Software Alternatives, Accelerators & Startups

Chartio VS Scikit-learn

Compare Chartio VS Scikit-learn and see what are their differences

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Chartio logo Chartio

Chartio is a powerful business intelligence tool that anyone can use.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Chartio Landing page
    Landing page //
    2023-07-09

Chartio is a business intelligence system that makes databases as easy to analyze as a spreadsheet. You don’t need to know SQL or a proprietary language to use Chartio, but you can use SQL if you prefer. Chartio enables business users to transform data themselves – without the help of a data scientist. Chartio is simple to set up. You can connect and start analyzing your data in less than an hour. And it gives you the flexibility to quickly add new data and storage as your needs change.

  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Chartio features and specs

  • User-Friendly Interface
    Chartio offers a highly intuitive and easy-to-use interface that makes it accessible for users with varying levels of technical expertise.
  • Powerful Data Visualization
    Chartio provides robust data visualization tools that allow users to create complex and detailed charts and dashboards with ease.
  • Wide Range of Data Connectors
    Supports integration with numerous databases and data sources, making it versatile for different business needs.
  • Collaborative Features
    Enables team collaboration through shared dashboards and reports, facilitating better decision-making.
  • Real-Time Data Updates
    Capable of processing and displaying real-time data, enabling users to make timely and informed decisions.

Possible disadvantages of Chartio

  • Cost
    Chartio can be expensive compared to other data visualization tools, especially for small businesses or startups.
  • Learning Curve
    Despite its user-friendly interface, new users might still face a learning curve to fully leverage advanced features.
  • Limited Customization
    While powerful, some users may find the customization options for visuals and dashboards somewhat limited compared to competitors.
  • Dependency on Internet
    Requires a stable internet connection for optimal performance, which may be a drawback in environments with poor connectivity.
  • Closed in 2022
    As of March 1, 2022, Chartio was acquired by Atlassian and the product itself was retired, making it unavailable for new users.

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

Chartio videos

Chartio: Demo and Review

More videos:

  • Demo - Chartio demo video

Scikit-learn videos

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Category Popularity

0-100% (relative to Chartio and Scikit-learn)
Data Dashboard
75 75%
25% 25
Data Science And Machine Learning
Business Intelligence
100 100%
0% 0
Data Science Tools
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Chartio and Scikit-learn

Chartio Reviews

25 Best Reporting Tools for 2022
It features data exploration, customizable dashboards, and different types of charts. Chartio provides users connections from Amazon Redshift to CSV files helping them explore data. Users can also share dashboards and reports with members via E-Mail and track corporate metrics using the solution’s Snapshot feature.
Source: hevodata.com
The Top 14 Marketing Analytics Tools For Every Business
The software provides business owners, product teams, data analysts, and marketers with helpful organizational tools. Chartio offers a central dashboard and functions for data exploration with the ability to present data from multiple sources in a variety of charts. The main fault with Chartio, however, is that is some users may be faced with a steep learning curve,...
Source: improvado.io

Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...

Social recommendations and mentions

Based on our record, Scikit-learn seems to be more popular. It has been mentiond 31 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Chartio mentions (0)

We have not tracked any mentions of Chartio yet. Tracking of Chartio recommendations started around Mar 2021.

Scikit-learn mentions (31)

  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
  • 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
    Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 5 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 11 months ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / about 1 year ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / almost 2 years ago
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What are some alternatives?

When comparing Chartio and Scikit-learn, you can also consider the following products

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Domo - Domo: business intelligence, data visualization, dashboards and reporting all together. Simplify your big data and improve your business with Domo's agile and mobile-ready platform.

OpenCV - OpenCV is the world's biggest computer vision library

Grow - Grow is a business intelligence software that empowers businesses to become data-driven and...

NumPy - NumPy is the fundamental package for scientific computing with Python