A good algorithm needs to be developed through integrating knowledge-based data into analytic models simulation testing, implemented for problem-solving.
It perceives the emergent trends of digital leadership, advises on how to run a digital organization to unleash its full potential and improve agility, maturity, and provide insight about Change Management. It also instructs the digital workforce on how to shape a game-changing digital mindset and build the right set of digital capabilities to compete for the future. Here is a set of “Algorithm” quotes in “Digital Master."
1 An algorithm is a procedure or formula for solving a problem. An algorithm is a model of the real world.
2 A good algorithm needs to be developed through integrating knowledge-based data into analytic models simulation testing, implemented for problem-solving.
3 Algorithms are indeed nice tools for a data scientist. However, you need to keep in mind that underlying these algorithms are models, models with their own assumptions, strengths, and weaknesses.
4 The algorithms require data and understanding the idiosyncrasies of these data is critical to model performance.
5 Understanding how to synthesize new predictors in a way that increases the predictive power of these data is critical to increasing model performance. This is where the Data Scientist shows the true value, and where algorithms fall flat on their faces.
6 Analytical algorithms and data scientists are not mutually exclusive, but they are absolutely complimentary.
7 The point is that humans should all have some humility and recognize the limitations of their expertise and partner them with the other experts to apply the analytical algorithms for problem-solving.
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