Before starting with a project it is extremely crucial to understand what the actual problem is, who the key audiences are, and what the metrics are. We should also have concrete information on any stringent rules or regulations that could hamper your project, be it in terms of timeline, the technical requirements (whether to use python or R, GitHub of codespaces, etc.), any industry-specific conditions etc.

Considero que comenzar identificando los objetivos del proyecto de ciencia de datos es un error. El primer paso debe ser identificar los objetivos del negocio y cómo el proyecto va a contribuir para su consecución. Podemos tener el mejor proyecto de análisis o incluso de predicción, pero si el negocio no puede tomar acción y/o sacar provecho de esa información para mejorar su rentabilidad entonces no será de real valor para la organización. Tener esto claro al comienzo nos ayudará a fijar prioridades y elegir el proyecto de mayor impacto para concentrar allí la mayor cantidad de recursos.

When handling competing deadlines in data science: 1. Define Clear Objectives: - Break down project goals into specific, measurable objectives. 2. Know Stakeholders: - Align goals with the expectations of clients, team, and end-users. 3. Assess Constraints: - Consider time, resource, and budget constraints. 4. Understand Dependencies: - Prioritize tasks based on dependencies. 5. Risk Assessment: - Identify and plan for potential risks. 6. Quantify Trade-offs: - Understand trade-offs between goals. 7. Set SMART Goals: - Ensure goals are Specific, Measurable, Achievable, Relevant, and Time-bound. 8. Regularly Revisit and Adjust: - Adapt goals based on project progress and changing circumstances.

Assuming project leads to a model: > First clarify the problem statement of the project > Perform parametric estimates based on previous projects > Draw a schedule aligning with data science CRISP-DM phases and see how far or near you are against the deadlines > Set expectations with the stakeholders informing them practically when a preliminary model and a refined one will be ready However, you can provide early insights to stakeholders by having data analysts perform exploratory data analysis, which can set a course for corrective actions or improvements that could address the problem to some degree until a model gets ready to take more decisive decisions.

Before diving into your tasks, take the time to clearly define the goals of each project or task. Understanding the objectives will help you prioritize your work and stay focused. Additionally, identify any constraints or limitations that may impact your work, such as resource availability, budget constraints, or dependencies on other teams or projects.

Not all tasks are created equal, so it's important to prioritize them based on their importance and urgency. One effective way to prioritize tasks is to use the Eisenhower Matrix, which categorizes tasks into four quadrants based on their urgency and importance. Tasks that are both urgent and important should be tackled first, followed by those that are important but not urgent. Tasks that are urgent but not important can be delegated or postponed, while tasks that are neither urgent nor important can be eliminated or postponed.

Effective management hinges on the strategic prioritization of tasks. Begin by evaluating each task's urgency, significance, and complexity. Urgent tasks demand immediate attention due to impending deadlines or their substantial impact on the project's trajectory. Significant tasks are pivotal for achieving project objectives or enhancing stakeholder value. Meanwhile, complex tasks necessitate considerable time, expertise, or resources. Employing a structured approach, such as a prioritization matrix, allows for the systematic ranking of tasks. Concentrating efforts on tasks that are both urgent and crucial ensures that you navigate through deadlines efficiently, keeping the project on track without compromising quality or outcomes.

When a project is started, the first step would be to identify your tasks. Not all tasks would be equal due to various reasons such as the complexity of data, stakeholder preference, prerequisite of project, etc... So in this case you can put all your tasks into an Excel sheet with tasks in one column and based on the urgency due to the reasons mentioned above color up the cells. For example in an ML project lifecycle, the most important phase would be to collect data because without data the project isn't moving forward so priority is provided to it. If you are building a new application and a stakeholder wants a feature like tagging then it should be prioritised more and delivered first

In managing competitive deadlines in data science projects, task prioritization is key. Evaluate the importance of each task, identify critical ones, and break down the project into manageable tasks. Set intermediate deadlines, use project management tools, and communicate clearly with your team.

Organize your tasks and use platforms that assist with this, like GitHub and Trello and Slack. Set a clear ToDo list and weigh importances against deadlines. And try to do the most difficult or uncomfortable tasks first!

- Segment the project into smaller, manageable tasks. This makes it easier to allocate resources, estimate time requirements, and set deadlines. - Use flowcharts or diagrams to visualize the workflow, making it easier for the team to understand the project’s progression. - Project management tools like Gantt charts can be particularly useful in visualizing the schedule, showing how tasks overlap and align with the project timeline. - Leverage project management software. Tools such as Asana, Trello, and Jira offer features for task assignment, deadline reminders, and progress tracking. - Ensure that the entire project team is aware of the workflow and schedule. Clear communication helps!

One thing that has worked for me is to set the expectations at the very beginning. Work always operates on 3 degrees of freedom - Quality, Quantum and Time taken. Only two of the factors can be controlled. Realize what is under your control and convey the expectation accordingly. Always err on the side of over communication. Break your task into stages - for e.g create a baseline model and take feedback. Don't jump into a utopian fine tuned model. Remember - tight deadlines define your character. Always try and become the go to person who delivers on tight deadlines.

Borrow a few concepts and even tools from project management, from daily stand-ups (even if it's just you working on a project - it's nice to reflect on the progress every day) to dedicated software (like Trello, Asana, notion)!

Kanban board will be more efficient and you can make different sections: 1. Tasks and work packages: List of tasks required to complete the project 2. Identify dependencies in correct order between tasks 3. Allocate timeframes for each task on Gantt Chart 4. Indicate resource assignments for each task to prevent overloading or underutilisation 5. Highlight milestone and checkpoints on Gantt chart

Once you've identified your goals and priorities, it's time to plan your workflow and schedule. Break down your projects into smaller, more manageable tasks, and create a detailed timeline for completing each task. Consider using project management tools like Gantt charts or Kanban boards to visualize your workflow and track your progress. Be sure to include buffer time in your schedule to account for any unexpected delays or issues that may arise.

Effective communication is key to successfully managing competing deadlines. Keep your stakeholders and team members informed about your progress, deadlines, and any challenges you may be facing. Regular check-ins and status updates can help ensure that everyone is on the same page and that any issues are addressed promptly.

Establishing a culture of open dialogue and regular check-ins (useful ones!) is fundamental. Effective communication isn't only about updating statuses but creating an space where feedback is actively sought and challenges are addressed in real-time. This may mean setting up structured meetings but may also mean short tailored updates that highlight progress towards their specific interests or concerns than can foster trust and maintain their engagement without chewing though hours of meetings. In data science projects where uncertainty is common, my experience is being proactive in communication can preempt misunderstandings and align everyone's expectations, thereby ensuring smoother project execution amidst competing deadlines.

It can be tempting to show all the insights and exciting data you’ve uncovered, but remember less is more, especially for your non-technical stakeholders. Focus on the key points and deliverables, let your colleagues ask clarifying questions and keep them engaged with the data model as it evolves.

Do not underestimate the capacity of "non-data" teams to understand data constraints. They will most likely be able to tell apart engineering issues from visualization steps, for instance. No need to get into technical details here, but make sure they don't see DATA as one big mysterious block.

Through my current job, I have come to recognize the immense value of effective communication. The insights and feedback provided by the business stakeholders play a vital role in enhancing the efficiency of our existing processes. Moreover, they possess a deep understanding of the business, making their input invaluable. By involving in frequent communication, we not only improve our collaboration but also gain valuable knowledge and insights that contribute to our professional growth. It is crucial to keep all team members well-informed about the progress, status, and challenges we encounter. This practice promotes transparency, encourages teamwork, and enables us to collectively address any potential hiccups more efficiently.

Effectively managing competing deadlines in data science projects involves prioritizing tasks, breaking down tasks into manageable chunks, setting realistic deadlines, utilizing project management platforms, allocating time wisely, maintaining transparent communication with stakeholders, remaining flexible, delegating tasks when possible, learning to say no to additional commitments, and reflecting on time management strategies for continuous improvement.

Efficiently executing tasks in data science ensures quality deliverables. Leverage tools like VS Code, PyCharm for interactive coding or in-depth analysis. Follow best practices to maintain consistency, make sure your code is properly formatted. Thoroughly test and validate your models to ensure accuracy and optimize them for performance. Document your process and results methodically for transparency, keep proper comments and function docstrings so that your work is self explainable and present your findings effectively to highlight your data science project's value. This approach solidifies your reputation for delivering robust, reliable insights and ensures that your contributions are clear, impactful, and actionable.

With your plan in place, it's time to start executing your tasks. Stay focused and work diligently to meet your deadlines. Consider using time management techniques like the Pomodoro Technique to stay focused and productive. Be sure to track your progress regularly and make adjustments to your plan as needed.

🧪 Implement Solutions: Apply data science techniques effectively, ensuring high-quality outputs. 📊 Follow Best Practices: Utilize industry standards for coding, testing, and documentation. 🔍 Optimize Results: Continuously test and refine your models for accuracy and efficiency. 📝 Documentation: Thoroughly document your processes, methods, and insights. 💼 Presentation: Use tools like Jupyter Notebook and Power BI to showcase your findings clearly.

It can be useful to have collaborative coding sessions. For a project creating complicated metrics that took pages of Python coding, a colleague and I worked on it simultaneously using our own methods. It uncovered gaps and differences in our understanding for certain steps and ultimately improved our code quality and problem solving.

Outsource it. Pay someone trying to build their profile on upwork $10 to work in parallel with you on your goals. You don’t have to give them the real data or let them know their doing something meaningful.

Data science is not a specialized group anymore with the emergence of open source tools and low code/no code frameworks. While the core principles of building a model, training and deploying still needs specialist skills, I’d consider leveraging interdisciplinary teams like Data Engineers, BI analysts and delegating tasks to anyone who is analytics savvy. This naturally creates upskilling opportunities for professionals in the organization and also a healthy backlog

Implementing Data Science/Machine Learning in a project is much complex be in the terms of Data preprocessing, model Development, or model deployment, one must efficiently divide these modules/sub modules in such a way that deadlines can be achieved on timely manner, to understand this, one must first understand which module will take more time in developing, for an instance suppose dataset provided is not refined then being a data scientist , one must understand/calculate the time consumption to refined first. Secondly, if there is class imbalance in data then one must perform EDA precisely so that predictions/forecast should not be overfitted/undercoated and so on so forth. Hence to conclude one must manage time module wise accordingly

1)Craft a Storyline for Your Project: Treat your data science project like a compelling story you're unfolding on LinkedIn. Share the project's objectives, challenges, and milestones as episodes in an ongoing series. This keeps your network engaged and looking forward to your updates. 2)Share Real-Time Problem-Solving: Post about the real-time challenges you face and how you're addressing them. This transparency not only humanizes the process but also positions you as a thought leader who's not afraid to tackle tough problems.

- Keep up with the developments; this is an area that it's constantly developing and there is dynamic progress. - Managing deliverables might mean outsourcing some smaller tasks so they can be delivered in parallel to other tasks. This depends on the team structure and size of course. - Make sure that you have a development environment setup before commencing any project. You don't want to waste time on systematic setups. - Something that I always encourage is to do a "lessons learned" session at the end of each project to identify areas that we can improve for future projects.