Reviewing the Top Data Science Platforms of 2024

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Hi all! It’s

Non-Brand Data

and

DataBites by Josep

.

We are currently collaborating to create this article to review the top data science platform in 2024. We combine our experiences and opinions to produce a piece that helps everyone. So, let’s go!

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Anaconda

I am sure that Anaconda facilitates many data scientists' first data science platforms. For those who don’t know what Anaconda is, it is a data science platform that provides an open-source distribution of Python that offers a complete suite of tools and libraries, facilitating seamless package management and deployment. There is an enterprise edition, but the free one already works well.

Cornellius’ opinion: 

• What I like about Anaconda is that it includes over 1500 pre-installed data science packages. 

• It also has Jupyter Notebooks installed and integrates well with various IDEs, such as JupyterLab, Jupyter Notebook, Spyder, and PyCharm. 

• Its main disadvantage is that it has many pre-installed packages and an IDE so it could be resource-intensive, and it has a higher learning curve. 

• An alternative version of Anaconda called Miniconda might work well for smaller projects.

Josep’s Opinion

• Its ability to create and manage virtual environments allows users to isolate project dependencies and avoid conflicts smoothly. 

• It supports the most used data science and ML libraries, like TensorFlow or scikit-learn.

• While Anaconda is extremely useful, exploring other tools is also beneficial. As a data scientist who began with Anaconda, I found it challenging to transition to different platforms. 

Google Colab

Google Colab is a game-changer for collaborative data science and machine learning projects. If you regularly use Jupyter Notebook, you will love this! It is a cloud-based notebook environment that requires no setup and is accessible from anywhere with an internet connection. 

Cornellius’ opinion: 

• What I like about Google Colab is that It provides free access to GPUs and TPUs, which can be used for heavy-lifting tasks. 

• The platform is also integrated with Google services such as Google Drive, making file management and collaboration easier. 

• The weakness is that the Google Colab Free-tier sessions are time-limited and often can’t last longer than 12 hours. 

• During peak times, access to GPUs and TPUs can also be limited if you do not have premium access.

Josep’s opinion: 

• It allows multiple users to work on the same Jupyter Notebook simultaneously. This was life-saving for me, especially for university work, where multiple students had to code in a single notebook! 

• The platform provides free access to GPUs and TPUs, which is great for training complex ML models. 

• It supports seamless integration with Google Drive, making saving and sharing your work easy. 

• Its free tiers do have a limit, especially during peak hours. 

Hugging Face

Hugging Face has emerged as a major player in the NLP and AI community, allowing the deployment of language models and pre-trained models. It has become a popular hosting place where open-source research is discussed, as it provides access to state-of-the-art pre-trained models. The platform’s Transformers library offers an extensive collection of pre-trained models (more than 120k!!), such as BERT, GPT-3, and Llama, which can be fine-tuned for specific tasks with minimal effort. 

Cornellius’ opinion: 

• HuggingFace can be user-friendly for beginners as it provides extensive documentation and tutorials, even for those new to NLP and ML. 

• The platform has an active community that contributes models and datasets, making it an excellent place for learning. 

• HuggingFace is not only a place for hosting, but it also supports a spaces feature that allows users to create and share model demos and applications.

• HuggingFace is primarily focused on NLP, so it may not cover other domains that much. It also has many features but often depends on cloud services, which may incur costs.

Josep’s opinion: 

• It presents a datasets library with over 20k datasets. 

• What I like the most about is how it has simplified any model's fine-tuning process, giving us all the tools required to do so. 

• Its interface is super easy to use, enabling sharing and discovery of models and datasets. 

• The platform’s APIs simplify the integration of NLP capabilities into applications.

• The company is committed to improving the state-of-the-art open-source models and presents an active community. 

Kaggle Notebooks

Kaggle is one of the most famous platforms for data professionals. It offers all types of data for our projects and is usually the first place to check a specific dataset. It also allows users to participate in competitions and presents a tiered progression system. Kaggle became even better when they introduced the Kaggle Notebooks.

Cornellius’ opinion: 

• Kaggle Notebooks is similar to Jupyter Notebooks but is integrated with the Kaggle platform and also provides access to datasets directly from the notebook. 

• Kaggle also offers free access to GPUs and TPUs for hardware acceleration, making it a popular place for users to work. 

• The free-tier sessions on Kaggle Notebooks are limited and often restrictive for long-running tasks. During high-demand periods, access to GPUs and TPUs can also be limited.

Josep’s opinion: 

• It offers a cloud-based Jupyter Notebook interface and an interactive and versatile coding, visualization, and collaboration environment. 

• Users can explore vast datasets, fostering knowledge exchange and innovation.

• The platform’s integration with popular libraries makes it a practical tool for exploratory data analysis, feature engineering, and model building. 

• Kaggle’s community-driven approach enriches the learning experience, providing valuable insights and inspiration.

KNIME

KNIME (Konstanz Information Miner) is an open-source data analytics platform. It’s probably different from the previously reviewed platform, as it uses a GUI that allows users to create workflows by drag-and-drop. It stands out with its intuitive graphical interface, making it accessible for users with minimal programming experience while still being powerful for advanced users. This is definitely one interesting trade-off!

Cornellius’ opinion: 

• The platform is designed to create and produce data science workflows quickly. 

• It provides many pre-built menus for various data science tasks that support multiple data sources and formats. 

• As a data platform, the KNIME enterprise users also offer workflow automation, scheduling, and collaboration capabilities.

• KNIME's weakness is its versatility and challenges as case complexity increases. Building and managing complex cases might require a deeper understanding of the platform. 

Josep’s opinion: 

• The node-based workflow design allows users to construct data processing pipelines visually, simplifying complex data manipulations and analytics. 

• Its extensive library of nodes covers a wide range of functionalities, from data preprocessing to ML and deployment. 

• The platform supports integration with several data sources and tools, such as SQL databases, Python, R, and big data technologies like Hadoop and Spark. 

• KNIME's ability to create modular and reusable components enhances productivity and maintainability, making it an excellent choice for scalable data workflows.

• My only concern is that the tool is limited to its graphical interface, which sometimes lacks flexibility and makes it difficult to keep up with the latest developments.

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Conclusion

There are so many free data science platform tools to learn and use. In this newsletter, we try to summarize our top 5 data science platforms for you to use.

The data science platforms we recommend are:

• Anaconda

• Google Colab

• HuggingFace

• Kaggle Notebooks

• KNIME