Gyana VS Data Science from Scratch

Compare Gyana VS Data Science from Scratch and see what are their differences

Umso

The Website Builder for Startups featured

Contents:

» Base Details
» Videos
» Reviews
» Alternatives

Gyana

Intuitive easy-to-use report and dashboard tool to stop wasting time on repetitive and tedious tasks.

Data Science from Scratch

Data Science and Python, starting at zero

Landing page //
2022-10-14

Gyana is a no-code data science startup headquartered in London. Their flagship platform enables any business user to become a citizen data scientist with a few clicks-instead of training on hours of coding. Gyana is co-founded by Oxford graduates David Kell and Joyeeta Das and backed by tech leaders such as Biz Stone (co-founder of Twitter). Since their inception in 2016, they remain passionate about democratizing access to data science and have won many awards for pushing data-driven culture forward by participating in critical global projects."

Landing page //
2019-07-07

Gyana

Website: gyana.com
Pricing URL: Official Gyana Pricing
$ Details: paid Free Trial £39.0 / Monthly (1 user, 1,000,000 rows of data, unlimited features and updates)
Platforms: Web Browser Google Chrome
Release Date: 2016 June

Edit details

Data Science from Scratch

Website: joelgrus.com
Pricing URL: -
$ Details: -
Platforms: -
Release Date: -

Edit details

Gyana features and specs

User-friendly Interface
Gyana provides a simple and intuitive interface that makes it accessible for users without technical backgrounds to create visualizations and analyze data.
No-code Platform
As a no-code data analytics tool, Gyana allows users to perform complex data operations without writing any code.
Customizable Dashboards
Users can create personalized and interactive dashboards that cater to specific business needs and display real-time data insights.
Integration Capabilities
Gyana can connect with multiple data sources, enabling users to import and consolidate data from various platforms easily.
Collaboration Features
The platform offers collaborative features that allow teams to work together on data projects, share insights, and improve efficiency.

Possible disadvantages of Gyana

Limited Advanced Features
While Gyana is excellent for basic to intermediate analytics, it may lack some advanced features that professionals in data science might require.
Pricing Structure
Depending on the needs of a business, the cost associated with subscribing to Gyana might not be budget-friendly for smaller organizations.
Learning Curve
Despite being a no-code platform, some users may still experience a learning curve when adapting to its features and functionality.
Scalability Issues
The platform might encounter challenges when handling large datasets, which can be a limitation for enterprises requiring extensive data processing.
Dependence on Internet Connectivity
Cloud-based operation means users need a stable internet connection to access and use the platform effectively, which might not always be feasible in remote areas.

Data Science from Scratch features and specs

Hands-On Learning
The book encourages a practical approach to learning data science by implementing algorithms and concepts from scratch, helping readers understand the underlying mechanics.
Comprehensive Coverage
It covers a wide range of fundamental topics in data science such as statistics, data visualization, linear algebra, and machine learning, providing a solid foundation.
Python-Based
Since the book is centered around Python, a popular programming language in data science, it is accessible to a large audience already familiar with Python.
Developer-Friendly
The content is ideal for developers looking to transition into data science, as it focuses on programming and algorithmic aspects of data science.

Possible disadvantages of Data Science from Scratch

Steep Learning Curve
Beginners may find the approach challenging if they do not have prior programming experience in Python or understanding of mathematical concepts.
Lack of Real-World Applications
The focus on building from scratch may lack the practical application perspective and real-world examples that some learners might seek.
Outdated Information
As data science is a rapidly evolving field, some methodologies, tools, or libraries discussed might be outdated or less common in the industry today.
Less Emphasis on Tools
The book emphasizes building concepts from scratch over familiarizing readers with powerful existing data science libraries and tools like TensorFlow or PyTorch.