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@philippschmalen
Last active April 6, 2024 06:56
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[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]

🎯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]

🔢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]

💎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]

🔮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]
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