In this article, we will explore the reasons why should you learn Python for your career in 2023, particularly if you are working in the field of data science and machine learning.
Python is not only a versatile programming language, but it also offers numerous advantages in various domains, such as web and desktop application development, data analysis, and more.
Despite some concerns about Python’s performance, major cloud platforms like AWS, Google Cloud, and Azure have started offering deployment techniques specifically tailored for Python applications.
Python’s versatility makes it an ideal language to learn. You can use Python in almost every field starting with software testing to game development. Let me explain you some special points which I belive the reason why you should learn Python in 2023.
Web Application Development
Python is widely used for creating web applications. Frameworks like Flask and Django provide powerful tools for developing robust web applications using Python.
Previously almost all web applications used to develop using either Java or .Net. But now things are changing. Almost all companies want to develop or migrate their web application using Python.
So if you learn Python specifically for web app development, you will get lots of job opportunities. Not only a job, if you have good understanding in frameworks like Django or Flask, there is a good potential in freelancing. This way also you can earn money after learning Python.
In this blog, I shared lots of web applications such as object detection web app, question-answering web app, and many more. Those can be good starting point if you want to learn Web app development using Python.
Desktop Application Development
Python can also be used for creating desktop applications. Libraries such as PyQt and Tkinter enable developers to build user-friendly desktop applications with Python.
If you are new to desktop application development, read this tutorial to learn how to make desktop application using Python. I also shared some desktop application projects, you can check those out.
- Build Digital & Analog Clock GUI with Python Tkinter
- How to make a Stopwatch GUI in Python Tkinter
- How to create a GUI calculator in Python using Tkinter
Frankly speaking, currently, desktop applications are not that much popular in IT industry. But still, you can learn this technology as in some cases, desktop applications are more useful than web applications.
Data Science and Machine Learning
When I started my career in Data Science field, at that time R language was popular. It was an easy-to-learn language and we could do almost anything with it.
But when deep learning and artificial intelligence gained popularity, this language started to lag behind in terms of development speed and execution advancements compared to other languages like Python.
Python is the language of choice in the field of data science and machine learning. Leading libraries like TensorFlow and PyTorch, developed by major companies like Facebook and Google, support advanced research and provide extensive functionalities for data analysis, machine learning, and deep learning.
In this website, I share tutorials about machine learning, deep learning, NLP, computer vision, etc. on a regular basis. If you are interested in these topics, follow and subscribe to this website.
Open-Source Libraries
Because of its simplicity, lots of programmers like Python language. Since lots of developers like this language, they make lots of libraries for their use and promote their work.
Whether it is connecting a database or visualizing any data, you can find all possible libraries for Python. This makes Python special among other languages.
Python’s core strength lies in its vast collection of open-source packages and libraries. These libraries cover a wide range of functionalities, allowing developers to accomplish complex tasks with fewer lines of code.
Database Integration
Python seamlessly integrates with databases such as SQL and MongoDB. Dedicated packages and libraries enable programmers to interact with databases efficiently, making it easier to query and manipulate data.
So if you want to use Python for the data engineering field, you can do it smoothly. This is the reason Python is the mostly used language in data engineering in 2023.
Data Analysis and Visualization
Python offers powerful libraries for exploratory data analysis, feature engineering, and statistical analysis. Libraries like NumPy, Pandas, and Matplotlib provide essential tools for data manipulation, visualization, and statistical modeling.
If you want to build an interactive dashboard using Python, you can do it using libraries like: Dash, Plotly, etc.
Python for End-to-End Projects
To clear your understanding of Python, it is crucial to understand how to develop end-to-end projects. Follow this roadmap to gain expertise:
- Learn basic data structures like lists, strings, tuples, and dictionaries.
- Familiarize yourself with logging and exception-handling techniques.
- Dive into object-oriented programming (OOP) concepts, including inheritance, encapsulation, class methods, and static methods.
- Master modular coding and project structuring using tools like VS Code or PyCharm.
- Gain proficiency in libraries such as NumPy, Pandas, and Matplotlib to perform data analysis and visualization.
- Explore machine learning algorithms and implement them in real-world problem-solving.
- Follow the data science project lifecycle, including data ingestion, data transformation (feature engineering, exploratory data analysis), model training and evaluation, and deployment.
- Stay updated with ML Ops tools like GitHub Actions, GitLab, MLflow, and DVC for seamless project deployment and management.
Python’s Growing Significance
Python’s significance continues to grow, particularly with the recent introduction of the OpenAI API. Knowledge of Python and experience in developing end-to-end projects enable seamless integration with the OpenAI API, even without extensive machine learning background.
However, it’s essential to note that comprehensive knowledge of machine learning concepts is crucial for excelling in data science interviews, as interviewers often test candidates on fundamental concepts.
Conclusion
Python is an indispensable programming language, especially in data science, AI applications, deep learning, and computer vision. Its versatility, extensive library ecosystem, and compatibility with various domains make it an ideal choice for developers.
By following the recommended roadmap and mastering Python’s capabilities, you can enhance your career prospects and tackle a wide range of projects.
Start learning Python today, whether as a hobby or for professional advancement. Remember, Python’s applications and opportunities are ever-expanding, so it’s a valuable skill to invest in.
I hope you understood why you should learn Python in 2023. This is it for this article. If you have any questions or suggestions regarding this article, please let me know in the comment section below.
Hi there, I’m Anindya Naskar, Data Science Engineer. I created this website to show you what I believe is the best possible way to get your start in the field of Data Science.