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Here's how you can ensure timely delivery of your work as a data science professional.

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In the fast-paced world of data science, delivering your work on time is as crucial as the insights your data provides. Timely delivery helps maintain your credibility and ensures that decision-makers can act on your findings when they're most needed. Whether you're wrangling large datasets, building predictive models, or interpreting complex results, the key to success is a combination of good planning, clear communication, and efficient execution. By adhering to certain strategies, you can improve your time management skills and become a more reliable data science professional.

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Prithvi Seshadri

Data Science @Shell | Microsoft Certified Data Scientist | Vellore Institute of Technology
Fresh-nes rameaux Zouzou

Data Scientist |Data Analyst|Formateur BI | Formateur Microsoft PL-300 Certificate| Excel Avancée | Python |…

1 Plan Ahead

Effective planning is the bedrock of timely delivery in data science. Before diving into the data, take time to understand the scope of the project and set realistic deadlines. Break down the project into smaller tasks and estimate how much time each will take. Use project management tools to track progress and adjust your plan as needed. Remember, data can be unpredictable—allocating time for unexpected challenges will save you from last-minute rushes.

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Fresh-nes rameaux Zouzou

Data Scientist |Data Analyst|Formateur BI | Formateur Microsoft PL-300 Certificate| Excel Avancée | Python | Consultant en Business Intelligente
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Automatisation et Intégration Continue CI/CD : Mettre en place des pipelines d'intégration et de déploiement continu (CI/CD) avec des outils comme Jenkins, GitLab CI/CD, ou CircleCI pour automatiser les tests et le déploiement des modèles. Docker/Kubernetes : Pour containeriser et orchestrer les applications et les modèles de données pour des déploiements rapides et scalables. Planification des Ressources Gestion des Ressources : Utiliser des outils comme Resource Guru ou Float pour planifier et allouer les ressources (temps, personnel, équipement) de manière efficace. Calendriers Partagés : Utiliser des calendriers partagés (Google Calendar, Outlook) pour coordonner les disponibilités et les échéances.

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2 Set Milestones

Milestones are checkpoints that help you gauge whether you're on track. They are particularly useful in data science projects which often have multiple stages, from data collection to analysis. By setting milestones for each critical phase, you can monitor progress and identify potential delays early on. This proactive approach allows you to manage your time more effectively and provides clear goals to strive for throughout the project.

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Abdul Basit

Software Engineer | Generative AI, Large Language Models (LLM), Statistical Analysis |
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Planning for the week and individual days is essential to reaching your destination. To achieve your goals, you need to take small steps, and daily tasks are those steps that help you reach milestones. By focusing on these tasks, you can make steady progress towards your objectives.

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Abdul Basit

Software Engineer | Generative AI, Large Language Models (LLM), Statistical Analysis |
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Planning for the week and individual days is essential to reaching your destination. To achieve your goals, you need to take small steps, and daily tasks are those steps that help you reach milestones. By focusing on these tasks, you can make steady progress towards your objectives.

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Abdul Basit

Software Engineer | Generative AI, Large Language Models (LLM), Statistical Analysis |
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Planning for the week and individual days is essential to reaching your destination. To achieve your goals, you need to take small steps, and daily tasks are those steps that help you reach milestones. By focusing on these tasks, you can make steady progress towards your objectives.

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Abdul Basit

Software Engineer | Generative AI, Large Language Models (LLM), Statistical Analysis |
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Planning for the week and individual days is essential to reaching your destination. To achieve your goals, you need to take small steps, and daily tasks are those steps that help you reach milestones. By focusing on these tasks, you can make steady progress towards your objectives.

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Abdul Basit

Software Engineer | Generative AI, Large Language Models (LLM), Statistical Analysis |
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Planning for the week and individual days is essential to reaching your destination. To achieve your goals, you need to take small steps, and daily tasks are those steps that help you reach milestones. By focusing on these tasks, you can make steady progress towards your objectives.

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3 Prioritize Tasks

In data science, not all tasks are created equal. Prioritize your work based on the project's needs and deadlines. Tackle high-impact tasks that directly contribute to your end goals first. If you're faced with multiple priorities, assess which ones will require the most time or are most critical to the project's success, and focus your efforts accordingly. Effective prioritization ensures that you're always working on what matters most.

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Lavanya S.

? 2X Top LinkedIn Voice ?? | IT & Computing Faculty @ EEI Dubai | Pearson Assessor | BTEC | Assistant Professor | IT Trainer | Lecturer (Level 3-6, MSc/MCA) | Data Scientist | Program Developer | 1st Rank Holder ??
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Ensuring timely delivery in data science hinges on smart prioritization. Not all tasks hold equal weight; focus on those with the highest impact. Start with tasks that directly advance project goals. When juggling multiple priorities, evaluate which ones are most time-consuming or critical to success and allocate your efforts there. For instance, if building a predictive model is crucial for a product launch, prioritize it over exploratory analysis. You can maintain steady progress and meet your timelines by continuously reassessing and aligning your tasks with project deadlines.

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4 Communicate Clearly

Clear communication is vital in ensuring that everyone involved in a project is aligned with timelines and expectations. As a data science professional, keep stakeholders informed about your progress and any challenges you encounter. This transparency allows for collaborative problem-solving and helps prevent misunderstandings that could lead to delays. Regular updates can also provide motivation and maintain momentum towards project completion.

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5 Use Tools Wisely

A variety of tools can aid in ensuring timely delivery of data science projects. From version control systems like Git to data analysis platforms, using the right tools can streamline your workflow and save time. Automate repetitive tasks where possible, and create reusable code for common analyses. However, be mindful of the learning curve associated with new tools—invest time in mastering them when it won't impact critical deadlines.

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Prithvi Seshadri

Data Science @Shell | Microsoft Certified Data Scientist | Vellore Institute of Technology
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I use Git for version control to track code changes and collaborate with my team. By setting up automated GitHub Action workflows with a cron scheduler we can schedule regular tasks like data fetching, preprocessing, and model training to ensure everything runs smoothly and on time. It's important to remember that GitHub Actions operate on UTC time, so we need to convert our desired times accordingly when creating cron jobs. For exploratory data analysis, I use Jupyter Notebooks to test and refine ideas which can later be pushed into automated pipelines. This streamlined approach simplifies our workflow and ensures seamless project delivery.

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Fresh-nes rameaux Zouzou

Data Scientist |Data Analyst|Formateur BI | Formateur Microsoft PL-300 Certificate| Excel Avancée | Python | Consultant en Business Intelligente
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Outils de Gestion des Données Apache Airflow : Pour l'orchestration des workflows de données et la planification des pipelines ETL. Luigi : Pour la construction de pipelines complexes de manière modulaire. Prefect : Pour une gestion moderne et flexible des workflows de données. Talend : Pour les solutions ETL et l'intégration de données. Outils de Planification et de Suivi des Ressources Resource Guru : Pour la planification et l'allocation des ressources. Float : Pour la gestion de la disponibilité et des charges de travail des membres de l'équipe. Calendriers Partagés : Utiliser Google Calendar ou Outlook pour coordonner les disponibilités et les échéances.

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6 Stay Flexible

Flexibility is key in data science, where data anomalies and unexpected results can change the course of a project. Be prepared to adapt your approach as new information emerges. This might mean revisiting your plan or reallocating resources to address new challenges. Staying flexible allows you to overcome obstacles without significantly impacting your delivery timeline.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Here's how you can ensure timely delivery of your work as a data science professional.

1

2

3

4

5

6

7

1 Plan Ahead

2 Set Milestones

3 Prioritize Tasks

4 Communicate Clearly

5 Use Tools Wisely

6 Stay Flexible

7 Here’s what else to consider

Data Science

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Here's how you can ensure timely delivery of your work as a data science professional.

1

2

3

4

5

6

7

1 Plan Ahead

2 Set Milestones

3 Prioritize Tasks

4 Communicate Clearly

5 Use Tools Wisely

6 Stay Flexible

7 Here’s what else to consider

Data Science

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