What do you do if you encounter roadblocks while solving problems in Data Science?

In my experience, identifying the root cause of a problem is indeed the crucial first step in solving it effectively. This process involves distinguishing between various types of issues, such as data issues, code issues, model issues, and knowledge issues.

Segment the Problem: Break down the problem into smaller, more manageable parts. This can help isolate where the issue is occurring. Simplify the Scenario: Try to simplify the scenario or use a smaller dataset to see if the problem persists. This can make it easier to pinpoint the cause. Algorithm/Model Review: Ensure that the chosen algorithms or models are suitable for your specific problem and data. Some algorithms might not perform well given certain types of data. Peer Review: Have a colleague review your code or approach. A fresh pair of eyes might spot something you’ve overlooked. Data Quality Check: Assess the quality of your data. Look for issues like missing values, or incorrect data types that could be causing the problem.

Root cause identification in roadblocked data projects can be challenging but absolutely crucial to find the next step forward. In my experience there is a clear structured way you can go about achieving this. 1. Question understanding of the problem statement : Speak to stakeholders and domain knowledge experts to identify if our current understanding of the problem statement is in the right direction 2. are we using the right data sources : Are our current data sources representative of the problem? Is the data updated? 3. Are our features representative of the problem? : Have we identified/engineered the right features to capture the scope of the challenge? 4. are we using the right models? : lastly, is there a better model to use.

Problems and setbacks in code can typically feel highly intimidating and challenging due to project timeline pressures. The best way to tackle such problems is to break them down to their fundamental components through a discussion and ideation meeting with the team. Once we've broken down the problem into more managable subchunks, what we in the team found helpful was to hold a hackathon session and split members into smaller groups to tackle one part of the problem allowing for developement of quick prototypes and identification of potential paths to resolve the entire issue on the day of the hackaton itself. If the problem is still too challenging, seek internal peers and external resources that can help find the breakthrough.

?? Isolate Components: Tackle each subproblem individually to make the overall problem more manageable. ?? Write Pseudocode: Draft solutions in pseudocode to clarify your approach without getting bogged down in syntax. ?? Iterative Approach: Solve one subproblem at a time, iterating over solutions to build towards the larger answer. ?? Seek Resources: Utilize forums, documentation, and peers for insights into similar issues or alternative solutions. ?? Think Laterally: Sometimes, approaching a problem from a different angle can reveal simpler solutions. ?? Prototype Solutions: Develop quick, small-scale models to test assumptions or approaches for each subproblem.

Exploring alternative solutions is best achieved by using different perspectives of different members to identify the best possible solution. In my personal experience, group hackathons are a highly effective way to find different strategies to tackle problems and not only identify but build prototyped solutions. Well managed hackaton days can potentially resolve challenges that otherwise might take days! Depending on the problem there maybe better alternatives as well, for example data quality issues can be resolved through having a brainstorming session on best practices, code quality issues can be resolved through pair programming sessions and so on.

Based on the problem, once the solution is identified, implementation should be relatively straight forward, following typical software best practices of version controlling, documentation of the issue faced as well as the solution found, as well as agile retros to identify what improvements can be made to prevent the event happening in the first place. Model and project based performance of the prototype can then be evaluated based on the original evaluation metrics of the project (be it modelling metrics like F1 score or business KPIs). Ensure that the model performance is meeting original standards set, and explain why if the standards are still not met.

Roadblocks and challenges are typical to any project in any business, and instead of being disheartened, the important thing is to ensure you learn from the experience. Agile methodologies are highly helpful in this continual improvement process, with sprint retros being a good platform for teams to discuss what went well (shouting out the good of overcoming it) as well as what went wrong and ideas for improvement. When implemented well, team retros are lively discussions of problems which result in continual improvement of standards by identifying actions that can be taken to prevent challenges faced form occurring ever again. Additionally sharing with internal communities and getting feedback on the experience can be a huge help too.

?? Reflect Regularly: Take time after each project or major task to reflect on successes and challenges. ?? Seek Constructive Feedback: Ask peers and stakeholders for input on your approach and outcomes. ?? Code Review: Periodically review your own and others' code for quality, efficiency, and readability. ?? Document Lessons: Keep a journal or digital record of insights and lessons learned from each project. ?? Share Knowledge: Present your findings and solutions at meetings, workshops, or online forums to help others. ?? Embrace Continuous Learning: Stay updated with the latest data science trends, techniques, and technologies. ?? Apply Learnings: Use insights gained to refine your methodologies and approaches in future projects.

When encountering roadblocks in Data Science, another aspect to consider is fostering a collaborative environment. Engaging with colleagues, seeking expert advice, or participating in data science communities can provide new perspectives and innovative solutions. Collaboration can spark creativity, offer support during challenging phases, and potentially introduce methodologies or tools you haven't considered. This collective approach enriches problem-solving strategies and promotes a culture of continuous improvement and learning within the data science field.