Popular AI-assisted data analytics tools of 2024

Data science has become the most critical and valuable force in this modern world. It is beneficial for businesses to make informed decisions. This field of data science relies heavily on AI and data analytics. In the future, applying artificial intelligence and data science will expand the advancement opportunities in all industries.

Data sets are becoming vast and complex. The complex information from these data sets can be broken down through these machine learning algorithms. Big data has enabled businesses to adopt sophisticated analysis processes to generate meaningful insights. This shift of advanced analytic systems was pivotal, providing a foundation to build on sophisticated machine learning algorithms.

The blog has listed notable AI-powered tools for data analytics in 2024. Let’s dive in!

What is AI Data Analysis?

AI data analysis refers to applying artificial intelligence (AI) techniques to process and interpret vast data sets, uncovering meaningful insights and patterns. Here are some use cases:

• Repetitive and time-consuming tasks involved in data processing can be automated through machine learning and other AI-driven approaches.

• AI helps organizations understand the underlying structures in their data by identifying trends, correlations, and anomalies.

• AI models can predict future trends and events by utilizing historical data and learning from patterns.

Job Title	Average Data Science Salary (INR)
Core Data Scientist	₹ 8.24 LPA
Data Analyst	₹ 6 LPA
Data Engineer	₹ 11.4 LPA
Data Architect	₹ 21.46 LPA
Data Manager	₹ 12.43 LPA

Source

Bengaluru maintains its position as the foremost center for data science and analytics employment, comprising 26% of such jobs. However, there has been a marginal decrease from the 29% recorded in 2022.

How to Use AI for Data Analysis?

A. Defining Objectives for AI-Driven Analysis

Identify the specific goals and outcomes you aim to achieve by applying artificial intelligence. This initial step sets the foundation for the entire process and ensures the subsequent stages align with the analysis’s overarching purpose.

B. Data Collection and Preparation

Ensure the data sources are reliable, relevant, and comprehensive. It may involve cleaning and preprocessing the data to handle missing values, outliers, and inconsistencies.

C. Selection of AI Techniques Based on Analysis Goals

Whether it’s machine learning, deep learning, or natural language processing, the selection should be tailored to address the specific requirements of the analysis.

D. Model Training, Evaluation, and Deployment

This step involves feeding the algorithm with historical data to learn patterns and relationships. Subsequently, evaluate the model’s performance using validation datasets to ensure its effectiveness and generalization.

E. Feature Extraction, Engineering, and Ethical Considerations

Identify relevant features contributing to the analysis and consider creating new features to capture additional insights. Moreover, ethical considerations, such as bias in data or model predictions, should be addressed.

F. Continuous Improvement and Refinement

Implement mechanisms for ongoing refinement based on new data, changing objectives, or emerging trends. Regularly update models to adapt to evolving patterns and maintain the accuracy and relevance of the analysis.

The Relationship Between AI and Data Analysis

A. Revolutionizing Data Analysis with AI

With its advanced algorithms and computational power, AI has emerged as a game-changer in handling and interpreting vast datasets. Machine learning algorithms, a subset of AI, enable systems to learn from data patterns and make predictions or decisions.

B. Real-Time Insights and Predictive Capabilities

AI systems can analyze data in real-time, enabling organizations to make informed decisions promptly. Predictive analytics powered by AI algorithms allows businesses to forecast trends, identify potential opportunities, and mitigate risks.

C. Consistency, Accuracy, and Adaptability in AI Data Analytics

Algorithms are designed to follow predefined rules and execute tasks consistently, leading to more reliable outcomes. Moreover, AI systems continually learn and adapt, refining their models based on new data inputs.

Top 10 AI Data Analysis Tools

Below are the tools combining AI and analytics for 2024:

A. RapidMiner

• RapidMiner is a powerful AI data analysis tool known for its robust tools for data preparation, machine learning, and predictive modeling.
• RapidMiner provides flexible pricing plans, including a free primary-use version. It offers a user-friendly interface and is accessible to a broad audience.

B. Tableau

• Tableau is renowned for its dynamic dashboards and powerful data visualization capabilities.
• It incorporates AI through features like Ask Data, enabling users to pose natural language questions to the data and receive instant visual responses.
• Tableau offers various pricing options, including both on-premise and cloud-based solutions.

C. Microsoft Azure Machine Learning

• Microsoft Azure Machine Learning is a cloud-based platform providing a versatile environment for developing and deploying machine learning models.
• The tool’s strength lies in its integration with the broader Azure ecosystem. 
• Microsoft Azure Machine Learning offers flexible pricing based on usage, with options for pay-as-you-go or pre-paid plans.

D. KNIME

• KNIME is an open-source toolkit that offers comprehensive tools, including machine learning, blending, and transformation capabilities.
• KNIME is free to use.

E. Google Cloud AutoML

• Google Cloud AutoML simplifies machine learning model development by providing pre-built components and automation tools. 
• The tool features a user-friendly interface with drag-and-drop functionalities.
• Google Cloud AutoML follows a pay-as-you-go pricing model.

F. PyTorch

• PyTorch is a popular open-source machine learning framework, especially known for its focus on deep learning.
• PyTorch offers extensive tools and libraries for researchers and developers.
• PyTorch is free and open-source, aligning with the philosophy of community-driven development.

G. DataRobot

• DataRobot specializes in automated machine learning, allowing users to build, deploy, and manage machine learning models with minimal manual intervention.
• DataRobot focuses on real-world business applications and predictive analytics.
• DataRobot’s pricing model involves a combination of licensing fees and usage-based charges.

H. Talend

• Talend is known for its data integration and administration prowess.
• Talend’s capabilities extend to managing big data channels, suitable for organizations dealing with large volumes of diverse data.
• Talend offers various pricing options, including open-source and subscription-based models.

I. H2O.ai

• H2O.ai is an open-source platform focusing on machine learning and predictive analytics.
• It incorporates automated machine learning features.
• H2O.ai is freely available. 

J. IBM Watson Analytics

• IBM Watson Analytics is a cloud-based tool that excels in data mining and predictive analytics.
• It can generate automated insights and perform predictive modeling.
• IBM Watson Analytics offers flexible pricing options.

How does MAHE help you realize your Data Science career dreams?

Manipal Academy of Higher Education (MAHE) is pivotal in realizing your career aspirations through its comprehensive MSc Data Science program. The program is designed to provide a deep understanding of the intricacies of data science, equipping students with the necessary skills to thrive in the rapidly evolving field. 

The curriculum covers various subjects: statistical analysis, machine learning, data visualization, and big data technologies. The institution provides hands-on experience with industry-relevant tools such as Python, R, SQL, and Hadoop.

Conclusion

In conclusion, the AI-assisted data analytics tools in 2024 showcase the relentless progress and innovation within the field. The tools discussed include various platforms, from established giants to emerging startups. These tools underscore the dynamic nature of the data analytics ecosystem. Therefore, integrating artificial intelligence is pivotal in enhancing analytical capabilities, uncovering insights, and driving informed decision-making.