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Scikit-learn VS Daily Time Tracking

Compare Scikit-learn VS Daily Time Tracking and see what are their differences

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Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Daily Time Tracking logo Daily Time Tracking

Daily shows what you have been working on and for how long. It creates accurate timesheets by asking what you are doing, so no more timers or switching tasks. Use its data to submit your hours, create invoices or simply increase your productivity.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Daily Time Tracking Landing page
    Landing page //
    2020-10-30

Daily is a 5 star-rated time tracker for Mac that works by asking what you are working on. It provides a better way to track your daily activities without the hassle of toggling timers, switching tasks or taking notes. Use its accurate timesheets to submit your hours, create better invoices not missing any work or simply increase your productivity.

Underneath Daily’s user-friendly interface supporting both light and dark mode, you will find dozens of useful features. Examples include synchronisation via iCloud, automation using AppleScript, exporting to CSV, JSON and more, a tracking scheduler and system-wide keyboard shortcuts.

Try Daily for free by downloading it from the Mac App Store and join thousands of other employees, freelancers, founders and professionals.

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.

Daily Time Tracking features and specs

  • User-Friendly Interface
    Daily Time Tracking offers a simple and intuitive interface making it easy for users to navigate and log their time.
  • Detailed Reporting
    The platform provides comprehensive reporting features that allow users to analyze their productivity and time allocation.
  • Cross-Platform Compatibility
    It supports multiple platforms including web, iOS, and Android, enabling users to track time on the go.
  • Integration with Other Tools
    Daily Time Tracking integrates with popular productivity tools such as Asana, Trello, and Slack, enhancing its utility.
  • Customizable Settings
    Users can customize settings to suit their specific workflow requirements, including creating custom task categories and labels.

Possible disadvantages of Daily Time Tracking

  • Subscription Costs
    The platform requires a subscription, which may be a barrier for individual users or small teams with limited budgets.
  • Learning Curve
    Despite its user-friendly design, there is still a learning curve for users who are not familiar with time-tracking tools.
  • Limited Offline Functionality
    The app requires an internet connection for most features, which can be limiting in areas with poor connectivity.
  • Potential for Overhead
    Constantly logging time can become an administrative overhead, detracting from actual productive work.
  • Data Security Concerns
    Storing time-tracking data on a third-party service may raise concerns about data privacy and security for some users.

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.

Daily Time Tracking videos

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Daily Time Tracking

Category Popularity

0-100% (relative to Scikit-learn and Daily Time Tracking)

Scikit-learn

Daily Time Tracking

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Productivity
0 0%
Productivity
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Time Tracking
0 0%
Time Tracking
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 Scikit-learn and Daily Time Tracking

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

Daily Time Tracking Reviews

We have no reviews of Daily Time Tracking yet.
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Social recommendations and mentions

Based on our record, Daily Time Tracking should be more popular than Scikit-learn. It has been mentiond 56 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.

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 / 3 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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Daily Time Tracking mentions (56)

  • Time Tracking
    Check out Daily if you don't like manually toggling timers. Instead, it periodically asks what you are doing. Source: almost 2 years ago
  • "Blind" Time-tracker idea
    Just for an app reference, a quick google reference I found this https://dailytimetracking.com not sure if this helps, but seems pretty simple and not intrusive/invasive. Source: almost 2 years ago
  • Add work log for another user via API
    I'm the developer behind a time-tracking app, and I'm looking to build a Zapier integration for a larger customer who uses Jira. They want tracked time to automatically be pushed to Jira using their work log capability. They want to avoid using a (way more expensive) organization plan of Zapier, though. Source: almost 2 years ago
  • Looking for a good time tracking app with lots of statistics and graphs
    If you're on a Mac, you might want to try out DailyTry out Daily if you're on a Mac. Although it focuses more on simplicity, you might like its way of tracking time: by periodically asking what you are doing. For other options, check out this blog post. Source: about 2 years ago
  • Time tracker free
    Not free, unfortunately, but check out Daily. It tracks time by periodically asking what you are doing instead of requiring you to toggle timers when you switch tasks. Alternatively, check out this blog post for other options. Source: about 2 years ago
View more

What are some alternatives?

When comparing Scikit-learn and Daily Time Tracking, you can also consider the following products

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

Zoom - Equip your team with tools designed to collaborate, connect, and engage with teammates and customers, no matter where you’re located, all in one platform.

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

GoToMeeting - GoToMeeting is a web conferencing service offering a range of services which are available on Mac, PC, iOS and Android devices.

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

join.me - Instant screen sharing. Instant Aha!

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