how to get started with Data Science in 2022

Everything you need to know to get started with Data Science in 2022. Know about some of the popular data science tools. At the end of this article, some online resources to help you in learning data science.


Samradh Bhardwaj

2 years ago | 4 min read

There are a huge amount of data science openings from 2019. Many MNC's have seen that how good business insights can make more profit. Data Science has become the most growing field. Data Science existed for decades & will grow more for decades. That is Already Understood that "A Business Man" is always in search of ways to make the business more profitable, that's the role of data science.

"you can have data without information, but you cannot have information without data." - by Daniel Keys Moran

Table Of Contents:-

  1. What Exactly Data Science is?
  2. Data Science Real-World Examples.
  3. Get Started with Data Science
    1. Popular Data Science Tools
    2. Data Science Code Editors & IDEs.
    3. Resources to start learning Data Science

What Exactly Data Science is?

"Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains." says the Wikipedia. Now, let's understand this by breaking it down into simple language. Data Science is a field that uses mathematical approaches to extract insights from structured or unstructured data. Structured Data means data in the format of rows & columns, basically a tabular format. Unstructured data includes Text Data, Sound Data & etc.

These are the parts of Data Science. Data Science Includes Machine Learning, which includes Deep Learning.

What is Machine Learning?

Machine Learning is a kind of Artificial Intelligence, it is the process of using computer algorithms to improve the results by making use of data.

What is Deep Learning?

To be frank, it is all about big neural nets.

"Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised." - Wikipedia.

Data Science Real-World Examples

In Image Based Sectors:-

  1. Detect & Predict Disease in the image.
  2. Recognize & label Faces based on data (Attendance System).

In Sell & Buy based Services:-

  1. Recommend Products based on customer purchase history & interests.
  2. Apply Discount Strategies according to particular criteria.


  1. Netflix recommendation system is listed under the world's one of the most accurate recommendation systems.

Getting Started with Data Science

Some of the Popular Data Science Tools:-

  1. Python: User-Friendly Programming language with easy understandability but with low execution speed. Python Syntax is too easy to understand. It is one of the most powerful languages. it is recommended to use python for data science because of the high support of libraries like TensorFlow. Tensorflow is the most popular library of machine learning owned & maintained by Google.
  2. Python Libraries:
    1. Numpy, used for mathematical operations.
    2. Matplotlib, for good-looking visualizations.
    3. Pandas, to handle datasets and perform data preprocessing
    4. Tensorflow, a low-level library to make machine learning models & more.
    5. Keras, a high-level machine learning library now integrated with TensorFlow.
  3. R Programming Language: R is a statistical programming language. it is good for visualizations and statistical mathematical operations. it is not recommended for high-level machine learning projects. R Shiny is an R library used to deploy R projects.

IDEs & Code Editors for Data Science

IDE stands for Integrated Development Environment. IDE's are more functional, powerful & heavier than Code Editors. Code Editors are light weight and have less features than IDEs.

Here are some of the popular Data Science IDEs & Code Editors

Jupyter: Jupyter is Open Source Web-Based Code editor. its aim is to document the project along with lines of code. Jupyter Notebook & Jupyter Lab are different Products of Jupyter. Both are Open Source. Jupyter provides inline charts, cell-based code editing, rich markdown editor for documentation purposes. it is lightweight. Jupyter uses Ipython Console & It is the one of the best code editor for data science project. it is good for beginners. Here is How the interface looks like.

The interface is so simple and easy to understand. it starts with a menu at the top with common features like Create New Notebook, Open Notebook and etc. Below that, there is a tool bar to run, pause, re-run, duplicate the code & etc. Down that there are code cell, where we write the code. You Can switch from code cell to markdown cell from dropdown menu in the toolbar.

Price: Free

Get it hereDownload Jupyter Notebook

Pycharm: Pycharm is a freemium python IDE. it contains a vast amount of features for python development. it includes syntax highlighting, autocompletion, support for matplotlib plots, pandas dataframes and much more. it is everything you need to get started with data science.

Above are the images of Pycharm Scientific Tools. it allows to run code line by line, view matplotlib plots inside pycharm, view pandas dataframes and display variables in table format. Pycharm is a IDE and is slower than others because it's heavy. Pycharm is used by many big companies like Udemy, Trivago, Bepro Company, Alibaba Travels & more 840 big companies use this. it also makes it easier for collaborative coding.

Price: Free, Plans starting from $8.90/month

Get it hereDownload Pycharm 

Google Colab: Google Colab is a product developed by Google. Colab Notebooks are Cloud Hosted Jupyter Notebooks but with different look & feel, also with more features. Google Colab allows you to access files directly from Google Drive. Google Colab allows you to run notebooks on CPUs, GPUs & TPU(Tensor Processing Units)s. This is the greatest advantage of using Google Colab, because this will make it less time-consuming to train your ML model. Google Colab is mainly made to run Tensorflow models on Cloud with more faster GPUs & TPUs.

Price: Free, Plans starting from $9.99/month

Get it HereOpen Google Colab

Spyder: Spyder is a fully functional Scientific IDE for Python made on Python's PYQT5 module. Spyder has a great interface which makes it more useful than other IDEs. It is included with the Anaconda installation. it has every feature that you need as a Data Scientist. it includes line-by-line code running, plots within the IDE, capabilities to display datasets, documentation for every function or object of a module. There are several versions of spyder which vary according to the change in the interface over time.

Price: Free

Resources to start learning Data Science

Youtube: Youtube is a great platform to start learning anything. Here are Some Youtube Channels to get started.

  1. Krish Naik
  2. codebasics
  3. edureka
  4. intellipaat
  5. Great Learning

Online Learning Platforms: Nowadays, learning new things became too easy because of these online learning platforms. here are the popular ones.

  1. Udemy
  2. Coursera
  3. towardsdatascience
  4. geeksforgeeks

That's it for this article, we will come soon with another interesting article.


Created by

Samradh Bhardwaj

Data Scientist and ML Expert with experience in Computer Vision, NLP, and building Chatbots.







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