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Excel Dashboard School VS Scikit-learn

Compare Excel Dashboard School VS Scikit-learn and see what are their differences

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Excel Dashboard School logo Excel Dashboard School

Free Excel add-ins and tools on Excel Dashboard School. Boost your work productivity and save your time! No trials, 100% power!

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Excel Dashboard School Landing page
    Landing page //
    2023-09-24

Text Tools and Smart Utilities! Text processing and string manipulations in Excel are not an easy tasks but we’ll help you to simplify string related functions using our add-in.

Feature list:

  • Convert text to uppercase, apply Excel lowercase to your data or convert your text values to proper / title case
  • Delete first or last character from a selected string
  • Delete x characters starting at the nth position
  • Remove spaces, non-printable characters, all line breaks, initial apostrophes
  • Insert text before first, after last character
  • Insert text starting at nth position and much more….
  • Calendar. Quick dates entry to save your time.
  • Drop-down list creator: Create drop-down list in selected cells in seconds
  • Spreadsheet Tools – Effective management for Ranges
  • Data Collector: Consolidate Multiple Worksheets into one Workbook
  • Split Ranges: Split one data table into several different sheets based on the values of the specified column or other criteria.
  • Compare Ranges: Compare two columns or ranges and the output or highlighting of coincidences / differences between them.
  • Clean Ranges: Delete text, formats, notes, hyperlinks, conditional formatting, etc. in selected cells.
  • Screenshot Manager: Create screenshot of selected range and export it into various formats.
  • WorkSheet Manager
  • Perform vary operations (adding, sorting, protecting, etc.) on sheets in current workbook.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Excel Dashboard School features and specs

  • Comprehensive Learning Resources
    Excel Dashboard School provides a wide range of learning materials, including tutorials, templates, and examples, catering to different skill levels.
  • High-Quality Templates
    The site offers professionally designed templates that are ready to use, saving users time and ensuring a polished appearance.
  • Step-by-Step Tutorials
    Detailed, easy-to-follow tutorials help users build dashboards from scratch, making it easier for beginners to learn.
  • Regular Updates
    The platform frequently updates its content, ensuring users have access to the latest techniques and best practices in dashboard creation.
  • Community Support
    Excel Dashboard School has a community of users where members can share tips, ask questions, and offer support to each other.

Possible disadvantages of Excel Dashboard School

  • Cost
    Some of the resources and templates are behind a paywall, which may not be affordable for all users.
  • Complexity
    Even with tutorials, some users might find advanced dashboard features and functions quite complex and challenging to implement.
  • Limited Free Content
    Although there is free content available, it may be insufficient for users who need comprehensive or specific solutions without purchasing premium resources.
  • User Interface
    The website's interface and navigation could be more intuitive, potentially making it difficult for users to find specific resources or tutorials.
  • Excel Dependency
    Resources and tutorials are primarily focused on Microsoft Excel, limiting applicability for users who prefer or require alternative data analysis tools.

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 Excel Dashboard School

Overall verdict

  • Excel Dashboard School is generally a good resource for learning how to create dashboards in Excel.

Why this product is good

  • It provides a variety of tutorials, templates, and step-by-step guides which are beneficial for both beginners and advanced users looking to enhance their skills in Excel dashboard creation. The resources are practical and focused on real-world scenarios, making it easier for learners to apply their knowledge effectively.

Recommended for

  • Business professionals who need to create detailed reports and dashboards regularly.
  • Data analysts who want to improve their report presentation using Excel.
  • Students seeking to improve their Excel skills for career advancement.
  • Anyone interested in learning how to communicate data insights more effectively through visual representations.

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.

Excel Dashboard School videos

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Scikit-learn videos

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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 Excel Dashboard School and Scikit-learn)

Excel Dashboard School

Scikit-learn

Data Dashboard
58 58%
Data Dashboard
42% 42
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Technical Computing
100 100%
Technical Computing
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 Excel Dashboard School and Scikit-learn

Excel Dashboard School Reviews

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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 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.

Excel Dashboard School mentions (0)

We have not tracked any mentions of Excel Dashboard School yet. Tracking of Excel Dashboard School 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 / 6 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 / 12 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 / over 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 Excel Dashboard School and Scikit-learn, you can also consider the following products

Kutools for Excel - A handy Microsoft Excel add-ins collection to free you from time-consuming operations.

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

KPI Dashboard in Excel - Professional Management KPI Dashboard. Includes trend charts, past year/target comparisons, monthly & cumulative analysis in performance dashboard.

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

ASAP Utilities - ASAP Utilities is a powerful Excel add-in that fills the gaps in Excel.

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

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