Best 11 Data Exploration and Analysis tools and apps for data scientists

Other Data Exploration and Analysis Apps and Tools

Yellowbrick Data is a US-based database company delivering massively parallel processing data warehouse and SQL analytics products.

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Bokeh

Bokeh is a Python library for creating interactive visualizations for modern browsers. It also allows one to write Javascript code to build custom visualizations. Its plots can be published on the web or on Jupyter notebooks.

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Pandas

Pandas is a Python library for data manipulation and data cleaning. The tool is used to prepare data for visualization tools as well as machine learning models.

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Plotly

Plotly provides tools for building interactive, scientific data visualizations on the web browser. Plotly supports Python, R, Julia, .NET, and JS. It also provides Dash; a package for building web applications in Python.

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Scikit Learn

Scikit-learn is a free and open-source machine learning library for the Python programming language. It offers tools for solving clustering, classification, regression, and unsupervised problems.

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Matplotlib

Matplotlib is a library for creating static, animated, and interactive visualizations in Python. It offers the ability to take full control of the plotting process as well as extending third-party libraries.

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Seaborn

Seaborn provides a high-level interface for building visualizations with Matplotlib. It offers a simple API for building attractive visualizations with Matplotib at the backend. It supports normal descriptive plots as well as statistical plots.

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Altair

Altair is an open-source declarative statistical visualization library for Python, based on Vega and Vega-Lite.

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Streamlit

Streamlit is a library that enables you to quickly transform Python scripts into web applications with minimal effort. There is no front-end development required because the tool provides simple widgets for building the user interface.

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Jupyter

Jupyter offers open-source software for interactive computation across multiple languages such as Python, R, and Julia. Jupyter provides notebooks that one can write live code and share. You can use Jupyter notebook to write code, perform statistical modeling, data visualization, and machine learning. The software can be installed on your local computer or even tried online.

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Deepnote

Deepnote is a new kind of data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud.

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More AI Toolkit categories

Building POC application

Simplify your development process while showcasing the practicality of your AI projects. With our recommended tools you can create, test, and deploy proof-of-concept applications

Code Versioning

Optimize collaboration among your team members, ensure code integrity, and simplify the deployment process. These tools provide efficient code version control, making it easier to manage complex AI and Machine Learning projects.

Data Exploration and Analysis

Visualize and analyze your datasets, identify patterns, and make informed decisions. These tools offer a range of features, from data visualization to statistical analysis, empowering you to explore and understand your data in-depth.

Data Labelling and Annotation

Accelerate the training of your machine learning models by providing high-quality labeled datasets. These tools offer intuitive interfaces, annotation options, and collaboration features, ensuring accurate and consistent data labeling for your AI projects.

Data Versioning

Keep track of changes made to your datasets, improve reproducibility, and simplify collaboration. These tools enable you to manage and version your data effectively, enhancing the reliability and accuracy of your AI and Machine Learning workflows.

Development IDE

Improve your productivity, streamline your coding process, and leverage advanced debugging and testing features. These IDEs offer a seamless development experience, providing the necessary tools for building and deploying AI applications.

Experiment Tracking

Stay organized, monitor model performance, and reproduce results easily. These tools offer experiment-tracking features, allowing you to record, compare, and analyze your experiments, improving the efficiency and effectiveness of your AI workflows.

Feature Engineering

Extract, transform, and select features to enhance the performance of your machine learning models. These tools offer a range of feature engineering techniques, empowering you to create meaningful and predictive features from your data.

Feature Store

Simplify feature reuse, ensure consistency, and accelerate model development. These tools provide centralized repositories for storing and accessing features, enabling easy collaboration and improving the productivity of your AI projects.

Model Debbugging and Visualization

Gain insights into model behavior, identify issues, and improve model performance. These tools offer advanced debugging and visualization capabilities, helping you understand and optimize your models for better predictions and outcomes.

Model Monitoring

Detect anomalies, ensure model reliability, and take proactive actions. These tools offer real-time monitoring features, allowing you to track model performance and make data-driven decisions to maintain optimal model performance.

Model Packaging

Simplify model deployment, ensure consistency, and enable seamless integration into your applications. These tools offer comprehensive model packaging features, making it easier to share and distribute your trained models.

Model Registry

Simplify model versioning, track model lineage, and facilitate collaboration. These tools provide centralized repositories for storing and managing your models, enhancing reproducibility and enabling efficient model sharing.

Model Serving

Deploy models as scalable APIs and enable seamless integration into your applications. These tools offer high-performance model-serving capabilities, ensuring fast and reliable predictions for your AI-driven applications.

Model Training

Streamline the training and optimization of your machine learning models. These tools offer advanced training algorithms, distributed computing capabilities, and efficient resource utilization, empowering you to train models faster and more effectively.

Model Tuning and HPO

Automate the search for optimal hyperparameters, improve model accuracy, and save time and resources. These tools offer intelligent algorithms and efficient search strategies, enabling you to fine-tune your models for optimal performance.

Workflow Orchestration

Automate complex workflows, ensure reproducibility, and optimize resource utilization. These tools offer intuitive interfaces, workflow scheduling, and dependency management features, empowering you to streamline and scale your AI projects.

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