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WorkTango VS NumPy

Compare WorkTango VS NumPy and see what are their differences

Atlas HXM
Atlas HXM is the largest Direct Employer of Record (EOR) technology platform, supported by experts, that delivers flexibility for companies to expand across borders, onboard talent, manage compliance, and pay their global workforce. featured
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WorkTango logo WorkTango

WorkTango is a platform that enables you to get access to the power of genuine employee feedback.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • WorkTango Landing page
    Landing page //
    2023-10-09
  • NumPy Landing page
    Landing page //
    2023-05-13

WorkTango features and specs

  • Comprehensive Employee Engagement
    WorkTango offers a robust platform for gauging employee engagement through surveys and feedback, providing organizations with meaningful insights to improve workplace culture.
  • Real-Time Feedback
    The platform enables real-time feedback mechanisms, allowing employees to express concerns or share ideas promptly, fostering a responsive and adaptive work environment.
  • Data-Driven Insights
    WorkTango provides analytics and reporting tools that help organizations make data-driven decisions to enhance employee satisfaction and productivity.
  • Customization
    The platform offers customizable features that allow organizations to tailor surveys and feedback mechanisms to fit their specific needs and goals.
  • User-Friendly Interface
    WorkTango is designed with a user-friendly interface that makes navigation intuitive for both administrators and employees, ensuring widespread adoption across the organization.

Possible disadvantages of WorkTango

  • Cost
    For smaller businesses or startups, the cost of implementing WorkTango can be a consideration, as it may require a significant investment.
  • Complexity for Small Teams
    The comprehensive nature of the platform might be overwhelming or unnecessarily complex for small teams that do not require extensive engagement tracking.
  • Implementation Time
    Setting up and fully integrating WorkTango into an organization's workflow might require a significant amount of time and resources.
  • Training Requirements
    New users or administrators might require training to utilize the platform's full capabilities effectively, which could take additional time and resources.
  • Dependency on Digital Literacy
    The effectiveness of WorkTango can be hindered if employees lack digital literacy, as the platform is heavily reliant on digital interactions.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

WorkTango videos

+ Add

What is the Kazoo + WorkTango Employee Experience Platform?

More videos:

  • Review - WorkTango Webinar: So, You Think HR Owns Employee Engagement Think Again
  • Tutorial - WorkTango Webinar: How to Build a Best In Class Employee Engagement Program

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Category Popularity

0-100% (relative to WorkTango and NumPy)

WorkTango

NumPy

Business & Commerce
100 100%
Business & Commerce
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
HR
100 100%
HR
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using WorkTango and NumPy. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare WorkTango and NumPy

WorkTango Reviews

10 Workleap Competitors: Pricing & Reviews [2025 Guide]
WorkTango pricing: Pricing is not listed on the website. To learn more about pricing, you'll need to schedule a demo/sales call with WorkTango.
Source: matterapp.com
7+ Assembly Alternatives: Pricing & Reviews [2024 Guide]
About WorkTango: WorkTango is an employee engagement platform that offers tools for recognition, feedback, and surveys. The platform is designed to help companies gather real-time feedback, celebrate achievements, and improve employee engagement. WorkTango's customizable recognition programs and robust analytics make it a valuable tool for organizations looking to enhance...
Source: matterapp.com
15 Top Employee Recognition Platforms For Companies At Every Stage
While surveys and insights are a huge part of the platform, WorkTango also offers Recognition capabilities that incorporate points, tokens, and rewards as required. The Rewards Marketplace has local and global rewards, with automated fulfillment and zero markups.
Source: nectarhr.com

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 119 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.

WorkTango mentions (0)

We have not tracked any mentions of WorkTango yet. Tracking of WorkTango recommendations started around Apr 2022.

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 5 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 9 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 9 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 10 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 10 months ago
View more

What are some alternatives?

When comparing WorkTango and NumPy, you can also consider the following products

Hello Astra - Hello Astra is an Applicant Tracking System that leverages AI technology to help Hiring Managers with the recruitment process.

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

EVA-REC - EVA-REC is a state-of-the-art hiring platform that enables you to recruit and hire in a smarter and faster way.

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

PCRecruiter - PCRecruiter is a powerful, flexible, affordable web-based system for recruiting, sourcing, and placement professionals of any business size.

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

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