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QA VS Deepnote

Compare QA VS Deepnote and see what are their differences

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QA logo QA

QA is a training course platform that enables users to get the right amount of training for the right skills.

Deepnote logo Deepnote

A collaboration platform for data scientists
  • QA Landing page
    Landing page //
    2023-09-09
  • Deepnote Landing page
    Landing page //
    2023-10-09

QA videos

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Lesson 1 (October 25 batch) - QA Career & Job Market Overview. Understanding of QA & testing

More videos:

  • Review - QA Analyst Expectations vs Reality | Work Day In My Life
  • Review - How I do a front-end dev QA review

Deepnote videos

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Could this be the Best Data Science Notebook? (Deepnote)

Category Popularity

0-100% (relative to QA and Deepnote)

QA

Deepnote

Education
100 100%
Education
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Online Courses
100 100%
Online Courses
0% 0
AI
0 0%
AI
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 QA and Deepnote

QA Reviews

We have no reviews of QA yet.
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Deepnote Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Deepnote is a cloud-based data science notebook platform comparable to Jupyter Notebooks but with a focus on real-time collaboration and editing. It lets users write and run code in several programming languages, as well as include text, equations, and visualizations in a single document.
Source: lakefs.io
7 best Colab alternatives in 2023
Deepnote is a real-time collaborative notebook. It offers features like real-time collaboration, version control, and smart autocomplete. It also provides direct integrations with popular data sources like GitHub, Google Drive, and BigQuery. Its modern, intuitive interface makes it a compelling choice for both beginners and experienced data scientists.
Source: deepnote.com
12 Best Jupyter Notebook Alternatives [2023] – Features, pros & cons, pricing
Deepnote is a cloud-based, data science notebook platform that is similar to Jupyter Notebooks, but with a focus on collaboration and real-time editing. It allows users to write and execute code in a variety of programming languages, as well as include text, equations, and visualizations in a single document. Deepnote also has a built-in code editor and supports a wide range...
Source: noteable.io
The Best ML Notebooks And Infrastructure Tools For Data Scientists
A Jupyter-notebook enabled platform, Deepnote boasts of many advanced features. Deepnote supports real-time collaboration to discuss and debug the code. The platform will soon have functions such as versioning, code review, and reproducibility. Deepnote has intelligent features to quickly browse the code, find patterns in your data, and autocomplete code. It can integrate...
Source: analyticsindiamag.com

Social recommendations and mentions

Based on our record, Deepnote seems to be a lot more popular than QA. While we know about 32 links to Deepnote, we've tracked only 2 mentions of QA. 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.

QA mentions (2)

  • Advice about IT support/helpdesk apprenticeships in London?
    Got Es on all my A lvls and I don't think uni is the right route for me currently. IT has always been my main interest, not so much coding/software dev but other sides of it (will hopefully get narrowed down once I join workplace). IT helpdesk/support apprenticeships are the ones I'm looking at currently, I've found plenty and am about to apply to a bank which has an 'It Support Apprenticeship' role. Is there... Source: over 1 year ago
  • Taking 40% pay cut. No longer chasing money
    I know the best people we’ve hired all came from Https://qa.com. Source: about 3 years ago

Deepnote mentions (32)

  • A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev
    Deepnote - A new data science notebook. Jupyter is compatible with real-time collaboration and running in the cloud. The free tier includes unlimited personal projects, up to 750 hours of standard hardware, and teams with up to 3 editors. - Source: dev.to / 3 months ago
  • JupyterLab 4.0
    We looked into many of these issues with Deepnote (YC S19) [https://deepnote.com/]. What we found is that these are not necessarily problems of the underlying medium (a notebook), but more of the specific implementation (Jupyter). We've seen a lot of progress in the Jupyter ecosystem, but unfortunately almost none in the areas you mentioned. - Source: Hacker News / 11 months ago
  • Ask HN: Fastest way to turn a Jupyter notebook into a website these days?
    Upload your ipynb to Deepnote and publish as an app. That simple. https://deepnote.com. - Source: Hacker News / about 1 year ago
  • Quick tip: Using a SingleStoreDB Recursive CTE with London Underground data
    Using Deepnote, we'll create a Python notebook and upload the two GeoJSON files into a data directory. - Source: dev.to / over 1 year ago
  • free-for.dev
    Deepnote - A new kind of data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud. Free tier includes unlimited personal projects, up to 750 hours of standard hardware and teams with up to 3 editors. - Source: dev.to / over 1 year ago
View more

What are some alternatives?

When comparing QA and Deepnote, you can also consider the following products

Firebrand - Firebrand is a platform that allows users to learn different courses online and get certifications from it.

Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.

Stormwind - Stormwind is an online platform that offers online training courses to users on different software.

Apache Zeppelin - A web-based notebook that enables interactive data analytics.

SpringBoard - SpringBoard is a video content management and analytics platform.

Saturn Cloud - ML in the cloud. Loved by Data Scientists, Control for IT. Advance your business's ML capabilities through the entire experiment tracking lifecycle. Available on multiple clouds: AWS, Azure, GCP, and OCI.

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