Why data scientists should learn python?

Why Python?

Python is the programming language, known due to its simplicity and versatility. It is an ideal language for beginners since most of the keywords used are in English with very few exceptions.

When I first started programming, I began with C++ It was a nightmare to encounter a massive code snippet at first glance, only to print 'Hello World'. But python made it so simple that even a layman can interpret it easily.

After I started with Python, I know I will never turn my back on it. There are a number of reasons why you shouldn't switch back to any other programming language.
Here are 7 solid reasons why you should learn Python.

1. Readability and Simplicity

2. Vast Ecosystem of Data Science Libraries

3. General-purpose nature

4. Strong community and support

5. High demand in job market

6. Open source and free

7. No compilation

Readability and Simplicity

Python's syntax is clear and intuitive, making it easy to learn and write even for the newbies in tech. The best thing about it is that it doesn't include gibberish of random symbols but uses a very natural English language which our brain can easily understand. Since it's easy to learn, people don't spend much time on syntax but can quickly focus on more complicated concepts. Hence, Python helps you to code efficiently.

Here's a code snippet of displaying “Hello World” using C++.

#include <iostream>
using namespace std;

int main(){
cout<<"Hello World";
return 0;
}

The same task can be done in a single line using Python.

print("Hello World")

It is very obvious that Python shortened the same program. Another advantage is that it will not make your non-tech friend scared of the existence of code. Fewer lines of code makes you look more professional.

Vast Ecosystem of Data Science Libraries

Python has more than 137,000 libraries which are designed for scientific computing and other complex tasks. These include NumPy, SciPy, and Matplotlib. These libraries provide powerful tools for performing numerical computations and data visualization.

Python has a number of libraries that can be used for implementing machine learning algorithms. These include scikit-learn, TensorFlow, and Keras. These libraries provide tools for data preprocessing, model training, and model evaluation.

The following table shows the Python's specialized libraries for data science tasks:

General-Purpose Nature

Python is a general-purpose programming language. It is used for the development of complex scientific and numeric applications, making it the fastest-growing programming language. It is heavily used in academic and industrial circles you can use it to create websites, automation or scripting, software testing and, build machine learning tools.

Python plays a huge role in data science as it can also support very important tasks, such as data collection, analysis, modelling, and visualization, which are all key factors to work with in big data. Python is best used for automation. Automating tasks is extremely valuable in data science and will ultimately save you a lot of time, and provide valuable data.

We can use it in Web development with frameworks like Django and Flask. It even helps in building interactive dashboards and web applications. This versatility makes Python a valuable asset for individuals with diverse career aspirations.

Strong community and support

No one would be left with an unanswered query in Python, given the millions of resources available for help. As it's a widely used programming language, thus it's easy to find reputable resources and documentation.

Python has a large and active community of developers. This means that there are numerous online resources, forums, and tutorials available to help you learn and troubleshoot issues. The community support is particularly beneficial for data scientists who may encounter various challenges during their work.

You can find a huge community on Reddit, who are always available to answer your queries, or share their code and talk about their tips and tricks. Additionally, Discord servers also give a similar comfortable environment to learn Python freely.

These easy access to such a huge community is a game changer. It boosts confidence and trust me, I lurk most of the time when some senior developers are chatting, and it helps me learn lots of things.

High demand in job market

It's not news that Python has become a go-to language in the Data Science fields. You can't really go wrong if Python is your first programming language. You can even switch languages once you have the hold, onto the fundamentals, but who even wants to leave such an easy and multi-function programming language.

Someone called Python; the golden passport, and I couldn't agree more. It really is one. It provides a wide range of fields which opens doors across various industries. Whether it's Healthcare, Finance, Retail, or Scientific Research, Python will always be there for you. For instance, you can build complex trading models or analyze market trends with its help.

As we are data enthusiasts, so let's add some insights too. Studies predict a 26% growth in Python developer jobs by 2020, outpacing the average job growth rate. Data scientists with strong Python skills command some of the highest salaries in the tech industry, averaging over $120,000 in US alone.

Open source and free

Python also provides open-source code and allows anyone to use it, contribute improvements in it, and collaborate. One can also share their expertise and do some flex in front of their fellows. It brings up a vibrant community of developers who constantly add new libraries, troubleshoot issues, and promptly answer your queries for free.

The 2022 Stack Overflow Developer Survey states that 67% of all developers worldwide use open-source tools and libraries in their work. See, this suggests there is a high reliance on free resources within the dev community.

Especially, it is beneficial for young developers to have free resources due to cost constraints, accessibility, and since COVID-19, the world shifted everything digitally and thus there is abundance of high-quality open-source tools and learning materials available.

If you ask me, I do not even use the resources even when there's 14-day trial or “get premium to remove ads” notifications, they bug me a lot! I prefer YouTube for everything. It's my favourite teacher (I just hope they don't make it a compulsion to buy it's paid version.

No compilation

Python works like a Lego. You snap the blocks together and see your creation gradually come to life. Exactly, that's how Python works as well—you see how data is functioning line by line, gradually transforming. No need to wait for lengthy compilations. You may get scared by looking at the following snippet, which contains multiple compilation errors.

It is really helpful for modifying the code efficiently. Want to change an algorithm? Just replace a few lines of code and you're done. This flexibility is what makes Python the king of programming languages for data science learners.

Alright, enough talk! Time to stop the training wheels and let's completely give yourself into Python. Remember, the only difference between a beginner and a master is a few lines of code and a lot of caffeinated nights.

Good luck on your journey with Python, shaping a better future byte by byte.

Share your insights and let me know if you liked it or if there's room for improvement. Feel free to drop your comments below.