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Analytics Projects in Data Science: A Practical Framework
- A data science professional shared notes on data science practices on the Rednote platform in 2023.
- The notes, which were recently translated into English, focus on transitioning analytics teams from reactive reporting to proactive business partnership.
- A well-structured data science team should prioritize three core project categories.
- Success depends on the proactive or reactive approach of the data team within the product organization.
- Ideal time allocation across projects should be 30% on metrics design, 50% on strategy analysis, and 20% on research projects.
- Neglecting research projects can hinder long-term team growth and business influence.
- Analysts should take ownership of business outcomes and exercise judgment.
- Metrics are crucial for defining and measuring business objectives and performance.
- Data analysts are responsible for tasks related to metrics, ideally led by the data science team.
- Data analysts play a significant role in problem framing and impact measurement in strategic analysis projects.
- Exploratory research projects are important initiatives focusing on enhancing analytics efficiency and decision quality.
- Data analysts need to 'sell the vision' of research projects to get them prioritized.
- Research projects are vital for career advancement in data analytics.
- Strategies are needed to secure time for research projects amidst daily business operations.
- Balancing metrics design, strategic analysis, and research projects is crucial for data science teams.
- Successful analysts navigate between reactive business needs and proactive value creation for better outcomes.
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