Data science project card.md

[Data science project title]

Become less tool-focused and more impact-driven. Summarize a data science project in 10 minutes with the sections below.

🚀Objective

Addresses a specific problem that links to a strategic goal/mission/vision

Examples

• "Enable data-driven marketing to get ahead of competitors"
• "Automate fraud detection for affiliate programs to make marketing focusing on core tasks"
• "Build automated monthly demand forecast to safeguard company expansion".
• Build x to solve/achieve/improve y
• Create x to become data-driven
• Daily usage of reports y

[TODO: Define objective]

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🎯Key results

Lists measurable outcomes that mark progress towards achieving the objective

Examples

• "80% of marketing team use a dashboard daily"
• "Cover 75% of affiliate fraud compared to previous 3 month average"
• "Cut 'out-of-stock' warnings by 50%, compared to previous year average"
• Increase/reduce/improve X by y%
• Increase/reduce x% on y
• Predict X with accuracy Y

[TODO: List key results]

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🔢Data

Describes properties the ideal or available dataset

Examples

• "Transaction-level data of the last 2 years with details, such as timestamp, ip and user agent"
• "Product-level sales including metadata, such as location, store details, receipt id or customer id"

[TODO: Sketch data properties]

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💎Value-feasibility

Puts the project into a business perspective by visualizing value, feasibility and uncertainties around it. The larger the range of points, the more certain is the project's value or feasibility.

Examples

• Estimated uncertain value from 2-7/10

• Estimated low feasibility from 1-5/10

  V: ⚪🟢🟢🟢🟢🟢🟢⚪⚪⚪
  F: 🟢🟢🟢🟢🟢⚪⚪⚪⚪⚪

[TODO: Estimate value-feasibility]

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🔮Extensions

Sketches follow-up projects and puts the project into a broader perspective.

Examples

• apply sales prediction to marketing campaigns features
• use cloud architecture as blueprint for sales prediction pipeline

[TODO: Map possibilities]