Top Data Science Tools for 2022

Check out this curated collection for new and popular tools to add to your data stack this year.

Top Data Science Tools for 2022
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The list includes tools for beginners and experts working in the data field. These tools will help you with data analytics, maintaining databases, perform machine learning tasks, and finally help you generate a report. These tools have also helped me handle new and unseen datasets faster so, if you are looking to become a super data scientist in 2022 then try adding these tools into your data stack.  

The tools are divided into five categories:

  • Database
  • Web Scraping
  • Data Analytics
  • Machine Learning
  • Reporting




Top Data Science Tools for 2022
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DuckDB is a relational table-oriented database management system that supports SQL queries for generating data analytics. It was designed to run faster analytical queries workloads. It also provides integration for R, Python, and Java. You can integrate it with your current data stack to produce analytical results. I usually use it for running analytics on .csv files and storing web app logs. To learn more read: The Guide to Data Analysis with DuckDB.



PostgreSQL is an open source object-relational database system which has been in development for 30 years by community and for community. It can handle complex queries, process large data, and optimize query run time. It is the most popular database among developers and data engineers. Almost all technical interviews or tests involve some kind of PostgreSQL questions. I use psycopg2 to ingest data and run data analysis in Jupyter notebooks. 


Web Scraping


Top Data Science Tools for 2022
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Beautiful Soup

Beautiful Soup is a Python library for pulling data out of HTML and XML files. If you are a data engineer or data scientist then you must master this tool to extract data from websites. During the data collection process, your manager will ask you to either learn a new web scraping tool or ask you to create a Python file to automate web scraping. It is an important step in creating fully-automated data pipelines. I use Beautiful Soup for scraping COVID19 data and extracting various social media data. 



Zyte is a cloud platform for running web crawlers and web scrapers. You can manage your web crawlers and run web scraping jobs. I immediately fell in love with the ease of use and fully automated web scraping solution. My web crawler is still running, collecting books data in a .csv file so, I can either download the file manually or integrate it with other databases for a fully automated ecosystem. If you are a student, you can sign up for GitHub's education pack and get 1 Free Forever Scrapy Cloud Unit - unlimited team members, projects, or requests.


Data Analytics


Top Data Science Tools for 2022
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Python is the most used language among data scientists and machine learning engineers. You can find almost all the libraries within Python to perform any data-related task from visualization to building machine learning API. I generally use Pandas and Plotly for data manipulation and visualization. 

  • Pandas is the popular library for performing data ingestion, manipulation, and visualization tasks. 
  • Seaborn: is an advanced version of matplotlib.pyplot that allows you to create complex data visualization with a few lines of code. 
  • Plotly provides an interactive way of visualizing data. I use it for all the visualization tasks, mostly to impress the management team. The custom animations and interactivity make data come to life.   



R is quite popular among data analytics and statisticians. It was created to solve statistical problems and now it has evolved into a complete ecosystem for data science. The R comes with Tidyverse that is the mother of all packages. 

Here are some of the famous packages:

  • ggplot2: for creating an amazing data visualization. 
  • dplyr: for data augmentation and manipulations.
  • readr: for loading CSV and TSV files.



Julia is an emerging new-age programming language that was created to solve scientific problems. With the introduction of popular libraries, Julia is becoming the go-to tool for performing data experiments and generating data analytics reports. If you want to learn more about data analysis with Julia, read my blog.

The data analysis packages:

  • CSV: is for loading CSV files
  • DataFrames: for data manipulation and data analytics.
  • Plots: is used for data visualization.



Tableau is a no-code tool that provides you the freedom of visualizing all kinds of data. It is my go-to tool for visualizing geospatial, categorical, and complex datasets. Tableau can be used with popular languages such as Python and R to provide end-to-end data science solutions. It is free and can be integrated with multiple databases. Recently, I have created a dashboard for impressing the higher management. It monitors the distribution of engineers across Pakistan. 


Machine Learning


Top Data Science Tools for 2022
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FastAI is a beginner-friendly library that provides high-level components to achieve state-of-the-art machine learning performance. It is now available in Julia to provide better model training performance. The FastAI is built upon Pytorch which is a popular library for designing deep learning solutions. I will highly recommend beginners to start their deep learning journey with a free course



Scikit-learn is used by data analytics, data scientists, and data engineering to perform data processing and machine learning jobs. It is an open-source library built upon NumPy, Matplotlib, and Scipy. Scikit-learn is used for simple predictive analysis but it lacks support for advanced deep learning problems. I use it regularly for time-series, regression, and classification problems. 



TensorFlow (TF) provides a complete ecosystem for machine learning. It supports CPU, GPU, and TPU for training complex models. TF supports browser-based applications, mobile devices, and cloud-based production. If you want a complete end-to-end solution for machine learning models I suggest you start by incorporating TF into your data stack.




Top Data Science Tools for 2022
Image by Fullvector


Jupyter notebook

Jupyter Notebook was developed to provide a document-centric experience. It is a web application that supports all the major programming languages. This tool is famous among all levels of data scientists, if you are a beginner or expert this would be a tool for creating scientific reports. You can run the web server locally or use a cloud platform such as Google Colab.



Deepnote is one of my favorite tools to perform all the data tasks. It is a cloud notebook platform that comes with multiple integrations such as GitHub and PostgreSQL. The platform provides you with free CPU hours and allows you to publish your notebooks in the form of articles. Recently, they have allowed publishing interactive data apps which can be used to develop dashboards or machine learning front-end applications. You can run your notebook on Python, R, Julia, Java or any preferred programming language. Deepnote is fast, interactive, and used by thousands of data scientists.     



Dash is ideal for building and deploying data apps with interactive user interfaces. You can create a dashboard and use it for model performance monitoring or to monitor company's operations. The Dash API was built on Plotly.js and React.js. It is available for Python, R, and Julia for you to create user inference within 10 minutes. 



The data science field is still growing and people are learning the latest tools to perform multiple tasks. Most companies want you to perform; data engineering, machine leaving, and MLOps tasks daily. Sometimes, they will advertise they are looking for data scientists but in reality, they are looking for someone to automate their workflow. 

In this blog, we have learned about databases, web scraping, data analytics, machine learning, and reporting tools. In the field of data science, there is no one-stop solution for all problems, you need to keep looking for better tools to be considered as a viable employee. So, If you are looking to get productive and want to impress your bosses then start learning these tools to excel in the field.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.