Top AI Tools for Data Analysts 2023

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As an interactive analytics and data visualization platform, Tableau can be used by someone unfamiliar with programming as one of its main selling features. Tableau allows users to create and distribute reports to others via desktop and mobile devices. Reports can be shared in a web browser or embedded in the app with the help of a data analytics tool, allowing data to be viewed and analyzed. This is possible whether the tableau is hosted in the cloud or on your own server. VizQL is Tableau’s query language and creates dashboards and visualization elements that users drag and drop into database queries. There is also less requirement for speed improvement on end user side.

Another top AI tool for data analysis is Microsoft Power BI, a powerful business intelligence platform that lets users filter through data and visualize it for insights. Users can import data from practically anywhere into the platform and create instant reports and dashboards. To further aid in data analysis, Microsoft Power BI also allows for the development of machine learning models. Multiple integrations are supported, including a built-in connection to Excel and Azure Machine Learning. Businesses that already use Microsoft Office products will find it easy to use Power BI for reporting, visualization and dashboard creation.

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Polymer is a robust AI technology that data analysts can use to organize and transform data into powerful databases with minimal human intervention. One of the best things about Polymer is that unlike other fantastic AI tools, it doesn’t require any code. The software uses AI to examine information and improve understanding. All this is possible with polymers without a long learning curve. Users can easily gain insights from their data by uploading spreadsheets to the platform and instantly converting them into a simplified database. Polymer boasts that it is the only software that makes user spreadsheets “instantly” “searchable, intelligent and interactive”. Data analysts, digital marketers, content developers and more use the service.

Akkio, a business analytics and forecasting tool, comes near the end of our list of the five best artificial intelligence tools for data analysts. It is an entry-level tool designed for users who are just getting started with their data. Users can upload their data to the AI ​​tool and then select the variable they want Akkio to predict to create a neural network specifically for that variable. It has great potential as a tool for predictive analytics, promotions and closing deals. To use Akkio, you don’t need any programming knowledge like the other great tools below. Eighty percent of the submitted data is used by Accio for training, while the remaining twenty percent is used for validation. The AI ​​tool provides an accuracy grade for the models and filters out false positives instead of making predictions.

MonkeyLearn is another no-coding platform with AI data analysis features that help users visualize and reconstruct data. MonkeyLearn features multiple Artificial Intelligence (AI) powered text analysis tools, allowing users to instantly analyze and visualize data. In addition, it can be used to implement text classifiers and text extractors, which facilitate automated subject and intent-based data classification and extraction of product features and user data, respectively. MonkeyLearn’s use of machine learning to streamline business processes and analyze text eliminates the need for countless man-hours of data entry. A popular feature in MonkeyLearn is the ability to automatically extract data from incoming requests. Data is sorted and categorized based on keywords and advanced text analysis and relevant content is highlighted and filed accordingly.

IBM Watson Analytics is a cloud-based business intelligence and analytics tool designed to help organizations make more informed decisions based on the results of analytics performed on their data. Natural language processing (NLP), predictive analytics (PA), and text mining are some of the AI ​​and ML methods used by Watson Analytics. Watson Analytics can be used to analyze semi-structured data found in documents and webpages and unstructured data found in social media and sensor data. Watson Analytics can aggregate and analyze data from multiple sources to reveal previously unseen patterns. IBM Watson Analytics is a powerful resource for organizations looking to better understand their data and make informed decisions. The platform is adaptable and can be scaled to accommodate businesses of any size.

RapidMiner is a comprehensive data science platform that streamlines the process of developing, deploying and managing data science projects. The data creation, modeling, evaluation and deployment tools it provides are useful for businesses of all sizes. One of its most attractive features is RapidMiner’s visual workflow designer. For users without any coding skills, it makes it easy to design and share data science projects. You can save time and energy when building projects with RapidMiner because it provides an extensive library of pre-built algorithms and machine learning models. RapidMiner is a robust data science platform thanks to its many capabilities, including a visual workflow designer. Companies of all sizes use RapidMiner as their data science platform. It’s a powerful resource for building, deploying, and monitoring data science initiatives of any size.

DataRobot is a web-based service that streamlines the entire machine learning model development and deployment process. It’s an effective method for companies of any size to improve their data-driven decision-making. DataRobot’s many useful features make it an excellent choice as an analytics platform. DataRobot streamlines the time-consuming steps involved in developing and releasing ML models. This allows organizations to build models without hiring or educating data scientists. DataRobot is a simple platform that even those unfamiliar with machine learning can use. The platform’s drag-and-drop interface model simplifies development and deployment. Fraud detection, churn forecasting, and product recommendations are just a few of the many business uses of DataRobot.

KNIME is a free and open-source IDE for those interested in machine learning and data science. Users can create data analysis and transformation processes. Data cleaning and preparation, data exploration and visualization, machine learning model construction, predictive analytics and dashboard generation are some of the many uses of KNIME. Apart from data scientists and analysts, KNIME is also useful for engineers. A large group of people use and modify it, and it’s easy to pick up and use. A large KNIME user and developer base is available to help if problems arise. KNIME is a good choice if you need a data analytics solution that is both robust and versatile. A large group of people use and modify it, and it’s easy to pick up and use.

The powerful data analytics platform Alteryx is suitable for businesses of all sizes. Some of its specialties are data mining, machine learning and predictive analytics. Alteryx’s strength is its ability to automate mundane processes. This can improve the accuracy of a company’s data analysis and save a lot of time and money. Alteryx’s ability to connect to external data sets is also robust. In this way, businesses can use Alterix to analyze data from databases, spreadsheets, social media, and other unstructured sources. Alteryx is a flexible program that has many applications. This can help with things like optimizing marketing efforts, detecting fraud more accurately, and making more informed financial decisions, among other things.

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Prathamesh Ingle is a mechanical engineer and works as a data analyst. He is also an AI practitioner and certified data scientist interested in the applications of AI. He is passionate about discovering new technologies and advancements with their real-life applications

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