Using AI – Complex Data Science Process

Slide Content

The slide presents an overview of a complex data science process using AI, dividing the workflow into four main stages: data, AI, value, and business impact. Each stage is further detailed with steps. "Data" covers information capture, quality audit, and selection. "AI" encompasses data engineering with analysis, exploration, cleaning, preprocessing, and modeling that involves model selection, training, and evaluation. The "value" phase is not expanded, whereas "business impact" involves implementing through deployment, integration, and maintenance, highlighting the need for monitoring and retraining.

Graphical Look

• The slide is divided into four columns with distinctive colors indicating different stages of the process.
• Each column contains a series of rectangles with text, arranged vertically to represent the flow within each stage.
• Icons are used to represent each stage—an image of stacked disks for “DATA,” a lightbulb for “AI," a star in a circle for “VALUE,” and an upwards arrow for “BUSINESS IMPACT.”
• Smaller text boxes at the bottom outline constraints, with red lightning bolt icons symbolizing each point.
• Horizontal arrows between the main columns suggest progression from one stage to the next.

The slide has a clean and organized layout with a clear flow from data collection to the business impact of AI. The use of color-coding and symbols efficiently delineates different sections and steps within the data science process.

Use Cases

• To introduce the steps involved in an AI data science project during an educational or training session.
• As part of a proposal presentation to stakeholders to outline the AI implementation strategy.
• During a team meeting to define roles and responsibilities across the data science workflow.
• In a conference or seminar to discuss best practices and methodologies in AI and data science.