A naukri.com initiative
Dev
1M
167
Image Credit: Dev
Data Science Foundations: Practical Approaches with the 80/20 Rule — Part 1
- Data science can be overwhelming with various components, but the 80/20 rule offers valuable insights for high-impact steps in the data science workflow.
- Avoidable issues leading to data science project failures include unclear goals, lack of project management, resource constraints, rushed timelines, misaligned incentives, and absence of executive champions.
- Deployment planning should be considered from the project's inception to ensure the model's impact and usability are prioritized.
- Conducting a thorough literature review before starting a project can provide inspiration, data sources, model types, and benchmarks for success.
- When evaluating data sources, consider factors like availability, cost, utility, update frequency, and granularity to optimize data selection.
- Checking data quality is crucial to prevent 'Garbage In, Garbage Out' scenarios, involving assessments for missingness, range, outliers, timeliness, and formatting consistency.
- Dealing with missing data involves considering the amount missing, patterns, algorithm tolerance, and imputation options like mean/median, mode, or predictive imputation.
- Identifying strong features for models requires using filter methods, wrappers, embedded methods, and domain knowledge to enhance model performance.
- Data science blends art and engineering, emphasizing the importance of the 80/20 mindset for focused, impactful outcomes in projects.
- Strategic planning, prioritization, and feedback loops play key roles in transforming data science projects from prototypes to successful productions.
Read Full Article
9 Likes
For uninterrupted reading, download the app