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InsightSquared VS NumPy

Compare InsightSquared VS NumPy and see what are their differences

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InsightSquared logo InsightSquared

#1 for Salesforce.com Pipeline forecasting, profitability analysis, activity tracking: all the small business intelligence you need. Works using CRM data and automatic syncing.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • InsightSquared Landing page
    Landing page //
    2021-09-29

  learn.insightsquared.comSoftware by InsightSquared

  • NumPy Landing page
    Landing page //
    2023-05-13

InsightSquared features and specs

  • User-Friendly Interface
    InsightSquared offers an easy-to-navigate dashboard with customizable reports and visualizations, making it accessible for users with varying levels of technical expertise.
  • Comprehensive Analytics
    Provides in-depth sales analytics and KPIs that help sales teams track performance and make data-driven decisions effectively.
  • Integration Capabilities
    Can be easily integrated with popular CRM solutions like Salesforce, ensuring seamless data flow and unified analytics.
  • Customer Support
    InsightSquared is known for its excellent customer support, offering timely assistance and resources for troubleshooting.

Possible disadvantages of InsightSquared

  • Cost
    The platform may be cost-prohibitive for small businesses or startups, making it more suited for mid-size to large enterprises.
  • Learning Curve
    Despite the user-friendly interface, there can be a learning curve for new users to fully utilize all the advanced features and capabilities.
  • Customization Limitations
    While the tool offers many built-in reports, users might find limitations in customizing certain aspects to fit extremely specific organizational needs.
  • Integration Issues
    Occasional issues may arise with data syncing from specific CRM systems or other integrations, necessitating manual adjustments.

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 InsightSquared

Overall verdict

  • InsightSquared is considered a valuable tool for businesses looking for robust sales analytics and performance management solutions.

Why this product is good

  • Comprehensive Analytics: InsightSquared offers deep insights into sales data, helping businesses make informed decisions.
  • User-Friendly Interface: The platform is designed with a user-friendly dashboard that is easy to navigate, even for users who may not be tech-savvy.
  • Integrations: It integrates well with popular CRM systems like Salesforce, allowing for seamless data synchronization.
  • Customizable Reports: Users can create bespoke reports tailored to their specific needs and business goals.
  • Real-Time Data: Provides up-to-the-minute data, enabling businesses to react swiftly to changing conditions and opportunities.

Recommended for

  • Small to Medium Businesses (SMBs) looking to optimize their sales processes.
  • Companies that rely on Salesforce and want to enhance their CRM with powerful analytics.
  • Sales managers who need detailed, actionable insights into team performance.
  • Businesses looking for a cost-effective analytics solution compared to full-scale business intelligence platforms.
  • Organizations aiming to improve forecasting accuracy and sales pipeline management.

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.

InsightSquared videos

+ Add

InsightSquared Customer Webinar - A Sales VP's Guide to the Quarterly Business Review

More videos:

  • Review - Nate G. Reviews His Experience Using InsightSquared
  • Review - What is InsightSquared?

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 InsightSquared and NumPy)

InsightSquared

NumPy

Business Intelligence
100 100%
Business Intelligence
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Data Dashboard
60 60%
Data Dashboard
40% 40
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 InsightSquared and NumPy

InsightSquared Reviews

8 Alternatives to Apache Superset That’ll Empower Start-ups and Small Businesses with BI
Apache Superset can hold a vast universe of SQL data to fit their users' needs. InsightSquared is a revenue intelligence specialised business intelligence tool. Therefore, InsightSquared integrations are limited to that purpose.
Source: trevor.io

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.

InsightSquared mentions (0)

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

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 / 4 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 / 8 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 / 9 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 InsightSquared and NumPy, 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.

Sisense - The BI & Dashboard Software to handle multiple, large data sets.

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

Clari - Clari is a predictive analytics platform for sales people.

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

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