What makes a professional?
What make a professional?
A professional has the ability to execute the correct decisions at the correct time. Amateurs are missing one of these components more often than not.
A professional is practiced and practice prepares and hones our ability to execute. We may have the knowledge to make a prediction but without the ability to execute we fall short of the mark and failure to execute at all is where most of us live.
This is how I feel about my data analytics and prediction skills. Sometimes I am so hampered by making the correct decision, that I fail to make any decision at all.
Except in one area of my life and that is music.
When I play music (on my saxophone), I am forced to execute according to a variety of conditions. When I play in a concert band, with an arranged piece of music (all the notes written out for me) then my execution comes down to my ability to play the notes set forth in time according to the conductor. When I play Jazz, however, I am granted more freedom in what I play and I am obliged to play what I feel How I translate my "feels" to actual notes and phrases has an impact on my ability and therefore defines me in a more abstract sense. I like Greg Fishmans' term "Ketchup on Brownies". I might have the ability to play my instrument in a more structured setting but in the less constrained realm of improvisation, I am held to a higher standard of understanding and execution in an attempt to connect with the audience, which I am included. A musician that doesn't understand the musical relationships that are in place can sound like "ketchup on brownies", not necessarily wrong, but not sonicaly appealing either.
So how does this fit into data science and machine learning?
Jazz is the ability to execute the correct notes at the correct time. If my brain hasn't been trained to comprehend the chords and phrases of a piece of music then I will be unable to make the correct predictions on what notes to play during an improvised solo or ensemble section of a song.
So we must train (practice) in order to execute and we must choose the correct training methods in order to achieve a robust predictive method. This is where a practice professional data scientist uses their intuition to choose the best model to fit the problem. This ability is where my focus is today and it is as vast as the field of math itself.
Let's keep learning.
A professional has the ability to execute the correct decisions at the correct time. Amateurs are missing one of these components more often than not.
A professional is practiced and practice prepares and hones our ability to execute. We may have the knowledge to make a prediction but without the ability to execute we fall short of the mark and failure to execute at all is where most of us live.
This is how I feel about my data analytics and prediction skills. Sometimes I am so hampered by making the correct decision, that I fail to make any decision at all.
Except in one area of my life and that is music.
When I play music (on my saxophone), I am forced to execute according to a variety of conditions. When I play in a concert band, with an arranged piece of music (all the notes written out for me) then my execution comes down to my ability to play the notes set forth in time according to the conductor. When I play Jazz, however, I am granted more freedom in what I play and I am obliged to play what I feel How I translate my "feels" to actual notes and phrases has an impact on my ability and therefore defines me in a more abstract sense. I like Greg Fishmans' term "Ketchup on Brownies". I might have the ability to play my instrument in a more structured setting but in the less constrained realm of improvisation, I am held to a higher standard of understanding and execution in an attempt to connect with the audience, which I am included. A musician that doesn't understand the musical relationships that are in place can sound like "ketchup on brownies", not necessarily wrong, but not sonicaly appealing either.
So how does this fit into data science and machine learning?
Jazz is the ability to execute the correct notes at the correct time. If my brain hasn't been trained to comprehend the chords and phrases of a piece of music then I will be unable to make the correct predictions on what notes to play during an improvised solo or ensemble section of a song.
So we must train (practice) in order to execute and we must choose the correct training methods in order to achieve a robust predictive method. This is where a practice professional data scientist uses their intuition to choose the best model to fit the problem. This ability is where my focus is today and it is as vast as the field of math itself.
Let's keep learning.
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