Top 38 Data Science Tools To Use In 2023

It’s a fact that industries nowadays make their strategic decisions based on various sets of data, such as past trends, consumer habits, popular products, competitor behaviours, etc. But, how do these companies use relevant data to make strategic decisions?

A data scientist helps them achieve this goal! Data scientists are responsible for extracting, manipulating, pre-processing, and generating predictions from the given data. They do this by using various Data Science tools. In this blog, we will learn about these tools used by data scientists in almost every industry today.

Top 38 Data Science Tools List

1. SAS

SAS or Statistical Analysis System is one of the oldest DS tools in the market. As the name suggests, it is specifically designed for statistical operations. It is a close-sourced integrated proprietary software suite.

SAS is used for statistical analysis, advanced analytics, BI, and data management. It enables users to integrate, cleanse, prepare, and manipulate data by assisting in data mining, predictive analytics, and machine learning.

Also Read: Data Science Applications

2. Tableau

Tableau is a data visualization software that assists in data analysis and creating engaging presentations. This software helps to present data you analyze in an interesting and understandable way. 
Tableau also has the ability to visualize geographical data for plotting longitudes and latitudes on maps. The platform comes equipped with an active online community where you can share your findings and ideas. It comes with a free version called Tableau Public, which can be accessed by anyone.

3. TensorFlow

Developed by Google, TensorFlow is used with advanced technologies like data science, machine learning, artificial intelligence, etc. Due to its popularity and relevance today, it is an open-source and ever-evolving toolkit. It is a Python library you can use for establishing and training data science models. 

Tensorflow has a variety of applications, such as speech recognition, image classification, drug discovery, image and language generation, etc. It is easy to use as it is written in Python and is widely used for differential programming.

4. BigML

BigML offers a fully interactable, cloud-based GUI environment used for processing machine learning algorithms. It provides standardized software using cloud computing for industry requirements.

BigML specializes in predictive modeling and uses various ML algorithms, like clustering, classification, time-series forecasting, etc. It provides an easy to use web-interface using Rest APIs. You can create a free account or a premium account based on your data needs.

5. Knime

Knime is an open-source platform. You can operate it with minimum programming expertise. The easy-to-use Graphical User Interface (GUI) of the platform assists in data extraction, transformation, and analysis.

6. RapidMiner

RapidMiner is one of the popular Data Science tools known for its capacity to provide a secure and scalable environment for data preparation. Any data science or ML model can be prepared from scratch using RapidMiner.

RapidMiner can help you perform various data science tasks like text mining, predictive analysis, model validation, comprehensive data reporting, high-end analytics, and tracking in real time.

7. Excel

Excel is a part of Microsoft Office. It can be used by beginners to understand the basics of data science. It lets you apply formulas, key combinations, and various functions to your data to make data representation and analysis easier.

It also provides an interactable GUI environment to specifically pre-process information easily, as it is not fully capable of handling large data sets. However, it is still one of the most preferred tools for creating powerful data visuals and spreadsheets.

8. Apache Hadoop

Hadoop is an open-source framework that can create simple programming models and distribute extensive data set processing across thousands of computer clusters. It works really well for research and production purposes and offers multiple modules. It is also perfect for high-level computations.

9. PowerBI

PowerBI is one of the few Data Science tools integrated with business intelligence. It can be combined with other Microsoft data science tools for performing data visualization. 

You can generate insightful reports along with a personal data analytics dashboard from a given dataset using PowerBI. The incoherent sets of data can be turned into coherent sets using PowerBI, which can further generate powerful insights.

Also Read: Data Science Projects

10. DataRobot

DataRobot is an AI-driven, ML-integrated automation platform that aids in developing accurate predictive models. A wide range of machine learning algorithms, including clustering, classification, and regression models can be carried out by DataRobot.

The platform also supports parallel programming which allows the use of thousands of servers to perform simultaneous data analysis, data modelling, and data validation. Its easy-to-use and responsive GUI makes data analytics possible for freshers and expert data scientists.

11. Sap Hana

Sap Hana is a relational database management system that makes data storage and retrieval easy. The in-memory data storage stores data in the main memory, offering enhanced querying and data processing for effective storage and retrieval. Text search and analytics, graph data processing, predictive analysis, etc. can be performed using Sap Hana.

12. MongoDB

MongoDB is a high-performance database. The platform lets you store large volumes of data in a collection by the name of MongoDB documents. It is a highly scalable platform as it consists of all the functions of SQL, supports dynamic queries, offers high data replications capability, etc.

13. Python

Python is one of the most popular languages in the world, especially for machine learning. It has a simple syntax, is easy to learn, and is cost-effective. It is exceptionally versatile and is used by various industries. It comes with dynamic semantics, built-in data structures, and dynamic typing and binding capabilities. It allows you to perform various mathematical, statistical, and scientific calculations.

14. Trifacta

This tool is used in data science for the purpose of cleaning and segregating the data. It cleans the data by removing unnecessary data from large data sets. It also separates structured data from unstructured data, making it easy to detect and rectify errors.

15. Minitab

Minitab is a one-stop solution for data manipulation and analysis. It allows you to identify trends and patterns in an unstructured dataset and then simplify that data for data analysis. As a data scientist, you can automate data science calculations and graph generation while performing regression analysis.

16. R

R is a highly scalable software specifically meant for statistical analysis and programming. Functions like data clustering, statistical modeling, data cleaning, and visualization can be performed in less time effectively.

17. QlikView

QlikView is known for its business intelligence as it lets you analyze multiple things at a given time. Its strength lies in deriving relationships out of the unstructured data and then analyzing the same. It also lets you visually represent the analyzed data. Its fast in-memory data processing lets you perform data aggregation and compression quickly.

18. Google Analytics

Google Analytics is one of the most popular Data Science tools in the field of digital marketing. Marketers use data derived from Google Analytics to understand customer behavior and study various browsing patterns of websites to make strategic marketing decisions. It is extremely easy to use and can also be used by non-technical professionals.

19. MATLAB

MATLAB is an enterprise-focused DS tool. It is a multi-paradigm numerical computing environment for processing mathematical information. It is a closed-source software that facilitates matrix functions, algorithmic implementation, and statistical modelling of data.

20. Julia

Julia is a programming language designed specifically for Data Science, but it is so advanced in efficiency and functions that it is compared with Python. It can match up to the speed of its contemporaries like C and C++. It has a math-friendly syntax and automatic memory management which lets you perform complex statistical calculations quickly.

21. MicroStrategy

MicroStrategy is a popular choice for organizations. It has a vast array of powerful functionalities revolving around data discovery, visualization, and reporting that make the platform an effective Data Science software for enabling decision-making processes. You can learn more about these data science tools through this in-depth data science course. 

22. SPSS

SPSS is an effective tool in science research where statistical analysis plays a critical role in obtaining informative insights from data sources. It provides researchers with essential features that help through simplified data manipulations, descriptive statistic calculations, and regression analyses.

23. D3.js

D3.js enables users to generate dynamic and interactive expressions from their raw datasets directly within web browsers. It has an impressive suite of features for manipulating and linking various formats of data with HTML/SVG/CSS elements in user-defined ways. It also provides limitless possibilities when it comes down to producing customized visualizations.

24. ggplot2

ggplot2 is a Data Science tool for data visualization found in the R programming language. This remarkable program provides users with the ability to develop customizable and superior-quality visualizations through a layered process. Its features allow users access to numerous statistical graphics while supporting various plot types.

25. Matplotlib

Matplotlib has a collection of tools ideal for generating everything from stationary graphs to interactive displays. This powerful software has everything necessary you need to create beautiful images easily. It is well-suited for applications involving line and scatter plots (among others).

26. Jupyter

As an open-source web-based application supporting numerous programming languages, Jupyter is an ideal choice for researchers engaged in extensive work. It covers areas including data analysis and machine learning.

27. NLTK

NLTK empowers experts in this field by providing powerful features such as tokenization or scrambling text into meaningful pieces for easier reading comprehension in every known global dialect. 

28. Scikit-learn

Scikit-learn is one of the most capable machine-learning libraries available for use in the Python programming language. It has powerful tool sets that focus on classifications, regressions, and many other areas.

29. Weka

Weka focuses on important areas of data interpretation, such as preprocessing techniques, classification options, etc. The clear GUI makes it ideal for simultaneous use by teams across a variety of fields.

30. Microsoft HD Insights

Microsoft HDInsight excels at delivering managed data allowing for efficient processing and analysis of massive datasets in various programming languages. It uses different tools in an organized environment. 

31. Informatica PowerCenter

Informatica PowerCenter provides advanced capabilities. Being an enterprise-ready solution, this platform empowers users to achieve seamless ETL workflows across diverse sources while maintaining the highest standards of reliability.

32. H2O.ai

H20.ai offers users access to credible resources such as scalable infrastructure and high-performing elements suitable for various algorithms encompassing frameworks in deep learning. It also allows customers access to analyze large-scale data operations.

33. IBM SPSS

It is a powerful software suite employed in performing different statistical analyses, like predictive modeling and data management. It also has extensive features for tasks like hypothesizing-testing or advanced analytics functions.

34. Keras

This outstanding tool helps in creating and training neural networks with ease through its user-friendly interface. It offers simplified processes to build even the most intricate deep-learning models.

35. NumPy

It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. It is widely used in data analysis, numerical computing, and machine learning.

36. Pandas

Pandas is a powerful data manipulation and analysis library for Python. It provides data structures like DataFrames for efficient handling and analysis of structured data. 

37. PyTorch

PyTorch is a deep learning framework widely used for building and training neural networks. It offers dynamic computational graphs, making it flexible for model development and experimentation. 

38. SciPy

SciPy is a library for scientific computing in Python that builds upon NumPy. It offers a collection of algorithms and functions for numerical optimization, integration, interpolation, signal processing, linear algebra, and more.

Conclusion

According to a study, 91% of all the decision taken in large organizations is based on the data extracted through various data science tools. Data science is truly the skill of the future as all the tools or platforms mentioned above have been instrumental in the progression of data science as a field. Let us know in the comment section below which one is your favorite tool to understand data.

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