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A defining part of the engineering approach is moving from an ambiguous real-world problem to a formal and executable solution through the same underlying principles of abstraction, modeling, reasoning, algorithms, and computation.
Step 01
Identify variables, constraints, objectives, assumptions, and available information.
Step 02
Translate the real-world problem into mathematical / computational form.
Step 03
Represent uncertainty, incomplete information, distributions, dependencies, and decision conditions where appropriate.
Step 04
Select or develop an appropriate analytical, numerical, optimization, machine-learning, or computational method.
Step 05
Search for feasible, efficient, robust, or multi-objective solutions under constraints.
Step 06
Convert the model into software, algorithms, services, or production systems.
Step 07
Test assumptions, evaluate outputs, measure performance, and refine the model or implementation.
Comfortable working across multiple programming languages and programming paradigms.
Able to work across different frontend and backend frameworks and adopt new technologies when the problem requires them.
Comfortable working with different database models and selecting the appropriate storage technology according to data and system requirements.
Technology selection follows the problem, constraints, architecture, performance requirements, and long-term maintainability.
Able to move from mathematical formulation and experimental research to algorithms, software implementation, validation, and production-oriented systems.