Top 5 AI Tools You need to know as a Data Analyst

Big data and AI-powered analytics tools are changing the face of data analysis. For a data analyst, it is about leveraging AI tools to gather information effectively that ultimately drives decisions and provides deeper perspectives. In this blog we will explore top five AI tools for data analysis that need to be on every data analyst’s radar and considered for adoption in a workflow.

Table of content:

Strengthening Your Analysis Skills with the Top 5 AI Tools 

for Every Data Analyst:

Polymer:

Polymer
Polymer is an artificial intelligence tool for data profiling and preparation of data intelligence platform. However, it helps in tasks like data cleaning and transformation that reduce time consumed by Data Analysts in data wrangling. Polymer’s predictions derived from AI contribute to high degree of correctness and harmony in data presentation, enhance reliability of the results.
The use of a data profiler and data quality. The polymer is an AI-based tool for identifying data quality problems, such as deviations and irregularities that can be found in the data. Automation of data profiling ensures that data analysts can detect and fix data quality problems promptly before conducting their analysis, thereby avoiding errors.
As a result, it also makes possible recommendation from an intelligently converted data transformers. Effective data analysts should be aware of AI-proposed data transformation and feature engineering methodologies towards prepared data for analysis. Not only does it save on time, it also makes the process of prepping up data more reliable.

Tableau:

Tableau
Tableau has transformed the landscape of data analysis and become a powerful data visualisation tool. Uniquely, it combines AI in data analysis. Tableau is able to explain data automatically by choosing key factors present in your data. It assists in identifying hidden relationships hence making it much simpler for data analytics to relay their findings in a persuasive manner.
In Tableau’s case, AI-driven capabilities of prediction, clustering, and recommendation reduce manual tasks and enable faster data analysis. For instance, Explain Data feature makes it easier to understand why some specific data points or a trend exists. It makes it easy for data analysts to generate meaningful interpretations on the same as soon as possible.
The other significant AI-driven aspect of ask data allows for a question-based approach. Tableau can generate visualisations and insights based only on plain English questions asked by data analysts. This characteristic provides an entry point for non-technical stakeholders in understanding complex information in data-driven insights.

MonkeyLearn:

MonkeyLearn
In an era of big data and social media, text data analysis is absolutely vital for data analysis. MonkeyLearn is an advanced and all-round AI tool for text analysis which allows businesses to derive meaningful intelligence from such unorganized text formats like online platforms for discussion, customer comments, and survey feedback.
Using MonkeyLearn, data analysts can execute numerous text analysis operations such as sentiment analysis, entity detection, among others, for instance, classifying text. For these jobs, the platform has pre-developed models which help data analysts to begin instantly. Furthermore, with MonkeyLearn, it is possible to tailor the developed models taking into account specific industry or company requirements.
For instance, sentiment analysis is an important tool in data analysis. MonkeyLearn’s sentiment analysis helps to classify text data as positive, negative or neutral, offering insight on customers comments and patterns.
It is a vital process of gaining intelligence out of textual entities. MonkeyLearn’s AI models can recognize entities like names, dates, places among others which is a crucial step in structuring raw data.
Another important task for data analysts becomes text classification. MonkeyLearn’s automated text classifiers help organize and analyze data by automatically categorizing text into predefined categories.

Microsoft Power BI:

Microsoft Power BI
Another notable example of an AI-powered business intelligence tool is Microsoft Power BI, a leading solution in the market of its kind. For data analysts, AI powers BI’s natural language queries and Quick Insights which make them especially useful.
Power BI allows data analysts to have a conversation with data through natural language inquiries. For instance, it converts free text queries into hardcore queries that empower the data analysts to execute free style analysis and derive beneficial insights without any knowledge of complex coding or complicated queries.
Power BI quickly creates insights out of data. With the use of power BI, data analysts are able to identify major trends and anomalies within their data, therefore making them concentrate more easily on what matters most.
In addition, Power BI provides smart data preparation and transformation options which makes data analysis even simplified. This way, data analysts obtain increased efficiency in their activities and maximum benefits from their data through AI capacities.

Akkio

Akkio
Akkio—A Platform for Simplifying Predictive Modelling. The company has ready-made machine learning models that one can modify and use in different data analysis processes. This entails model speeding up development for data analysts and the capacity to utilize artificial intelligence prediction power without requiring much specialization on machine learning.
Generating accurate predictive models entails some hard knowledge of both statistical algorithms in machine learning, data pre-processing and fine-tuning. Akkio makes it easier by offering out of the box models which include customer segmentation, sales forecasting, and sentiment analysis among others.
These models are highly flexible, allowing data analysts to adapt them according to their specific needs for particular datasets or specific objectives of a company. AI based capabilities of Akkio facilitate feature selection, hyperparameter tuning, as well as model evaluation rendering the process of generating exact and purposeful predictive models easier for data analysts.
In addition, Akkio easily integrates with various data analysis tools and platforms, thus promoting effective working process of data analytics.

Conclusion:

Data analysts today require something more than conventional tools to unlock all their data’s potential in our current data-intensive environment. The integration of AI tools like Tableau, Microsoft Power BI, Polymer, Akkio, and MonkeyLearn in your processes enable you to be more productive, insightful and add value to your organisation. The skilled data analyst could then leverage this to advance their knowledge and competitiveness as other related fields continue to move very fast. The adoption of AI tools for data analysis is not an option but a necessity.

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