How To Learn Python For Data Scientists
Python has been growing as a popular choice among Data Scientists. The language itself has merits that make it a great choice for all kinds of programming as well as analytic applications. As far as Data science goes, Python shines in that arena as well.
Python in Data Sciences - A Wide Array Of Application
Python in itself is a very flexible and user-friendly language. It can be adapted for any function and is also a glue language, which means lines of codes written in other languages can directly be added into the Python code. There are certain traits of Python that can be exploited in the Data Science field, which is the reason behind it being such a necessity for every data scientist.
For starters, Python is much simpler than conventional languages which makes it easy to study, write as well as troubleshooting. Coding and compiling in Python is an easy process, and the simplicity makes it easier for programmers to find and fix errors. This has helped its growth and now enjoys support from a large community of Data Scientists, which makes working with it easier.
Python also has a large number of libraries for Data Science applications, making it easier to produce results with it. These libraries are bundles of pre-existing functions that can be directly imported into your project to save time. Python has an ever-evolving collection of packages makes work less tedious and more productive. This is what makes the language a blessing.
Learning Python: For Data Scientists
Python is a flexible language with infinite capabilities, which makes it almost impossible to learn fully. However, this also means that as a Data Scientist, there are parts of Python that you need not concern yourself with, and you can selectively learn the part of the language which suits your work and the current project. Learning Python as a whole is a good thing, but to save time, its easier to start with the general concepts and learn as you go.
Start with the Basics - An Absolute Must
First things first; Learning to code is the basic part of learning any language. And coding in Python is quite easy due to how simplistic the language is. The short and crisp syntax makes it looks less of a test of typing and is quite refreshing as compared to other languages. You can start with basic commands and functions, move on to fundamental concepts like loops and reach reasonably high levels quite fast. Some applications and websites teach Python to users by having them practice python exercises of increasing complexity, helping them learn as they go.
Mini Python Projects For The Much Needed Experience
As it is with everything else, Practice makes you perfect. For Python, reading the code and learning the commands aren’t the key to learning it, but the practice is. Python mini-projects can help to understand how the language works by using it to solve problems and perform operations. The more you practice, the more you improve.
You can also improve by looking at codes written by other programmers, solving Python Exercises online and in the same ways as you learn any other subject.
Well-equipped Python Library
This is the most important part of Python that attracts coders to it. The language itself is growing in popularity due to its large and well-equipped Library. It has additions that you can directly attach to your code, which contains functions that would otherwise require you to manually add to the program. This makes it easier to execute them and obtain results faster.
Python Library is a package that has tools for every application, so not every addition is meant for every program. It is vital to recognise the ones that will serve your purpose, and hence a waste of time to learn about the ones that do not v=concern you. Therefore, learning about Python Libraries should be more of a continuous process, where you come across new and useful ones as you progress and start working. However, there are important libraries that you can learn about right off the bat, like NumPy and Pandas for Data Manipulation and Matplotlib for Data Visualisation and Plotting.
It is easier to learn Python nowadays due to the vast availability of experts and resources online. Forums like Quora and StackOverflow can help you interact with others, clear your doubts and learn easier.
Advanced Data Science in Python
As you progress, you will be able to do Advance Data Science applications like Regression models and k-means clustering on Python, which will be symbolic of how far you’ve progressed. Your ability as a Data Scientist will increase with your proficiency in Python, and you will even be able to start with higher tier aspects like Machine Learning.
Python as a Subject - Go For It
Learning a computer programming language is like any other subject, as it takes time and effort. It all depends on your skill, proficiency and dedication. However, once mastered, Python can be a strong point on your CV and an indispensable tool in your arsenal. Its growing popularity is a sign of it becoming a no brainer as a programming language, and its simplicity, flexibility and large library contributes to it.
Also, check out our article on Python vs R for Data Science to find out which language is better.