Data holds great value in our day-to-day lives. From waking up to eating, shopping, and sleeping, we all try to do things according to data. How?

Well, you sleep for 9-6hrs because according to data this much sleep is required for maintaining metabolism and you shop according to reviews and trends (that’s data). 

Businesses are data-driven, so data is an organization’s most prized resource. All business decisions are taken based on data as this helps them to understand their customer base. But collecting and analyzing data is not that easy.

That’s why there is a separate field called data science. The person who masters the technique of data collection is termed a data scientist.  

Data science is the job of obtaining value from huge data. Data scientists understand data and obtain meaningful insights from it so that important decisions can be made. For this, data scientists have to regularly upskill themselves in the most popular and advanced data science tools and technologies. Data scientists require to broaden their skillset to have a competitive edge. 

Today, we will be discussing data science tools and frameworks that professional requires in their career.   

Enough of story building, let’s dig into it!

A-Z of Data Science

data science

Data science is the buzzword and the fastest-growing field that every industry requires and wants to leverage. This interdisciplinary domain has to deal with all kinds of data in all possible forms. As per the GlobeNewswire report, the data science niche will rise up to a compound annual growth rate of 25% by the end of 2030. This signifies that the requirement for data science is increasing every day as enterprises are heading towards a more data-driven approach.

The data professionals or the data scientists identify any data through relatable questions, by collecting data from data sources, data organization, changing data of the solution, and communicating this information for better business decisions. 

A data professional, be it a data engineer, data analyst, or data scientist should have knowledge of math, statistics, programming skills, and ML models. 

A data scientist process the data by categorizing data into the below-listed types:

  • Data Collection
  • Data Organization
  • Data Cleaning
  • Data Analysis
  • Data Visualization

Moreover, the data science tools through which the data is processed are classified into five categories:

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

Let’s find out about the data science software tool as per the above-mentioned five categories.

Best Data Science Software Tools

data science



It is an open-source object-relational database system that is in development for over 30 years by the community and for the community. PostgreSQL is well designed to handle complex problems, process large data, and refine the query run time. PostgresSQL database is popular among all programmers and data engineers. Enterprises use the PostgresSQL database to store transactional data and business intelligence-related data. 


DuckDB is also a relational but table-oriented database management system and it supports SQL queries for rich data analytics. It is designed to run faster analytical queries.  It comes with an integration for R, Java, and Python. You can also incorporate it with current data slack to acquire analytical results.  

DuckDB is meant for analytical query workloads. The queries are mostly complex, and long-running, and it takes a significant size of the store data. 

To perform its operations efficiently, duckDB holds a columnar-vectorized query execution engine, where the queries are understood as a large batch of values in a single operation. 

Web Scraping

Beautiful Soup

To start with, web scraping is the activity of extracting data from web pages. One good tool for web scraping in python is Beautiful Soup. It is a python library for extracting data out of HTML and XML files.

As a data scientist, you have to master this tool because it is an essential step in creating fully-programmed data pipelines. We extract many social media-related data through Beautiful Soup.  


Zyte is the original and the best web data platform. It is a cloud platform meant for web crawlers and web scrapers. With Zyte you can get whatever web data you need. It is easy to use and is a fully-automated web scraping solution. Web crawler collects book data in a .csv file, you can download it dynamically or integrate it with other datasets.

Zyte is there in the market for long 12 years and so it has experience in sourcing publicly available data as per the legal compliance standards.

In addition to this, if you are a student, you can get GitHub’s education pack and can get one whole year of free scraps cold unit with unlimited team members and projects.  

Data Analytics


Among Python and R, Python is the most used language by data scientists and ML engineers. This is because Python inholds almost all the libraries 

Python incorporates almost all libraries to perform the data-related task from visualization to developing machine learning API. 

Python has successfully overtaken R and Kaggle who were once the premiere platform for data science competitions. You can avail Pandas and Plotly for data fudging and visualization. To explain clearly, Pandas is a famous library for fulfilling manipulation, visualization, and data ingestion. Plotly is an inter mutual method of visualizing data. You can avail it for all kinds of visualization processes, more importantly, to impress your management team. 

Seaborn is another but the more up-to-date version of matplotlib. Pyplot through which you can create composite data visualization just with a few lines of code.  


R is no doubt a powerful and most accepted programming language in the field of data scene. R is most widely used for analytical computing and graphics. It comes with various libraries to support various phases of the data science life cycle. 

Keeping aside all its features R has a humongous and very supportive community where you can find answers to all your doubts. To use R language you will need RStudio. 

R provides a vast and comprehensible collection of tools for data analysis. It is ideal for data handling and data storage. 

It offers graphical functionalities for data and displays the output on paper or on a computer screen.  


A tableau is a no-code tool that specializes in making beautiful visualizations but it is focused more on corporate environments with data engineers. It also comes in a free model but with limited capabilities. 

Tableau is like the more you pay the more you can access including the benchmarked data from third parties. The software also comes with a non-profit tool and versions for students. It is the best tool for complex datasets. 

Moreover, all things you can do SQL can do in Tableau. With Tableau, you can design charts, graphs, and some other types of visualization for your data science and ML model. 

Machine Learning


FastAI is a user-friendly library that offers top-ranked components to achieve perfect machine learning performance. It is also available in Julia and offers better model training performance. The aforementioned is built upon Pytorch, which is popular for designing deep learning solutions. 


It comes with an out-and-out ecosystem for machine learning and supports CPU, GPU, and TPU as its complex models. It backs all browser-based applications, cloud-based production, and mobile devices. Thus, if you want to have a complete end-to-end solution for the ML model then you should integrate TensorFlow into your data stack. 


Jupyter Notebook

It is a computational notebook and a popular open-source web application tool that helps manage the data effectively. Apart from data scientists, mathematicians, and beginners can also benefit from this tool. 

This tool will give you a document pivotal experience. This web application supports all types of prime programming languages. 


Dash is perfect for developing and deploying data applications with an interactive UI. you can build a dashboard for your model performance to analyze business operations. This Dash API was developed on React.js and is available for Python, R, and Julia to form a user interface within minutes. 


Deepnote is the best tool to perform data tasks. It provides multiple integrations like GitHub and PostgreSQL. It comes with free CPU hours and will let you publish compelling data apps to develop dashboards or ML front-end applications. Deepnote is fast, engaging, and used by the majority of data scientists.

Benefits of Saddling Your Data

data science

To Serve Your Customers Better

One can recognize and analyze customer behavior by harnessing data. This will help the businesses realize their customer’s actual needs and desires that they are expecting from their services. By analyzing data you can offer a better experience across your industry. 

To Make You More Productive

Data study can highlight the problems in the internal processes that can be a reason for low productivity. Once you are aware of such things you can make necessary changes to make your operational line more efficient and productive. 

To Prevent You From Future Risk

Predictive analysis is one type of data science method in which you can underlie the areas with potential risks. By taking suitable actions you can prevent those risks and can protect your organization.

To Make Knowledged Decisions in Real-Time

Decisions should be made daily. However, these real-time commitments can be uncompromising for your business. Through data science, you can retrieve real-time analytics about the current state of the business. And you will be able to make knowledgeable decisions. 

To Maximize Your Resources

Analyzing your business data can help you discover activities and chores that are exhausting financial and human resources. This way you can make important changes and can protect your crux. 

To Improve Your Data Security

Data protection is important especially when there is a large data and a lot of people are accessing it. With data science tools, you can detect prospective security blemishes and fix them before your data gets leaked. 

To Sum Up

The best thing about these data science tools is that you don’t have to be an expert in a programming language to execute data science. All these tools come with pre-defined features and a user-friendly GUI.  

Using these data science tools will help you better understand to what extent you can leverage these tools to get important insights.

Nevertheless, if you want any assistance related to data science tools and apk, reach out to us we will be more than happy to help you.  

Extern Labs is a mobile app and software development company located in the US and India. It’s well acknowledged for providing tech-related solutions at an economical price. 

You May Also Like