How can you work with experts in different data science frameworks?

When I first delved into data science, I was overwhelmed by the sheer variety of frameworks available. I realized that understanding the basics of these frameworks was crucial to avoid confusion and make informed choices. Learning the basics of frameworks like TensorFlow and Scikit-learn allowed me to appreciate how they simplify complex tasks and streamline the development process. For beginners, I highly recommend starting with online tutorials and courses that explain what frameworks are and why they matter in data science.

I agree, to add on some of the widely used data science frameworks are - 1. pandas: used for data manipulation and analysis, providing data structures like DataFrames. 2. NumPy: package for numerical computations in Python supporting arrays and mathematical functions. 3. scikit-learn: ML library for classification, regression, clustering, and more. 4. TensorFlow: open-source ML framework for deep learning and neural networks. 5. PyTorch: deep learning framework with dynamic computation graphs, popular for research and prototyping. 6. NLTK: library for NLP and text processing in Python 7. spaCy: library for NLP that is designed for production use and provides fast and efficient text processing

Get the experts to work as follows: The Story teller The Artist The Number cruncher The Objection Handler The Negotiator And we should be home.

Gaining insight into the specific scenarios in which a particular framework is applicable serves as a key factor. This understanding significantly expedites the process of understanding the underlying implementation of the given framework.

More often than not, people try to learn multiple frameworks without actually realising exactly what the framework is trying to achieve. Frameworks do avoid reinventing the wheel, but you should understand what a wheel is before hand! So my advise would be to have a sharp image of what it is that you are trying to achieve, focus on the mathematics behind it, and try to understand the algorithms/ paradigms related to it. This would help in discussing abstract ideas across different fields and would allow you to communicate and work with broader range of experts.

One specific framework I had to learn was PyTorch for deep learning projects. Initially, I found it challenging, but by focusing on core concepts like tensors, autograd, and neural network modules, I was able to grasp the essentials. Overcoming the learning curve of a new framework often involves patience and practice. Reading official documentation and experimenting with small projects can be incredibly beneficial. A helpful tip I can share is to build a simple project using the new framework as a way to consolidate your understanding of its core components.

The main agenda is not to memorize every single function available. Instead, we should be able to understand the docstring provided to use the function or feature effectively to bring out the best outcome. Also a strong foundation would always help in developing a better outcome.

Understanding the fundamentals of the data science framework you are working with is crucial. This entails gaining knowledge about its core concepts, features, and syntax, as well as learning how to install, configure, and execute it. Online tutorials, documentation, books, and courses are valuable resources for acquiring this essential knowledge. While you don't need to become an expert in every intricate detail, having a grasp of the framework's code logic and structure is important for effective collaboration and contribution to projects.

"familiarize yourself" is the key here. "Familiarize," don't try to "memorize". For example, instead of trying to implement your own train-test split function, you should know that scikit-learn provides this out of the box. Once you know that a helper function exists, the usage syntax is just a search (or if you are lucky, a docstring) away. P.S.: I still copy-paste sklearn's train_test_split()'s syntax from its examples in it's docstring.. shhhh ??

Exactly to the point, whenever learning a framework in a rush (assuming you've the underlying knowledge of what you are trying to achieve) the best way would be quickly go through few examples in the documentation or some youtube tutorial example. This would easily tell you the 80-90% of the basic syntax and configurations needed. However, the ideal way would be to gloss over the documentation or follow a standard tutorial or book.

From my experience, sticking to the fundamentals never fails - establish the problem clearly and communicate it effectively. Solutions often arise organically within cooperative environments as the "How". Possessing a variety of frameworks can sometimes stimulate diverse solutions due to differing perspectives. However, adhering to the fundamentals and accurately modeling the problem assists the team in opting for the most efficient solution. As a rule of thumb, it is preferable to use tried and tested tools within teams when working on machine learning projects.

Clear communication is essential when collaborating with experts from different framework backgrounds. I recall an instance where I had to work with a colleague well-versed in Keras while I was more comfortable with PyTorch. We made it a point to discuss our expertise levels openly and ask questions when needed. Documenting code and providing clear comments and explanations can be a lifesaver. It ensures that both you and your collaborators can easily understand and build upon each other's work. When working with experts from diverse backgrounds, I've found that using plain language and avoiding jargon makes discussions more productive and less prone to misunderstandings.

Clear and respectful communication is vital when collaborating with experts in data science frameworks. It is important to be transparent about your expertise level, seek assistance when needed, and provide honest feedback. Clearly articulate your goals, expectations, and assumptions while actively listening to the perspectives and suggestions of others. Avoid technical jargon and use common terminology to enhance comprehension. Documentation through code comments, annotations, and diagrams facilitates understanding and sharing of information.

I believe that during the project a scrum is needed. However , there is sneed to work separately with different parties : working with devops is often a continuous work, verifying that the Environments work well to, access to data and others . Working with the LOB focuses on the model results and the ongoing insights . This requires scrum manners . Namely 2-3 days meeting . I worked once with an overseas team and we simply set weeklies .I believe that such scrums prevent deviations and preserve the project‘s relevance

Adding short yet crisp commands and simple as well as frequently used functions rather than complex ones that are rarely used would definitely help. Indeed optimised short crisp code is the key.

In collaborative data science projects, version control systems like Git have been indispensable. They enable seamless collaboration by tracking changes and facilitating easy integration of code from various sources. Cloud platforms like Google Colab have made it incredibly convenient to share code and collaborate without worrying about compatibility issues. They've been particularly useful when working on projects with team members who use different local setups. For code editing, tools like Jupyter Notebook have been my go-to, thanks to their support for multiple languages and frameworks, which simplifies working with experts who prefer different environments.

With three major operating systems, four currently supported versions of Python, and countless versions of dependencies possible, making sure that a piece of code works as expected on any setup it might be run on is of utmost importance. At neptune.ai, we test all our public examples on Unix, Windows, and MacOS running Python 3.8-3.11 and using the latest versions of all dependencies. Some tests fail, that is inevitable, but the point is to ensure maximum compatibility. With Google Colab now running Python 3.10, if you host code on Colab, make sure it runs well on Python 3.10 in a Unix environment.

I recently faced a decision between using Apache Spark and Dask for distributed data processing in a big data project. By comparing their performance, scalability, and ease of use, I could make an informed choice that best suited our project's needs. Experimentation and benchmarking have been crucial in determining which framework performs better under specific conditions. It's a valuable approach to avoid costly mistakes in production. Remember that the "best" framework can vary depending on the context, so discussing the pros and cons with your collaborators can lead to more informed decisions.

Data Scientists / Analysts are learners at heart. For me Exchanging ideas (written or verbal) and comparing them against my peers has been a really 'fun' way to learn and collaborate. .. Sometimes, its heartening to see different perspectives on the same problem, leading to similar/different solutions with similar/different interpretations. - Appreciating the differences, yet understanding the value of these differences is where learning lies.

In my experience, one of the most accessible ways to acquire new skills is by enrolling in online courses that offer professional certificates. These courses are not only high in quality but are also cost-effective and designed to accommodate a busy schedule. I have had an exceptional experience with Coursera, where I earned my IBM Data Science Professional Certificate and the AWS/DeepLearning AI certificate on Large Language Models. Both certificates have been invaluable in my professional development.

For students and professionals preparing to enter the data science field, the most valuable path to gain hands-on experience with experts in various data science frameworks is through engaging with open-source projects on GitHub or participating in Google Summer of Code (GSOC) in 2024. These platforms provide incredible opportunities to become an active part of the data science community, collaborate on projects, and join discussions. This immersive experience is unmatched in its ability to help you build your skills and make meaningful contributions. Be sure to keep an eye out for GSOC 2024 application openings; it could be your gateway to a remarkable learning journey in data science.

Very simplistically put, but I see a theme. The fundamental requisite is to get groups buy in before consensus. If consensus before buy in, amongst modalities, then when the outcomes are scrutinized against the actual, it eventually fails to support the model and goal.