Notes:
Data Science Methodology guides data scientists in solving complex problems with data. This includes forms of data colleciton, strategies for measurement and methods for comparison.
- Business understanding
- Analytic approach
- Data requirements
- Data collection
- Data understanding
- Data preparation
- Modeling
- Evaluation
- Deployment
- Feedback
- Get stakeholder buyin and support
- Define: Prepare clearly defined set of questions to help identify the right analytic approach
- Understand: Understand the goal of the sponsor
- Objectives: Organize set of clear objectives
- Engagement: Stakeholder engagement is important in capturing the requirements and clarify the questions
- what is the problem are we trying to solve?
- Define goals and objectives
- Kickoff the project with
- What is expected out of sponsor
- Set the project direction
- Remain engaged and provide guidance
- Ensure needed support should need arise
- Steps to run analytical approach
- Identify the patterns
- Choose an analytical approach
- Apply machine learning
- Available patterns to address the questions
- Descriptive (Current status)
- What is the current situation?
- Diagnostic (Statistical Analysis)
- What happened?
- Why is this happening?
- Predictive (Forecasting)
- What if the trends continue?
- What will happen next?
- Prescriptive (Reccommendations)
- How do we solve it?
- Descriptive (Current status)
- Use Descriptive Model to show relationshops
- Use Predicitive Model to show probabilities of an action
- Use Classification Model to capture yes/no answers
- Learn without being programmed
- Can identify relationships and trends in data
- Uses clustering association
- A classification outcome is certain
- A decision path is well represented by describing the conditions leading to high risk
- Simple to understand and implement