Data Analytics Overview

Data Analytics Stages

• Three stages of data analysis exist: descriptive, predictive, and prescriptive analytics.
• Descriptive analytics involves analyzing past data to identify patterns and trends.
• Predictive analytics uses past data to predict future outcomes.
• Prescriptive analytics provides recommendations based on predicted outcomes, aiming for better future results.

Descriptive Analytics and Data Visualization

• Descriptive analytics analyzes past data to understand patterns and trends.
• Data visualization effectively communicates insights from data analysis, creating engaging and insightful stories.
• Intuitive data visualization accurately translates data into understandable interpretations, avoiding misleading information.
• Important to understand the limitations of data visualization by avoiding misinterpretations and understanding how to avoid the tricks outlined in the book "How to Lie with Statistics".

Role-Playing Exercise (Health Insurance)

• Managers in a health insurance provider role contribute to understanding the healthcare costs of their members.
• Health insurance in the United States generally covers all medical costs.
• The role-playing exercise focused on health insurance managers for senior citizens (66 years and older).
• The goal was to provide adequate healthcare while managing costs and member health.

US National Medical Expenditure Survey (NMES) Dataset

• The NMES dataset involves 4,406 senior citizens from the general population.
• The sample includes individuals aged 66 and older; not specific health insurance members.
• The dataset includes variables like hospital stays, health status, chronic illnesses, and demographics.

Al-Powered Descriptive Analytics with Microsoft Excel

• Excel Al simplifies data analysis using AI tools.

Predictive Analytics

• Predictive analytics analyzes past data to predict future events, based on the assumption that the future will mirror the past.
• It focuses on highly accurate estimations and predictions.
• Predictive analytics is useful in prioritizing tasks, anticipating outcomes with little control, and benchmarking performance to identify improvements.

Best-Performing Model for Predicting Hospital Stays

• The Mean Absolute Error (MAE) is a metric for assessing the accuracy of a model.
• Lower MAE values indicate more accurate predictions.
• The analysis presented graphs showing model performance in predicting hospital stays.

Gradient Boosted Tree Model

• Gradient Boosted Trees model helps understand factors impacting hospital stay counts.
• Important factors for predicting hospital stays were identified through this model.
• Variables ranked by their contribution to the model's prediction accuracy were shown.

Prescriptive Analytics

• Prescriptive analytics analyses past data to provide guidance on interventions for improving future outcomes.
• The focus is on input factors (factors that affect the outcome), aiming to improve the outcome, not replicate the past.
• Using interpretability, prioritizing, and anticipating outcomes, and benchmarking gives more valuable insights.

Accurate Predictions

• An accurate prediction is valuable; a goal for many models.
• State-of-the-art techniques achieve prediction accuracy.
• Simulations help estimate the effects of different values.

Explainable Al (XAI)

• XAI is a form of Artificial Intelligence providing explanations about model predictions, outcomes, and actions.
• Stakeholders include managers, users, developers, and regulators.
• Goals of XAI include algorithmic transparency, realistic representation, ethical responsibility, and prescriptive actionability.

XAI for Algorithmic Transparency

• XAI for algorithmic transparency explains how the AI model reached its conclusions using easily understandable language.
• This is achieved by simplifying internal operations while avoiding model misconceptions.
• This usually benefits developers most.

XAI for Realistic Representation

• XAI for realistic representation explains the correspondence between the AI model predictions and reality.
• This understanding often requires domain input from experts.
• Different types of models may correspond to reality in different ways.

XAI for Ethical Responsibility

• XAI for ethical responsibility provides information with values like fairness and privacy in mind.
• Bias elimination in training data is part of fairness.
• Consideration for user privacy and data protection is important.

XAI for Prescriptive Actionability

• XAI for prescriptive actionability supports action recommendations from the AI.
• This usually involves finding causal relationships and collaborative improvements.
• This is useful for persuading managers and also for helping users better understand the goals of the model.

Relationships Among XAI Stakeholders' Goals

• A diagram outlining the relationships among the stakeholders' goals shows how they relate to each other, suggesting that ethical responsibility (ER) needs explaining (XAI); however, algorithmic transparency is just part of the needed explanation by XAI for realistic representations.

Accumulated Local Effects (ALE) Plots for XAI

• ALE plots visualize the impact of different X (variables) values on the Y value (a prediction variable) for XAI.
• This helps in understanding which factors affect outcomes and to what degree.

Actionable Explanations

• Actionable explanations in XAI provide insights for managerial action based on predictions.
• The analysis should highlight relevant factors, their influence, and the potential for managerially actionable change.

XAI Process

• The process of actionable explanations involves gathering relevant concepts.
• Actionable explanations should be classified.
• Analysis of various methodologies is vital for achieving useful and meaningful conclusions.

Relevance of Concepts in XAI

• Useful and relevant concepts are crucial for effective XAI, categorized appropriately.
• Relevant concepts must affect the outcome being predicted, classified in ways that help to predict future outcomes.
• Non-relevant concepts, those which do not affect the predicted outcome can be ignored and resources can be saved by not being investigated.

Controllability of Concepts in XAI

• Controllability of concepts means how much managers can influence the actions, values, and outcomes.
• Understanding the different levels of control (high, low, no) is important in managing action.

Evaluation of the Teacher

• Provide comments instead of just numerical scores.
• Comment on aspects you liked or areas you think need improvement.
• Focus comments on the in-class session, not on homework or other instructors.

Conclusion

• Summary of the three stages of data analysis:
• Descriptive (past data and patterns), predictive (future prediction) and prescriptive (actionable recommendations)
• Importance of XAI to understand machine learning model results.

Explore the three stages of data analytics: descriptive, predictive, and prescriptive. Learn how these stages work together to provide insights and recommendations for better decision-making. The quiz also covers the effective use of data visualization and its limitations.