Data science forensics is a hot growing field for professional data scientists. Usually employed to detect financial and business fraud, it is now being used to detect voting fraud in the US. Teaming with data engineers, computer scientists, lawyers, law enforcement and regulators, forensic data scientists look for anomalies to flag for further investigation.
Recent events suggest a smart upgrade of election voting systems. I propose an intelligent e-voting system architected to prevent vote fraud and protect personal privacy while allowing optimal convenience and ease-of-use for voters.
Biometric technology allows voter identification to grant access to voting systems (using facial recognition, iris retina scanning, fingerprint or voice identifiers).
COVID19 exposed the danger of relying on models to make policy decisions. It appears in vogue with certain academics that models are "science" and fit for forecasting and guiding policy decision making. Yet as any successful real world executive, trader or surgeon will tell you, models are NOT appropriate for decision making but may or may not be useful for understanding phenomena.
Models are a useful tool, NOT an accurate forecaster.
COVID19 has exposed many hidden conflicts of interest in the scientific community. An ancient proverb holds that he who serves two masters has to lie to one. Public scientists have an interest in obtaining funds for research. Private scientists have an interest in holding gainful employment in organizations that may profit from exaggerated disease risks and public fear. Incentives matter.
Professional data scientists are goal oriented and understand there are many ways to skin a cat.
One school of thought is to NOT aim directly at the goal but rather focus on process and NOT care about the outcome. This mindset provides great success for professional athletes, big mountain powder skiers, traders, surgeons, CEO's, fighter pilots and other elite performing people. It reduces anxiety and calms the mind.
Seasoned data scientists understand that leading a high performance organization demands both discipline and freedom. Discipline to apply logic, avoid biases and follow scientific method. Freedom to explore a diversity of conceptual frameworks and allow cross pollination of subject matter expertise.
Seasoned data scientists understand that some data is more important than others.
Great data scientists are able to acquire high value awesome data and interpret to find new truths.
Are you preparing to become a great data scientist?
Professional data scientists understand that algorithm source codes is the secret sauce to significant competitive advantages, better public policy and massive profits. Ownership and control of the algorithm source code is of critical importance.
Data scientists who work as an employee directly for an employer concede legal ownership and control of any and all work products to the employer.
A swarm is a number of phenomena massed together in motion in space or time. One technical definition is a group of independent agents interacting with one another and with their environment. Natural examples include a flock of birds, colony of ants and school of fish. There is a unique type of intelligence from the swarm resulting from the collective behavior of decentralized, and self determined and organized systems.
Professional data scientists frequently use artificial intelligence (AI) to help achieve goals. AI adds significant value when used properly. Building AI into data science processes is awesome fun and sometimes mind blowing.
Every year and every month AI gets better, stronger and smarter. Sometimes AI will exhibit unique conceptual frameworks that humans would not think of - adding massive competitive advantage when applied to a myriad of processes.
AI can be a beautiful thing.