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Tableau Public VS Scikit-learn

Compare Tableau Public VS Scikit-learn and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Tableau Public logo Tableau Public

Your data has a story. Share it with the world.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Tableau Public Landing page
    Landing page //
    2023-10-07
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Tableau Public features and specs

  • Free to Use
    Tableau Public is available for free, allowing individuals and organizations to create and publish data visualizations without incurring costs.
  • User-Friendly Interface
    The platform provides a drag-and-drop interface that simplifies the process of creating interactive visualizations, making it accessible to users without a technical background.
  • Cloud-Based Sharing
    Visualizations can be published to the cloud, making it easy to share insights and dashboards with others via a URL.
  • Community Support
    A large and active community offers a wealth of publicly available visualizations, tutorials, and forums, providing support and inspiration.
  • Rich Visualization Options
    Tableau Public offers a variety of visualization types, enabling users to create complex and insightful visual stories.

Possible disadvantages of Tableau Public

  • Limited Data Security
    Since visualizations are publicly accessible, sensitive data cannot be used with Tableau Public, limiting its use for confidential business information.
  • Data Source Limitations
    Tableau Public supports fewer data source connections compared to the full version of Tableau, potentially restricting data integration.
  • File Size Restrictions
    There are limits on the size of the data files that can be uploaded, which may be insufficient for large datasets and can constrain analysis.
  • No Offline Access
    Because it is cloud-based, an internet connection is required to access and publish dashboards, potentially causing issues for users with unreliable connectivity.
  • Limited Feature Set
    As a free platform, Tableau Public lacks some advanced features and customization options available in paid versions of Tableau, which may limit analysis capabilities.

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.

Analysis of Scikit-learn

Overall verdict

  • Yes, Scikit-learn is generally regarded as a good library for machine learning, especially for beginners and intermediate users who need reliable tools with efficient implementation of numerous algorithms.

Why this product is good

  • Scikit-learn is considered a good machine learning library because it provides a wide range of state-of-the-art algorithms for supervised and unsupervised learning. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is well-documented, easy to use, and has a consistent API that simplifies the integration of different algorithms. Furthermore, there's a strong community and continuous development, which means it is well-maintained and updated regularly with new features and improvements.

Recommended for

  • Beginners learning machine learning concepts and application.
  • Data scientists and engineers looking for a robust and efficient toolkit to build and deploy machine learning models.
  • Researchers who need an easy-to-use library that facilitates the experimentation of various algorithms.
  • Developers who require a seamless, Python-based machine learning library that integrates well with other data analysis tools and environments.

Tableau Public videos

+ Add

Introduction to Tableau Public

More videos:

  • Tutorial - Introduction to Tableau Public | Tableau Public Tutorial | Edureka
  • Review - Tableau Desktop Vs Tableau Public | Tableau Training Videos | Tableau Certification - ExcelR

Scikit-learn videos

+ Add

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 Tableau Public and Scikit-learn)

Tableau Public

Scikit-learn

Business Intelligence
100 100%
Business Intelligence
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Office & Productivity
100 100%
Office & Productivity
0% 0
Data Science Tools
0 0%
Data Science Tools
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 Tableau Public and Scikit-learn

Tableau Public Reviews

27 dashboards you can easily display on your office screen with Airtame 2
By connecting and visualizing your data in a matter of minutes, Tableau Public can make you forget about the old-school spreadsheets and reports that used to clutter your life.
Source: airtame.com

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...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Tableau Public. It has been mentiond 40 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.

Tableau Public mentions (12)

  • Jobs for people who just want to crawl under their desks
    Now, if you do want to play around with data visualizations, you can do that for free with Tableau Public https://public.tableau.com/en-us/s/. Source: about 4 years ago
  • Data visualization
    Tableau Public - https://public.tableau.com/en-us/s/. Source: about 4 years ago
  • Weekly Entering & Transitioning Thread | 02 Jan 2022 - 09 Jan 2022
    Tableau and PowerBI are generally viewed as industry standard tools. The good news is you have free options for both! Tableau Public and PowerBI Desktop are what you're looking for. Try them out and pick which one you like more. The skills are pretty transferable once you master the basics. As far as python ML tools, ones that pop up pretty frequently are scikit-learn (questionable math notwithstanding), XGBoost,... Source: over 4 years ago
  • Foxhole Statistics
    I played with the idea of doing something similar and putting it all in Google Sheets (https://www.google.com/sheets/about/) so I could visualize it all in Tableau Public (https://public.tableau.com/en-us/s/). Source: over 4 years ago
  • Help on creating a weekly and monthly summary?
    I record everything in a spreadsheet and then built a set of dashboards using Tableau. Took a bit to get set up, but once I had it, I can produce the summary in a few minutes. Here's an example of my weekly summary. If you're interested, happy to send you the Tableau workbook so you can take a look. Source: over 4 years ago
View more

Scikit-learn mentions (40)

  • Detecting Ingress Tool Transfer (T1105) with Python
    Certutil.exe or notepad.exe opening an external connection lands in rare because, fleet-wide, those processes almost never egress. Tune the <= 3 threshold to your environment size. For a more principled version, score each (process, destination) pair by frequency and treat the long tail as the hunt queue, which is the same idea behind scikit-learn's rarity-based anomaly methods without the model overhead. - Source: dev.to / about 2 months ago
  • Best AI Cybersecurity Training for Security Teams: How to Pick
    Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / 2 months ago
  • Where to Get Hands-On AI Training for Cybersecurity Professionals
    Pre-configured environment. A good course ships a VM or container with Jupyter, pandas, scikit-learn, PyTorch or transformers, and realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. No setup tax. - Source: dev.to / 2 months ago
  • How Anomaly Detection Actually Works in Security Operations
    Isolation-based models: Build random decision trees that split features. Points that are isolated quickly (short average path length across trees) are anomalies. IsolationForest in scikit-learn implements this. Handles high-dimensional feature spaces without assuming a distribution. - Source: dev.to / 3 months ago
  • Building a Personalized Meal Recommendation System
    In practice, youโ€™ll want to use libraries (like scikit-learn or TensorFlow.js for more advanced modeling), but the principle remains: find what similar users enjoy, and use that as a basis for recommendations. - Source: dev.to / 5 months ago
View more

What are some alternatives?

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

KiniMetrix - Our approach blends proprietary metrics and frameworks, smart Business Intelligence software...

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

BrightGauge - BrightGauge is a business intelligence software for IT service providers.

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

Datamatic.io - Datamatic - WordPress for data visualizations

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

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