Why Python is for Data Science Know the 5 Reasons
Here in this blog, we will discuss the top five reasons Python is good for Data Science. You must learn Python as a programming language for breaking into a Data Scientist career path. If you still have doubts over the right programming language to help break into the Data Science domain, this article will help you clear your doubts.
Python is not only the best language for Data Science but is also good for Machine Learning. R was built for Data Science but is inclined more towards statistical analysis, whereas Python is a general-purpose language that finds Data Science as one of the applications. If you wish to break into the Data Science domain, check out the Data Scientist Course. Lately, there is a huge demand for the Python skillset.
This article is a summarized version of my research over many books, websites, blogs, and other resources which helped me summarize my insights into the top five reasons. Let’s understand what makes Python the best programming language for Data Science, Machine Learning for 2021, and beyond.
1. Python is easy and simple to use and learn
It is probably one of the biggest advantages of Python over other languages. Python was born out of the need to simplify complex syntaxes of many other leading programming languages. That’s the reason why Python has very high readability. Its syntaxes are simple and a lot similar to the English language that we speak.
This characteristic makes Python easier to learn and the learning curve flatter compared to other languages like R. Python is very straightforward and intuitive.
2. Python has efficient tools and libraries
Data Science deals with extracting insights from data sets that may contain data of all sorts. By all sorts, we mean structured, unstructured, data error, irregularities, etc. A Data Scientist’s major job is to extract insights by analyzing data. And they do this by using tools and packages that help them solve problems faster. The insights can be extracted by using the best libraries and packages that are built to help you get the job done.
The other best thing is that many of these packages are open-source. Some of the most popular libraries of Python are NumPy, SciPy, Pandas, Matplotlib, Seaborn, etc. You also do not need to worry about how these open source libraries work as long as you know how to clean your data, how to apply mathematical formulas and run statistical analysis.
In reality, you can work very easily with these libraries because it only takes a Google search to find out the right answers. Because after working closely with the Python script, you will start understanding and getting to know these libraries. Pandas is used for Data Cleansing, Matplotlib is used for generating visuals like graphs, charts, etc.
Other advanced libraries like TensorFlow, PyTorch help you in solving problems dealing with Machine Learning, Deep Learning, and Neural Networks. These libraries are continuously being updated by professionals by qualified professionals from around the world. Being a Data Science or Machine Learning professional, you just have to know how to work with these libraries to get your job done.
3. Pandas Library
Pandas library was mentioned in the 2nd point, but it plays such a crucial role for Python that it deserves a special mention. There are very few projects which don’t use Pandas. Using Pandas, you can clean and analyze data from datasets. This library contains many functions like import and export data, data manipulations, etc. Pandas is a must-have for every Data Science professional. Check out Python Tutorial to help you learn Data Science from scratch.
4. Jupyter Notebook
Jupyter Notebook is probably the best and easiest way to learn and get started in Python. Jupyter only uses a web browser. Jupyter was born from IPython, which in itself was a command-line terminal used in Python. As we know command-line terminals are difficult to work with, it’s far easier to work with a web interface. And thus, Jupyter was born.
Owing to its ease of use, it’s used by many online courses to easily make professionals understand Python concepts. No surprise that it has become one of the most popular tools for Data Scientists.
5. Community Support
Community Support is one of the biggest factors behind such a surge in the popularity of Python in the IT domain. There is a huge network of qualified professionals and Python experts who work continuously to enhance Python and its libraries. Most of the work is shared as Open-source work. Many top organizations around the world offer their expertise by enhancing Python libraries like Google for TensorFlow and Facebook for PyTorch, etc.
Conclusion:
Python is an indispensable part of Data Science. There may be many languages, but Python has surely left its mark. The above-mentioned reasons are what make Python a great asset to invest in if you aim for a career in Data Science.
Lucas Noah, with a Bachelor of Information Technology (BIT) degree, is a prolific writer known for his expertise in the tech world. Currently, he brings his wealth of knowledge to Creative Outrank LLC and Oceana Express LLC, enriching their websites with hi... Read more
