data science tools
DATA SCIENCE

Top 10 Data Science Tools in 2024

Data Science is an in-demand profession and will continue growing in the coming years. From this, you can assume its importance, and now if you want to get into data science, you must know about the tools, skills, and frameworks required. You must begin with the skills required and for that data science tools come to the top.

In this blog, we'll be talking about the top 10 and most popular data science tools that will be immensely used by data scientists in 2024. They are the most popular ones and their usage helps data scientist perform their tasks efficiently and effectively. Let's explore them one by one:

data science tools

Table of contents


  1. Top 10 Data Science Tools in 2024
    • SQL (Structured Query Language)
    • Statistical Analysis System (SAS)
    • Tableau
    • TensorFlow
    • BigML
    • Power BI
    • Apache Spark
    • Microsoft Excel
    • Apache Hadoop
    • MATLAB - Multi-Paradigm Programming Language
  2. Conclusion
  3. FAQs
    • What are the most popular data science tools?
    • What is mostly used tool in data science?
    • What are the 5 applications of data science tools?
    • What are the highest paying data science tools?

Top 10 Data Science Tools in 2024

There are various data science tools but you need to focus on only the most popular ones. Let's look at the top and in-demand data science tools that are used very often by data scientists in 2024. These data science tools help you to perform complex tasks efficiently.

1. SQL (Structured Query Language)

SQL, developed by IBM is the most popular data science tool used by data scientists. It is a standard database language used to communicate with databases (access and manipulate data). It is used to create, update, delete, and retrieve data in databases like MySQL, Oracle, PostgreSQL, etc. A data scientist can work on operations that include defining, creating, and querying the database using SQL commands.

Features of SQL:

  • High Security
  • High performance
  • Rich Transactional Support
  • Flexible and Scalable
  • Open-source
  • Portable

Companies Using SQL:

  • Netflix
  • Uber
  • Amazon
  • LinkedIn

Supporting Platforms:

  • Windows
  • Linux
  • macOS 

2. Statistical Analysis System (SAS)

SAS is designed specifically for statistical operations, to analyze data. It provides tools for data modeling and organization, widely used in areas like advanced analytics, business intelligence, predictive analytics, and data management. You can also execute SQL queries and support informative visualization via graphs, and multiple SAS versions support machine learning, data mining, time-series reporting, etc.

Features of SAS:

  • Simple GUI
  • Data analysis
  • Statistical modeling
  • Easy to learn
  • Provides a well-managed suite of tools
  • Accessing of data from database files

Companies Using SAS:

  • Cisco
  • MuSigma
  • Infosys
  • KPMG

Supporting Platforms:

  • Windows
  • Linux
  • iOS

3. Tableau

Tableau is a data visualization tool, which has powerful graphics to create graphical visualizations. It has powerful graphics and can be used to make visualizations that can be interacted with. You can solve advanced and complex data analysis and visualization problems in time. More than 60,000 companies utilize this tool for data visualization and creating interactive dashboards.

Features of Tableau:

  • Ability to interact with different spreadsheets
  • Online analytical processing (OLAP) cubes
  • Visualize geographical data by plotting longitudes and latitudes on maps
  • Can connect Tableau to cloud services

Companies Using Tableau:

  • Apple
  • Deloitte
  • Google
  • Tesco

Supporting Platforms:

  • Windows
  • Linux
  • iOS

4. TensorFlow

TensorFlow is a Google-owned open-source ML tool for creating deep-learning neural networks. It helps you to generate dataflow graphs for numerical computations. It is used in domains like artificial intelligence, deep learning, and machine learning and helps in creating and training models and deploying them.

Features of TensorFlow:

  • Flexible
  • Scalable
  • Open-source
  • Data visualization
  • Image recognition

Companies Using TensorFlow:

  • Google
  • Intel
  • Airbnb
  • Uber

Supporting Platforms:

  • Linux
  • macOS
  • Windows
  • Android

5. BigML

BigML is an online, cloud-based, event-driven tool that helps in data science and machine learning operations. It allows you to work on techniques such as regression, classification, time series, forecasting, etc. It uses automation methods to automate the tuning of hyperparameter models and the workflow of reusable scripts.

Features of BigML:

  • Helps in processing machine learning algorithms
  • Easy-to-use interface
  • Automation techniques
  • Visualize interactive datasets

Companies Using BigML:

  • Amazon
  • Google
  • IBM
  • Databricks

Supporting Platforms:

  • MacOS
  • Windows
  • Linux

6. Power BI

PowerBI is a powerful data science tool used to generate rich and insightful reports from a given dataset. It can integrated with business intelligence. It uses formulae languages like Data Analysis Expressions (DAX) and M. It is used for interactive visualizations, large datasets, large-scale real-time analytics, and generating quick analytics.

Features of PowerBI:

  • Create data analytics dashboards
  • Develop logically consistent datasets and generate rich insights
  • Transform incoherent datasets into coherent datasets
  • Data visualization

Companies Using PowerBI:

  • Microsoft
  • Accenture
  • Capgemini
  • Hexaware Technologies

Supporting Platforms: Windows

7. Apache Spark

Apache Spark is one of the most popular open-source data processing and analytics tools that handle massive volumes of data. It is known for its robust analytics engine that provides steam processing and batch processing. Not only it is used for data analysis but also works for machine learning projects.

Features of Apache Spark:

  • Analyzes complex data streams
  • Real-time streaming data processing
  • Advanced Analytics
  • Dynamic in nature

Companies Using Apache Spark:

  • Amazon
  • Alibaba
  • CRED
  • Microsoft

Supporting Platforms:

  • Windows
  • macOS
  • Linux

8. Microsoft Excel

Microsoft Excel is a very popular data science tool used for analytical operations. It helps to build powerful data visualizations and spreadsheets that are ideal for robust data analysis. It comes with various formulas, tables, slices, filters, etc. Also, you can connect it with SQL for further operations.

Features of Microsoft Excel:

  • Built-in formulae
  • Various types of data visualization elements like charts and graphs
  • Offers pivot table
  • Easy for beginners to understand

Companies Using Microsoft Excel:

  • Amazon
  • Deloitte
  • JP Morgan Chase
  • PayPal

Supporting Platforms:

  • Windows
  • macOS
  • Android
  • iOS 

9. Apache Hadoop

Apache Hadoop is an open-source data science tool that works in distributed processing and computing large datasets, i.e., can store and manage a large amount of data. It helps data scientists for data exploration and storage by identifying the complexities in the data. It is widely known for its parallel data processing.

Features of Apache Hadoop:

  • Scales large amounts of data
  • Use Hadoop Distributed File System (HDFS)
  • High availability
  • Integrated functionality

Companies Using Apache Hadoop:

  • IBM
  • Cloudera
  • Amazon
  • Uber

Supporting Platforms:

  • Linux
  • Windows

10. MATLAB - Multi-Paradigm Programming Language

MATLAB is a closed-source, high-performing tool that helps in performing mathematical operations used in different scientific disciplines such as testing of data science models and signal and image processing. To create and link the layers of a deep neural network, we have a Deep learning toolkit.

Features of MATLAB:

  • Develop algorithms and models
  • Algorithmic implementation
  • Statistical modeling of data
  • Render data statistical modeling

Companies Using MATLAB:

  • DRDO
  • Honeywell International Inc
  • Doosan Group

Supporting Platforms:

  • Windows
  • Linux
  • macOS

Consider learning from a data science course, which will provide you with more in-depth details, and what could be more beneficial than getting an IIT-Certification, which you can add to your resume to get placed in top product-based companies hiring for data scientists.

MDN

Conclusion

Now that you know the top data science tools in 2024, you can use them as per the project's requirements. You should be aware of the features of data science tools before using them. These tools help you to perform tasks like analyzing, cleaning, visualizing, mining, and many other operations.

They help data scientists to perform complex tasks easily and efficiently. Get these tools by exploring them and using them in your project. And when you know about these top and in-demand data science tools, the key to becoming a successful data scientist becomes easy.

FAQs

Some of the most popular and in-demand data science tools are:

SQL
Tableau
TensorFlow
Apache Hadoop
Power BI
Matlab

What is mostly used tool in data science?

There are various tools used by data scientists but when you talk about the most used one, SQL is the one that is majorly used to retrieve, explore, and analyze data.

What are the 5 applications of data science tools?

There are various areas of application of data science tools, but the most popular ones are:

Machine Learning
Data Visualization
Predictive Analysis
Decision Making
Recommendation Systems

What are the highest paying data science tools?

The highest paying data science tools used by data scientists:

Statistical Analysis System (SAS)
Apache Hadoop
Tableau
Big ML
TensorFlow

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  1. Top 10 Data Science Tools in 2024
    • SQL (Structured Query Language)
    • Statistical Analysis System (SAS)
    • Tableau
    • TensorFlow
    • BigML
    • Power BI
    • Apache Spark
    • Microsoft Excel
    • Apache Hadoop
    • MATLAB - Multi-Paradigm Programming Language
  2. Conclusion
  3. FAQs
    • What are the most popular data science tools?
    • What is mostly used tool in data science?
    • What are the 5 applications of data science tools?
    • What are the highest paying data science tools?