Python for Data Science

Whenever we hear terms like "Data science", "Machine Learning" or "AI" there's usually one programming language that follows them Python. You might be wondering "But why Python?" , "Why is it popular among data scientists??" and most importantly "What tools shall I use as a beginner to start with, Tharun?" This blog provides you the answers to those questions.

 In this blog I am gonna share:

• Why is Python perfect for data science?
• The tools I personally use
• Getting started

Why Python for Data science?

Well python is loved in data science because:

• The syntax of python is much easier to understand even for non programmers.
• Vast amount of libraries for data analysis, Data visualization, data mining, ML and Deep Learning.
• Tons of tutorials,free courses among the internet. As it is getting widely popular people are opting for python.
• It can work with files like CSV, Excel, JSON etc..

Tools I use 

In VS code I use libraries like,

• Pandas : For loading, cleaning and exploring data sets. Like we can read the data from a data set in a let's say CSV format.

• Matplotlib : This is used for data visualization, We can understand how the data is arranged and we can see relationships in data by that we can fit an algorithm.

• Sklearn : Scikit-learn (sklearn) contains various machine learning algorithms. It also helps with splitting of data, evaluating models etc..

• NLTK : NLTK (Natural language Toolkit) is used for working with data in a text format and sentences. It is useful for building text based projects.

• Numpy : Well the name gives out it's function, Numpy(Numerical Python) is used for performing numerical computations, it is especially helpful in dealing with Multidimensional arrays and mathematical operations.

Getting Started

First setup your work environment by installing your preferred code editor. There are videos on youtube on how to install code editors like VS code, Jupyter notebook, Pycharm and Google colab.

 Install Python if you haven’t already, and then use the terminal or command prompt to install essential libraries that I have mentioned above which are enough for a beginner and start watching machine learning courses on any platforms. Code projects(even if they are small) whilst learning, it helps us understand more because we are implementing what we saw on a course. 

And that's it, If you feel like “I knew exactly what to do. But in a much more real sense, I had no idea what to do” in the wise words of Michael Scott don't worry, that just means you've officially started learning like the rest of us.