AI tools play a significant role in simplifying the responsibilities of Data Analysts within the realm of data science. These technologies streamline the process of effectively analyzing data and extracting valuable insights from vast databases. Among the five AI tools that prove beneficial for data analysis are powerful open-source machine learning software libraries such as TensorFlow, RapidMiner, KNIME, Orange (a component-based data mining software), and Weka. Notably, RapidMiner stands out for providing an integrated environment that covers data preparation, machine learning, and model deployment, thus contributing substantially to enhancing the capabilities of data analysts in the field of data science.
- Tableau:
Tableau, a leading AI tool for data analysts, offers an interactive platform for data visualization and analytics. What sets it apart is its user-friendly interface, which doesn’t require coding expertise. Users can effortlessly create and distribute reports across various platforms. Tableau utilizes VizQL, a query language that converts visuals into backend queries, eliminating the need for speed optimization. It handles complex computations, data blending, and dashboarding for large datasets, whether deployed on-premises or in the cloud. In summary, Tableau significantly enhances the efficiency of data analysis. - Microsoft Power BI:
Microsoft Power BI, a prominent AI tool designed for data analysis, serves as a comprehensive business intelligence platform. Users can seamlessly import data from multiple sources and promptly initiate the creation of reports and dashboards. Power BI also boasts support for machine learning models and other AI-driven functionalities for data analysis. It seamlessly integrates with native Excel and Azure Machine Learning, among other technologies. For businesses already utilizing Microsoft technologies, Power BI is an easily integrated solution for data reporting and visualization. Noteworthy advantages include personalized dashboards, secure report sharing, and unrestricted memory and speed capabilities. - Polymer:
Polymer, a robust AI tool, excels at transforming data into a versatile and potent database, all with a user-friendly, code-free interface. This program employs AI to enhance users’ comprehension of their data. With Polymer, users simply need to upload their spreadsheet, and it instantly converts it into an interactive database, eliminating the need for a protracted onboarding process. A diverse range of professionals, including data analysts, digital marketers, and content producers, rely on Polymer to elevate the intelligence and interactivity of their spreadsheets. - Akkio:
Akkio is a user-friendly business analytics and forecasting tool that caters to beginners seeking to delve into data analysis. Users can upload datasets, select a variable for prediction, and Akkio will construct a neural network around it. This no-code machine-learning platform holds great potential for predictive analysis, marketing, and sales. Akkio employs 80% of the submitted data for training and allocates 20% for validation. It’s a reliable tool for data analysis, emphasizing model accuracy assessment rather than mere outcome prediction and highlighting false positives. - MonkeyLearn:
MonkeyLearn, a no-coding platform, empowers users to leverage AI data analysis features to visualize and reorganize their data. It offers a range of AI-driven text analysis tools for swift data analysis and visualization. Users can configure text classifiers and extractors to categorize data by topic or intent and extract specific features. Through machine learning, MonkeyLearn automates business processes and streamlines text analysis, significantly reducing the time required for manual data processing. It can automatically extract information from incoming tickets, categorize it using advanced text analysis and keywords, and highlight and classify specific content for simplified sorting and processing.
Source : https://www.analyticsinsight.net/top-5-ai-tools-you-need-to-know-as-a-data-analyst/