Hello guys, If you want to learn Data Science, Machine Learning, and Deep learning in 2024 and are curious about which programming language you should learn, you have come to the right place. In the past, I have shared the best data science courses and the best Python courses, and today, I will tell why learning Python is the best for Machine Learning. When it comes to learning Data Science and Machine learning, you have two main choices: Python or R, but you will find that most Data Scientists and Machine Learning specialists use Python. I was thinking about it for quite some time; why do Data scientists love Python so much? And what makes Python an absolute choice for Data Science and Machine learning exploration.
I set out to research this, read many articles and books and joined Data Science courses with Python and R to figure out myself and what found was nothing more than surprising. It was the simple reason that makes Python more than any mystery advantage over R or any other mainstream programming languages like Java, C++, Ruby, or JavaScript.
I set out to research this, read many articles and books and joined Data Science courses with Python and R to figure out myself and what found was nothing more than surprising. It was the simple reason that makes Python more than any mystery advantage over R or any other mainstream programming languages like Java, C++, Ruby, or JavaScript.
From beginners to experienced programmers, Python is loved for its simplicity and powerful set of libraries and tools, which makes working with data really easy.
For example, you can easily cleanse raw data acquired from a survey to create your Machine learning Model using the Pandas library. If you try to do the same thing in other programming languages like Java, you will have to write tons of code, and it's not as easy as it is in Python.
This simplicity, easier learning curve, powerful toolset, and the library will make Python the best programming language for Data Science and Machine learning in 2024. Now, let's take a look at these reasons in detail before you choose Python to start your Machine Learning and Data Science journey with Python in 2024.
Python is also very readable and easy to learn, which means a shallow entry barrier compared to other programming languages like R, Java, or C++, which requires a proper environment to be set up to do anything other than running a trivial HelloWorld program.
And, If you are already convinced that Python is the best programming language for Data Science and looking for an online course that teaches you Python from a Data Science point of view, then I highly recommend you to join Kirill Erenemko and SuperDataScience Team's Python A-Z: Python For Data Science With Real Exercises! Course on Udemy. This hands-on course is the best course to learn Python for Data Science.
Python helps Data Scientists here; it comes with so many open-source Python libraries that can do all these tasks. These are the libraries that are regularly get updated, and all you need to do is use them in your Python scripts.
You don't need to learn how NumPy works or how Pandas works; as long as you can get your Data clean, apply some mathematical formulas, and run some statistical equations, you are happy.
Isn't that a result-oriented person will like it? Well, I certainly do. All you need to learn is how to import a Python module, and you are done. If you are curious about which Python module to use for which job, then just Google it, and you will find your answers. You don't need to remember which Python libraries I should use.
In reality, after working with a few scripts, you will automatically get familiar with essential Python libraries for Data Scientists like NumPy, which stands for Numerical Python, Pandas, which is the most critical tool for Data cleanup and Analysis; and MatPlotLib for visualizing data, creating charts and generating insights.
You also have TensorFlow, Sci-kit, and PyTorch, which provide some Scientific and Machine learning capability and are continuously being enhanced and updated by talented people worldwide. For example, Facebook has recently added a lot of machine learning capabilities on PyTorch.
As a Data Scientist and Machine learning enthusiast, you don't need to worry about updating libraries, adding new functionalities, etc., as someone else is doing that job for you. You just need to use the library to do your job.
Since working on the command line is not easy for everyone, they created a powerful web interface to Python and named it Jupyter Notebook.
The Jupyter Notebook is an incredibly powerful tool for developing and presenting Data Science projects. IT allows you to integrate code and its output into a single document, combining Visualization, mathematical formulas, and explanations.
In fact, most of the online courses I have taken about Machine learning on Google Cloud on Coursera use Jupyter Notebook for a hands-on example. Because of its impressive capabilities, Jupyter Notebook is very popular among Data Scientists, and it's one of the must-have tools for them.
And if all these good things are not enough, you would be surprised to know that Jupyter Notebook can also handle R code, which means you can also collaborate with a fellow data scientist using the R programming language.
You also benefit from their work as most things are shared as open source.
Many big organizations like Google and Facebook have contributed to TensorFlow and PyTorch, some of the most popular Python libraries for Data Science and Machine Learning.
Pandas contain many functions for data import, export, indexing, and data manipulation. It also provides a handy data structure like DataFrames (a series of rows and columns) and Series (1-dimensional array)and efficient methods for handling them.
For example, you can use Pandas to reshape, merge, split, and aggregate data. In short, Pandas is an indispensable tool for Data Scientists along with the Jupyter Notebook. If you want to learn Pandas better, I also recommend checking out the Data Analysis with Python and Pandas course on Udemy.
Coming back to the topic, because of all these excellent tools, frameworks, libraries, and simplicity of the Python programming language, Data Scientists love Python and continue to love it.
In short, here are 5 main reasons why Python is the most popular and best programming language for Data Science and Machine Learning
1. Python is Simple and Intuitive.
2. Jupyter Notebook allows Data scientists to collaborate and combine cod and output.
3. Python packages and libraries like NumPy and Pandas help with data cleanup and Analysis.
4. Community support
5. Pandas
If you still have doubts, here is a chart from IBM's survey about the most popular programming language for Machine learning in the last couple of years. It's a bit old, but it shows a clear trend that Python is way ahead of mainstream programming languages like Java, C++, JavaScript when it comes to Data Science and Machine learning.
That's why Python is the most popular programming language for Data Science and Machine learning. I am also from the same camp. I did try R but not more than a couple of days. Why? Because I wanted to spend my time on something that I can use in places other than Data Science, and on that parameter, Python is well ahead of R.
If you also think that Python is the best Programming language for Data Science, here are some courses you can take to learn Python from the Data Scientist's point of view.
Further Learning
The Complete Python Masterclass
Complete Python Bootcamp: Go from zero to hero in Python
Python – Beyond the Basics
Data Analysis with Python and Pandas
Other Articles Programmers and Data scientists may like
Thanks for reading this article so far. If you have any other reasons why Python is so popular among Data Scientists and Why Python is the best programming language for Data Science and Machine Learning, then please chip in and share it with us.
P. S. - If you don't know Python but want to learn Python now then I also suggest you check The Python Mega Course: Build 10 Real World Applications course to learn Python in-depth. It's a great hands-on course to further boost your training on Machine learning and Artificial Intelligence. It's one of the must-have tools in your arsenal.
5 Reasons to Learn Python for Data Science & Machine Learning in 2024
Anyway, here are the top 5 reasons why Python is so popular among Data Scientists and Machine Learning enthusiasts and why you should learn Python if you want to become a Data Scientist.1. The simplicity of Python itself
One of the main advantages of Python is that it's intuitive and straightforward, which makes it likable for anyone who wants to get a result rather than lost in code.Python is also very readable and easy to learn, which means a shallow entry barrier compared to other programming languages like R, Java, or C++, which requires a proper environment to be set up to do anything other than running a trivial HelloWorld program.
And, If you are already convinced that Python is the best programming language for Data Science and looking for an online course that teaches you Python from a Data Science point of view, then I highly recommend you to join Kirill Erenemko and SuperDataScience Team's Python A-Z: Python For Data Science With Real Exercises! Course on Udemy. This hands-on course is the best course to learn Python for Data Science.
2. Tools and Libraries
One of the leading jobs of Data scientists is to analyze the Data, and in the real world, Data comes in all shapes. They are often raw and not suitable for running any kind of analytics; hence, data wrangling is applied. It's a process to clean and transform the data so that you can analyze and model it to create insights.Python helps Data Scientists here; it comes with so many open-source Python libraries that can do all these tasks. These are the libraries that are regularly get updated, and all you need to do is use them in your Python scripts.
You don't need to learn how NumPy works or how Pandas works; as long as you can get your Data clean, apply some mathematical formulas, and run some statistical equations, you are happy.
Isn't that a result-oriented person will like it? Well, I certainly do. All you need to learn is how to import a Python module, and you are done. If you are curious about which Python module to use for which job, then just Google it, and you will find your answers. You don't need to remember which Python libraries I should use.
In reality, after working with a few scripts, you will automatically get familiar with essential Python libraries for Data Scientists like NumPy, which stands for Numerical Python, Pandas, which is the most critical tool for Data cleanup and Analysis; and MatPlotLib for visualizing data, creating charts and generating insights.
You also have TensorFlow, Sci-kit, and PyTorch, which provide some Scientific and Machine learning capability and are continuously being enhanced and updated by talented people worldwide. For example, Facebook has recently added a lot of machine learning capabilities on PyTorch.
As a Data Scientist and Machine learning enthusiast, you don't need to worry about updating libraries, adding new functionalities, etc., as someone else is doing that job for you. You just need to use the library to do your job.
3. Jupyter Notebook
Another reason why Data scientists love Python is Jupyter Notebook, which allows you to code and collaborate with other Data Scientists using a web browser. Jupyter Notebook was born from IPython, an interactive command-line terminal for Python.Since working on the command line is not easy for everyone, they created a powerful web interface to Python and named it Jupyter Notebook.
The Jupyter Notebook is an incredibly powerful tool for developing and presenting Data Science projects. IT allows you to integrate code and its output into a single document, combining Visualization, mathematical formulas, and explanations.
In fact, most of the online courses I have taken about Machine learning on Google Cloud on Coursera use Jupyter Notebook for a hands-on example. Because of its impressive capabilities, Jupyter Notebook is very popular among Data Scientists, and it's one of the must-have tools for them.
And if all these good things are not enough, you would be surprised to know that Jupyter Notebook can also handle R code, which means you can also collaborate with a fellow data scientist using the R programming language.
By the way, If you are planning to join multiple Coursera courses or specializations, then consider taking a Coursera Plus subscription which provides you unlimited access to their most popular courses, specialization, professional certificate, and guided projects. It costs around $399/year, but it's completely worth your money as you get unlimited certificates.
4. Community Support
Another reason which I found behind the popularity of Python among people learning Data Science in the community. Since Python has an active community, and many people are doing Data Science using Python, you already have an active community to call upon when you get stuck.You also benefit from their work as most things are shared as open source.
Many big organizations like Google and Facebook have contributed to TensorFlow and PyTorch, some of the most popular Python libraries for Data Science and Machine Learning.
5. Pandas
This is an extension of the second point, but Pandas is such an essential tool for Data Scientists that It warrants a special mention. Most of the Data Science project I have worked on starts with Pandas and finishes with it. It not only allows you to clean and massage your Data but also to analyze the data. You can load data from various data sources like CSV files, Excel, Databases, and many other sources.Pandas contain many functions for data import, export, indexing, and data manipulation. It also provides a handy data structure like DataFrames (a series of rows and columns) and Series (1-dimensional array)and efficient methods for handling them.
For example, you can use Pandas to reshape, merge, split, and aggregate data. In short, Pandas is an indispensable tool for Data Scientists along with the Jupyter Notebook. If you want to learn Pandas better, I also recommend checking out the Data Analysis with Python and Pandas course on Udemy.
Coming back to the topic, because of all these excellent tools, frameworks, libraries, and simplicity of the Python programming language, Data Scientists love Python and continue to love it.
In short, here are 5 main reasons why Python is the most popular and best programming language for Data Science and Machine Learning
1. Python is Simple and Intuitive.
2. Jupyter Notebook allows Data scientists to collaborate and combine cod and output.
3. Python packages and libraries like NumPy and Pandas help with data cleanup and Analysis.
4. Community support
5. Pandas
If you still have doubts, here is a chart from IBM's survey about the most popular programming language for Machine learning in the last couple of years. It's a bit old, but it shows a clear trend that Python is way ahead of mainstream programming languages like Java, C++, JavaScript when it comes to Data Science and Machine learning.
That's why Python is the most popular programming language for Data Science and Machine learning. I am also from the same camp. I did try R but not more than a couple of days. Why? Because I wanted to spend my time on something that I can use in places other than Data Science, and on that parameter, Python is well ahead of R.
If you also think that Python is the best Programming language for Data Science, here are some courses you can take to learn Python from the Data Scientist's point of view.
Further Learning
The Complete Python Masterclass
Complete Python Bootcamp: Go from zero to hero in Python
Python – Beyond the Basics
Data Analysis with Python and Pandas
Other Articles Programmers and Data scientists may like
- 10 Courses to Learn Data Science for Beginners
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Thanks for reading this article so far. If you have any other reasons why Python is so popular among Data Scientists and Why Python is the best programming language for Data Science and Machine Learning, then please chip in and share it with us.
P. S. - If you don't know Python but want to learn Python now then I also suggest you check The Python Mega Course: Build 10 Real World Applications course to learn Python in-depth. It's a great hands-on course to further boost your training on Machine learning and Artificial Intelligence. It's one of the must-have tools in your arsenal.
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