The Life of Data Science is Experience

Applied data science at the high level is about creating value, finding new truths and helping optimal decision making. Seasoned data scientists understand that the life of data science is experience and NOT models, statistics, math, artificial intelligence, logic or elegant computer code (although data scientists must be proficient with these disciplines). Technology and artificial intelligence are simply awesome tools that may be useful to achieve specific goals.

Experience in life, weighing evidence, solving problems, selecting the appropriate conceptual framework for a specific context, understanding reality vs. theory, avoiding cognitive and confirmation biases, understanding human psychology, how to set appropriate and attainable goals, how to select the right data sets, and how to recognize untruthful or "bad" data, is critical for high performance. Moreover, seasoned data scientists know how to architect and execute processes to collect the right "smart" data not currently available, how to design real time experiments, and how to modify learning algorithms via trial and error.

Today it is sexy for data scientists to design and execute learning algorithms yet executing algorithms using the wrong conceptual framework and wrong data can lead to disaster.

Great data scientists know from experience how to select the right conceptual framework and right data to apply the right algorithm to achieve specific goals - and that is why the life of data science is experience.