How and Where to Start with Python in Data Science?

How and Where to Start with Python in Data Science?

3 mins read

Python is the most popular and widely used language in Data Science, Machine Learning and Deep Learning. Most of the companies with the likes of YouTube, Google, Netflix etc are using Python to gear up there offerings to their customers. In this article, we will help you with how to start with Python in data science?

So far, in this series of data science tutorial, we have covered the following topics :
1. What is data science and why do we need this now?
2. What is Data Collection in Data Science?
3. What is a Descriptive Analysis?
4. What is Predictive Analysis in Data Science?

Hello Everyone, today I will be covering :
1. What is Python?
2. Python Installation

What is Python?
python is actually an object-oriented, high-level programming language with powerful semantics. It was developed by Guido Van Rossum found 1989. The python name was taken from a TV show and it’s very easy to learn this language. Several organizations are using python for their programming and Data Analysis. So when I say It’s an Object-Oriented and high-level Programming language it means that it is a sort of language or programming language wherein you have to mention data types. I will share an example wherein you have to mention data types along with the function.

Object-Oriented: Let’s say, you are trying to add a few numbers. You are trying to do the addition of 1,2,3,4,5,6,7,8,9,10. Here 1 to 10 numbers they are data types and function would be plus (+) or add. So when we say object-oriented, it means that you have to mention data types as well as the function. That’s what object-oriented means.
High-Level Programming Language: High-level programming language means that you will be writing code and that code has to be processed through compiler or interpreter. So that it will get some meaningful output. That’s the definition of an object-oriented language.

So basically python is a multi-purpose language that can be used for several purposes and when we say dynamic, there are numbers of functions available in python and with the help of those functions, you can update your program and make certain changes to it. Dynamic means keeps changing. So that is why python is famous around the organization these days and everyone, almost everybody is adapting to this language.

Advantages of Python: One of the next biggest advantages of Python is that it’s freely available for everyone and it’s easy to learn as well. So that is the background of python.

The world’s leading organizations are using Python-like YouTube, Quora, NASA, Amazon, Netflix, Google. These are the topmost companies around. Several other leading companies like LinkedIn, Dropbox and Spotify are using Python. Spotify is a musical app and has been recently launched in India.

So the ways Spotify uses python are for employing machine learning algorithms. The moment you search for a particular song in Spotify, it will create a list of your pattern of types of songs in the app. It is very intelligent and we don’t have that option in our homegrown music apps. Spotify gives the whole new experience just because python is being used and they want to make it easy even further for their customers by employing machine learning algorithms.

So you must understand by now, how important is python when it comes to an organization. NASA uses it for cryptography, YouTube uses it to enhance customer delight, and Google engine uses it to show you relevant results in a fraction of second. As per the survey, Google is being used by 70% of Americans and 80% Asians. So that is the kind of impact Google creates with the help of python.

Working with Python in data science is like a piece of cake. Honestly, start working on it. When you will start doing coding in Python, you will start realizing it. But the point is that your concept should be clear and you should know which libraries and which package should be used to get the desired result.

In our next blog, we will talk about the important libraries and what is the role of those libraries and how you can use them to fulfill your criteria or complete your task.

We strongly recommend installing the Anaconda Distribution, which includes Python, Jupyter Notebook (a lightweight IDE very popular among data scientists), and all the major libraries.

There are a plethora of sources available on the web which will guide you on how to install Python via Anaconda. Try this first since the very basic quality to become a data scientist is the ability to search the web and look out for solutions.

The easiest way is to go through these two videos which will guide you install Python. Here are the links :

It’s the closest thing to a one-stop-shop for all your setup needs.

Or Simply download Anaconda with the latest version of Python 3 and follow the wizard:

Anaconda Installation Screenshot (Python for Data Science Quickstart Guide)

Step 2: Start Jupyter Notebook

Jupyter Notebook is our favorite IDE (integrated development environment) for data science in Python. An IDE is just a fancy name for an advanced text editor for coding.

(As an analogy, think of Excel as an “IDE for spreadsheets.” For example, it has tabs, plugins, keyboard shortcuts, and other useful extras.)

The good news is that Jupyter Notebook already came installed with Anaconda. Three cheers for synergy! To open it, run the following command in the Command Prompt (Windows) or Terminal (Mac/Linux):


1 jupyter notebook

Launching from Anaconda NavigatorAlternatively, you can open Anaconda’s “Navigator” application, and then launch the notebook from there:

You should see this dashboard open in your browser:

Jupyter Notebook Dashboard

*Note: If you get a message about “logging in,” simply follow the instructions in the browser. You’ll just need to paste in a token from the Command Prompt/Terminal.

Step 3: Open New Notebook

First, navigate to the folder you’d like to save the notebook in. For beginners, we recommend having a single “Data Science” folder that you can use to store your datasets as well.

Navigating to Data Science Folder

Then, open a new notebook by clicking “New” in the top right. It will open in your default web browser. You should see a blank canvas brimming with potential:

New Jupyter Notebook