
C. Replit (To Code in Multiple Languages in the Browser)
Replit is a web-based programming environment that supports Python, JavaScript, and other languages.
Reasons to use it:
– No installation required.
– It supports cooperation with others, making it ideal for collaborative work.
To use it:
– Go to Replit.
– Create a new Python project.
Install Python on Your Computer
If you want to code offline, you must first install Python on your device, followed by an Integrated Development Environment (IDE). Here’s how you set it up:
A. Download and install Python.
1. Go to Python.org.
2. Download the newest Python version for your operating system (Windows, Mac, or Linux).
3. During the installation, remember to check the box labeled “Add Python to PATH.”
B. Select an Integrated Development Environment (IDE).
An integrated development environment (IDE) is a software tool that simplifies coding. Here are the greatest selections:
1. Visual Studio Code (Ideal for Beginners and General Use)
– Lightweight and fast.
– Supports a variety of languages and provides significant customization possibilities.
– Download from code.visualstudio.com.
– Ensure you install the Python extension from the Extension section in Visual Studio.
2. PyCharm (best for advanced Python projects)
It provides intelligent code suggestions and debugging features.
– Get the free Community Edition from JetBrains.
– Ideal for Python-experienced users looking to take on more complex projects.
3. Anaconda (Best for Data Science and Bioinformatics):
Includes Python, Jupyter Notebook, and data science libraries like NumPy and Pandas.
– Download from anaconda.com.
– After installation, you can launch Jupyter Notebook directly and begin coding.
Conclusion:
Now that your setup is complete, you’re ready to start coding! Whether you’re using an online service like Google Colab or working offline with an IDE, persistence is essential. Continue to practice, take on new difficulties, and, most importantly, enjoy the process!
Question for You:
Did you find this guide helpful? Share it with your friends that are beginning their coding path!
