What do you do if your Data Science project encounters a problem that needs solving?

O mapeamento dos problemas e possíveis impactos e requisitos necessários é o primeiro passo para definir a estratégia de solu??o. Após, esta etapa, o escopo de solu??es tende à se tornar mais claro e permitir uma execu??o dos adequados tratamentos corretivos.

If a Data Science project encounters a problem that needs to be solved: - Clearly identify the problem and its underlying causes. - Collaborate with colleagues and stakeholders to better understand the context and implications of the problem. - Use data analysis techniques to investigate and find potential solutions. - Try different approaches and adjust the process as needed. - Communicate results and proposed solutions clearly and effectively to interested parties. - Implement the identified solutions and evaluate their impact over time. - Be prepared to iterate on the process as new information and challenges arise.

Define the Problem: Clearly understand the problem. Have a proper vision of what needs to be solved. Review Existing Work: Review your data preprocessing steps, model selection, and any other relevant components of your project. This can help identify where the issue might have originated. Check Data Quality: Data issues are common in data science projects. Cleaning and preprocessing the data might resolve many problems. Consult Literature and Documentation: Consult relevant literature, research papers, or documentation. Experiment: Experiment with alternative algorithms, features, parameters, or methodologies to see if they improve your results. Document Findings and Solutions: Use proper documentation for future reference.

- Thoroughly assess the problem at hand to understand its scope and implications. - Identify the specific nature of the problem, whether it's data quality, model performance, or scalability. - Strategize an appropriate response based on your analysis, such as data cleaning, model adjustment, or exploring alternative approaches.

Once you identified the root cause and validated it, sharing the findings to wider groups in the team has always helped me. It could be some gap in business understanding or data quality issue from upstream or drawback of a modelling technique for your usecase.

- Collaborate with colleagues or peers to gain insights and fresh perspectives on the problem. - Leverage the collective knowledge and experiences of your team or online data science communities. - Engage in discussions, share your challenges, and seek advice or solutions from others in the field.

- I would prefer to go through IEEE and data science research papers to stay update to date with SOTA frameworks. It always helps to finalize project implementation approach and benchmarking different data science methodologies to choose efficient approach to solve the problems.

- Explore existing literature, academic papers, technical blogs, and case studies related to your problem. - Gain insights and alternative solutions from previous research and experiences shared by others. - Understand the theoretical foundations of your problem to devise a robust and informed solution strategy.

When iterating your model, also consider integrating ensemble methods or leveraging transfer learning where applicable. Ensemble methods combine multiple models to improve performance, while transfer learning uses pre-trained models as a starting point, saving time and resources. These strategies can offer significant improvements, especially when facing complex problems where traditional approaches fall short.

- Iterate on your model's performance by experimenting with different algorithms, hyperparameters, and feature engineering techniques. - Embrace the iterative nature of Data Science and understand that models often require multiple iterations to improve. - Use a trial-and-error approach to refine your model and move closer to achieving accuracy and reliability.

- Validate the effectiveness of implemented solutions using cross-validation techniques. - Ensure that your model performs well on unseen data to avoid overfitting issues. - Use validation to confirm that changes made to the model genuinely improve its ability to generalize.

- Maintain detailed documentation of your problem-solving process. - Track strategies that worked and those that didn't for future reference. - Facilitate better communication among team members and stakeholders through documentation.

A team member taught me to have a validation approach defined prior to building modeling, or in cases, prior to data cleaning. In the validation approach, consider if you can verify the data using alternate sources, check how are the outlier values / extreme cases being handled and how different variables are interacting with each other. Defining such an approach would help one avoid a lot of problems encountered later in a data science project.

Os impactos do problema devem ser avaliados antes de qualquer tomada de a??o. Sugiro uma análise de riscos e o tipo de impacto a curto, médio e longo prazo se o problema n?o for resolvido. Fazer a árvore CTC traz uma maior compreens?o do processo como um todo. Na verdade há uma possibilidade de aceita??o do risco e de suas consequências. Gerenciamento de riscos e oportunidades diante do problema já existente. Sim! Um problema pode trazer oportunidades se bem explorado.