Wie können Sie ein Data-Science-Framework verwenden, ohne Ihre Ergebnisse zu beeinträchtigen?

A perhaps unpopular opinion, but based on my experience, it's beneficial to spend the initial 1-2 weeks simply diving in and experimenting without adhering to specific frameworks. This hands-on phase helps you encounter early challenges, better grasp the task's nuances, and identify limitations. Once you have a deeper understanding, you'll be better positioned to choose a framework that suits you. The key is to strike a balance: have a plan, but don't get bogged down in over-planning.

Selecting an appropriate data science framework is pivotal for project success. The array of options like CRISP-DM, KDD, OSEMN, and TDSP offer distinct merits and demerits. Opt for a framework aligned with project scope, resources, team proficiencies, and objectives. Industry standards and domain best practices also hold weight. Yet, caution against blindly adhering; comprehend the underlying rationale and assumptions. Scrutinize each step's relevance and adapt as required. Flexibility and understanding should underpin framework utilization, fostering tailored and effective data science endeavors.

Selecting a data science framework tailored to your project's needs is crucial. Frameworks like CRISP-DM, KDD, OSEMN, and TDSP offer distinct benefits and drawbacks. Choose based on project scope, goals, resources, team capabilities, and industry standards. However, avoid blind adherence; grasp the framework's logic and assumptions. Assess each step's relevance and adapt them as necessary to ensure a tailored and effective approach aligned with your project's unique requirements.

I created my own framework! An expert at data mapping and multi-dimensional arrays does not look for outside solutions. Excel was not powerful enough early in my career to complete the output needed so I created my own version of Excel, amazingly it is only around 8 lines of code.

I agree with the perspective you've outlined. Choosing a suitable data science framework is essential, but it should not be a rigid, one-size-fits-all approach. It's important to consider several factors when selecting a framework for a data science project The choice of a data science framework should be a thoughtful decision that takes into account project-specific factors, team dynamics, industry standards, and flexibility. The goal is to use the framework as a guide to structure your work effectively while being adaptable enough to meet the unique needs of your project.

I feel that it's important to consult with subject matter experts before starting any installations for data collection. There should be complete clarity on what the end goal of the data collection is and appropriate data collection methodologies should be used. Subject matter experts include electrical experts for identifying sensing methodologies, collection site experts who are aware of on-site challenges, IT experts who can advise on data collection and storage methodologies so they are compatible with the organization's existing IT infrastructure. Also, experts on the physical processes being measured so the team can be sure that the data is representative of the process being measured and isn't affected by noise or other factors.

Data Science is an ever-evolving journey, and it’s fascinating how we all go through various stages. we all encounter stages where models shine in most areas but stumble on a few variables. Remember, models aren't absolutes; digging into the data often reveals crucial hidden metrics. Also, data quantity matters – without enough data for separation, results may not be optimal. Quality data is key. Embrace the iterative nature of data science, where each model refines your skills and unlocks the power of data.

Agreed that it is cyclical and the stages in the cycle can be changed and swapped out for alternates, as the project / product evolves. This is where documentation plays a huge role in listing out what works and doesn't for the data and domain, as part of the iterative exploration.

Iteration and refinement are essential in agile projects, and data science is no exception. Having said that, avoiding getting trapped in a cycle is crucial in data science. Frameworks provide clear objectives, ensuring the project doesn't stall in modeling or data analysis.

Indeed, a data science framework isn't linear but cyclical and iterative. Anticipate revisiting prior steps as understanding deepens. Refine problem statements, gather varied data, explore more, or experiment with models. Consistently validate outcomes using proper methods and metrics, and involve stakeholders for feedback. Document decisions, assumptions, and limitations. This adaptable approach ensures effective problem-solving, continuous improvement, and well-justified outcomes.

Data science frameworks should be used as versatile tools, adaptable to an organization's specific needs. They go beyond technical aspects, aiding in effective communication to non-technical stakeholders. Tailoring the framework to match the organization's structure and requirements is key.

In the exciting circle of data science frameworks, let's not forget our star attraction: human intuition. Frameworks give us a script, but they can't replace the improvisational flair that comes from years of experience, especially in the healthcare sector. As Data Scientists, we often find that the most profound insights arise when seasoned intuition plays jazz with a well-structured framework. Think of it as the spice that turns a good dish great-- too little and it's bland, too much and you've got a different problem. The secret sauce? A governance layer that vets the 'gut feelings' against the rigor of empirical evidence. In essence, let your framework be the rhythm section, but never stifle the solo!

Using a data science framework involves tailoring it to your context while preserving result integrity. Frameworks like CRISP-DM, KDD, OSEMN, and TDSP offer choices. Choose wisely based on project goals, skills, and industry standards. Avoid blind adherence; adapt steps as needed for a balanced approach. Iteration is key; the framework isn't linear but cyclical. Revisit and refine as understanding grows. Expect to redefine problems, gather new data, explore further, and validate results. Document decisions, assumptions, and limitations.

While data science is about methods and techniques, it is important to remember that this discipline's quality is directly related to scientists' ability to validate data at hand. In other words, having a strong domain knowledge is critical for an effective analysis just as much as it for a sucessful communication with others.

Data Scientists NEED to care more about ethics and social impact. Nobody is better positioned to fully understand the potential risks and yet most either don't give it much thought or don't care. The impact we have is extensive and only growing. It is time to start dedicating more consideration and energy to this issue as an industry. I think people assume that since data scientists are intelligent, they must be contemplating the impact of their products - and yet I've been at conferences where the AI Ethics talks were empty.

View a data science framework as an ongoing learning tool, not a definitive answer. Assess project performance through quantitative and qualitative metrics within the framework. Analyze feedback from stakeholders and users to identify strengths and weaknesses. This insight improves skills, methods, and generates ideas for future projects. Share lessons and practices with peers and the data science community, fostering growth and knowledge exchange. It's a continuous cycle of improvement and contribution.

The whole essence of data science is to learn and improve, what you build today, no matter how efficient it is, may turn out to be redundant soon if learning and improvisation is not at the core. Some suggestions, which can create difference in the learning if in place – 1. In the organization, must have a centralized & shared repository of the POCs, use cases etc. which are generally found to be to the point and can be helpful for anyone who might be starting with something similar 2. Though the trends tend to change, the fundamentals of the concepts mostly remain the same. Look in the previously developed model which will give you a good sense of the problem statement, a technique of how that was taken care of, a strong business concept

I would start with a value proposition. As a data science project could be endless if the primary goals are not set at the beginning. Then identify the data to be used, how could you feed them to be valuable, clean, and enough for the task. Define clearly whether is a classification, regression, clustering, or other deep learning project to be developed. Sometimes less is more, so start with a first approach with a small batch of data. Experiment with different types of framework or algorithms and finally decide which better suits for your goals or value proposition. You will never get all the answer in just one framework or methodology.

In the world of data science frameworks, let's not forget they are also mirrors reflecting our own biases and assumptions. I've come to realize that your framework is only as good as the diverse perspectives that scrutinize it. In healthcare, that's not just a 'nice-to-have,' it's mission-critical. As we evaluate performance and outcomes, we deliberately invite feedback from a broad swath of stakeholders—from clinicians to actuaries to patient advocates. Why? Because if your framework only understands the language of quants, it's basically that guy at a party who only talks about his fantasy football league. Valuable? Sure. The whole picture? Not by a long shot.

This brings to mind the transformative potential of AI in healthcare. Just as data quality matters in business, it’s equally crucial in the medical field. AI’s ability to analyze vast healthcare datasets can revolutionize patient care, diagnosis, and treatment planning. The future of healthcare looks promising with AI’s assistance, offering improved patient outcomes and more efficient healthcare delivery.

Data science is situated in a rapidly evolving landscape. New tools, techniques, and technologies emerge frequently. This dynamism underscores the importance of adaptability. While frameworks provide structure, staying open to adopting new methods and tools can enhance the efficacy of your data science projects. The ability to pivot and integrate the latest advancements while maintaining a structured framework can result in cutting-edge insights and solutions.

Here are a few strategies that I personally encourage: 👉Vision Alignment - Ensure your framework aligns with the overarching data vision, not just immediate goals. 👉Real-time Relevance - Embrace technologies that allow for timely data insights, enhancing decision-making speed. 👉Feedback Loops - Continuously refine based on data feedback, ensuring adaptability and precision. 👉Resource Optimization - Use data to direct resources effectively, maximizing ROI and minimizing wastage. 👉Collaborative Approach - Foster a culture where data insights are shared, debated, and refined collectively.

I would reiterate a point I made earlier - that Frameworks are for learning how to be a better data scientist, communicate effectively, and slow yourself down in order to avoid mistakes. However, they are not a goal in-and-of themselves and they should not be treated as such. In fact, the goal should be to learn how to do all these things WITHOUT a specific framework and if that's not working for whatever reason, use frameworks to help you figure out why ;)