Top 7 AI Tools for Data Analysts

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By Hiba Akbar

Data analysis is a crucial part of the direction and critical thinking in different enterprises. As the volume of information keeps on developing dramatically, the requirement for proficient and successful apparatuses to break down this information turns out to be progressively significant. Lately, Artificial Intelligence has arisen as a distinct advantage as AI tools for data analysts. 

AI Tools for Data Analysts

This article investigates a portion of the AI tools for data analysts.

Artificial Intelligence Data Analysis

Artificial Intelligence Data Analysis alludes to applying AI methods and calculations to process, decipher, and get significant experiences from huge and complex datasets. It utilizes AI, profound learning, and other computer-based intelligence advances to robotize examination, recognize examples, and make expectations or arrangements. 

Artificial intelligence data analysis empowers associations to separate important data from tremendous information all the more effectively and precisely. By AI capacities, for example, normal language processing, picture recognition, and oddity identification, organizations can acquire a more profound comprehension. They can settle on information-driven choices and open secret doors for development and streamlining.

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AI Tools for Data Analysts

The following AI tools for data analysts can assist you with determining better bits of knowledge in your next data analysis project:

  • Microsoft Azure Machine Learning
  • DataRobot
  • Google Cloud AutoML
  • PyTorch
  • Tableau
  • KNIME 
  • RapidMiner

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Now, we’ll discuss the key features of the tools one by one in detail.

1. Microsoft Azure Machine Learning

A cloud-based stage-as-a-administration presented by Microsoft Azure is the Azure Machine Learning Service. The Microsoft innovation gives start-to-finish AI capacities in the cloud, from model improvement to running tests to show sending as a Peaceful API endpoint. 

The stage additionally upholds coding in both Python and R through Jupyter Scratchpad, JupyterLab, and R Studio for various client inclinations.


Key Features

  • Azure Machine Learning upholds a few process choices for machine learning and AI jobs.
  • Azure Machine Learning allows datastores to mount information from Azure Capacity administrations, for example, an information lake store.
  • Azure Machine Learning’s notepads support Jupyter scratch pad, JupyterLabs, and RStudio. Clients have the adaptability to open up a current Jupyter journal portion or make a custom piece contingent upon the AI use case.
  • Azure Machine Learning Planner highlight allows clients to indicate and make machine learning through an intuitive GUI. The planner upholds a few pre-built modules for clients to browse during the model turn of events.
  • The Automated Machine Learning highlight allows clients to run robotized model tests and access requests to calibrate and prepare a current model to arrive at an ideal objective measurement indicated by the clients.

2. DataRobot

DataRobot is an AI stage for computerizing, guaranteeing, and speeding up prescient examination. It helps information researchers and experts fabricate and convey exact prescient models in a part of the time expected by different arrangements. 

DataRobot makes it simple to utilize and upgrade the most important open-source displaying methods from R, Python, Flash, H2O, VW, and XGBoost, and the sky’s the limit from there.


Key Features

  • Prior to running any calculations, DataRobot decides the univariate significance of each element for the objective variable.
  • DataRobot produces a quantitative positioning of how effective each component is for each model it produces.
  • DataRobot utilizes master model outlines that consequently select pertinent highlights. The stage additionally upholds manual tuning.
  • By running DataRobot on various subsets of elements, clients can perceive how different component records are analyzed.

3. Google Cloud AutoML

Google Cloud AutoML gives a mechanized AI administration to both IT and business clients. AutoML assists users with effectively assembling top-notch custom models with restricted AI mastery required. With Google Cloud AutoML, you will invest less energy in information readiness and model structure and more in settling on choices in view of expectation execution.


Key Features

  • Status Dashboard
  • Information Catch and Move
  • Information Extraction
  • Information Import/Commodity
  • Visual Investigation

4. PyTorch

PyTorch is a Machine Learning system in light of the Python programming language and the Torch library. Torch is an open-source ML utilized for making profound brain organizations and is written in the Lua pre-arranging language. It’s one of the favored stages for profound learning research. The system is worked to accelerate the cycle between research prototyping and arrangement.


Key Features

  • Offers designers a simple-to-learn, easy-to-code structure that depends on Python.
  • Empowers simple investigating with famous Python instruments.
  • Offers adaptability and is very much upheld on significant cloud stages.
  • Gives a little local area zeroed in on open source.
  • Trades learning models to the Open Neural Network Exchange (ONNX) standard arrangement.
  • Offers an easy-to-use interface.

5. Tableau

For Data Analysis, a top Data Visualization device is, as of now, called Tableau. It was positioned as a forerunner in examination and business knowledge by Gartner’s Magic Quadrant. It was established in America in 2003, and in June 2019, Salesforce purchased Tableau. 

It helps clients create different diagrams, guides, dashboards, and stories to envision and dissect information to support business direction. Moreover, Tableau urges everybody to be aware and grasp their information. You can associate with your information accessible as Succeed, CSV, and so on., or then again interface straightforwardly to your information base.


Key Features

  • Tableau is used for information perception. Its innovation upholds complicated computations, information mixing, and dashboarding so that staggering perceptions might be delivered that give bits of knowledge.
  • Tableau Dashboard incorporates a phenomenal detailing capability that can tailor the dashboard definitively for a given gadget, similar to a PC or cell phone.
  • A large number of columns of information can be taken care of with effectiveness by means of Tableau.
  • It offers various choices for representation that further develop the client experience.
  • It has worked really hard, situating itself as the forerunner in information representation programming.


KNIME is a low-code information science and information planning stage that makes understanding information and planning scientific work processes open to everybody. The KNIME suite incorporates two instruments: The KNIME Examination Stage is a work area-based instrument where experts and engineers build work processes. 

KNIME Server is undertaking programming intended for group-based coordinated effort, mechanization, the executives, and organization of work processes.


Key Features

  • Adaptability through complex information taking care of (cleverly programmed storing of information behind the scenes while amplifying throughput execution) 
  • High, straightforward extensibility through a clear-cut Programming interface for module expansions.
  • Instinctive UI.
  • Import/product of work processes (for trading with other KNIME clients)
  • Equal execution on multi-center frameworks.
  • Order line adaptation for “headless” bunch executions.

7. RapidMiner

Rapidminer is a complete information science stage with a visual work process plan and full computerization. It implies that we don’t need to do the coding for information mining errands. Rapidminer is quite possibly one of the most famous data science instruments. This is the graphical UI of the clear cycle in Rapidminer. 

It has the vault that holds our dataset. We can import our datasets. It also suggests several widespread data files that we can use. In addition to this, we can even collaborate with a database corporation.


Key Features

  • Information Designing. Make your information ‘snort work’ less excruciating.
  • Model Structure. Smooth out model creation — whether you’re new to information science or an old pro — through mechanized, visual, and code-based approaches.
  • Model Operations.
  • Man-made intelligence application building.
  • Cooperation and administration.
  • Trust and straightforwardness


AI tools for data analysts have revolutionized the field of data analysis by providing them with powerful capabilities to extract insights, identify patterns, and visualize complex data. Machine learning algorithms enable analysts to make accurate predictions and classifications, while Natural Language Processing techniques help in extracting valuable information from unstructured textual data. 

Additionally, automated data visualization tools simplify the process of presenting data in a visually appealing manner. As AI continues to evolve, we can expect further advancements in AI tools for data analysis, empowering analysts to derive even more meaningful insights from data.

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Are there AI tools for Data Analysts?

Artificial intelligence is changing information examination by acquiring quick and mechanized ways to get a handle on complex informational indexes through natural language search. There are different artificial intelligence devices accessible that assist with information examination, from both existing programming and new ones, like Microsoft Succeed, Jupyter artificial intelligence, and Tableau.

Which is the best AI tool to analyze data?

Polymer Search is a robust instrument with strong artificial intelligence made to transform your drilling information into a more smoothed out, strong, and adaptable data set, all without composing a solitary line of code. Polymer drives your spreadsheet with a robust AI to assist you with dissecting your information and working on how you might interpret it, all with a couple of snaps.

Can AI take over Data Analysts?

Indeed, obviously, certain pieces of the analyst’s job will be supplanted by artificial intelligence. However, numerous different parts will rather be improved or advanced, in this manner permitting experts to accomplish more excellent work significantly quicker.