While completing a course on UDEMY (Another one), namely, Programming 101; I encountered some new jargon. Namely: primitive data, nouns, adjectives, verbs, variables, constants, functions, parameters, arguments, objects, arrays, elements, hierarchy and operators.
This got me thinking of Jeremy Achin and his theory about fake data scientist out there. His main insight about this axiom of his was that on the road to becoming a data scientist; the road to follow is as follow: Statistics, Programming, Algorithm, Practical Knowledge leading to Real World Experience.
However, the learning path can be “Shortcut-ed” by skipping the three first stages and get to Practical Knowledge and Real World Experience. This was materialized by his DataRobot Project. Conclusion, pondering is getting real!
I must admit that meeting this new jargon was at first a bit confusing. But, while proceeding with the course I came to consider; that most of it was in my everyday life under other common names. On the other hand, some words were already in my vocabulary and were being used on a daily basis without me even noticing this dormant knowledge.
In a nutshell; this jargon could be simply explained. Primitive data are simply data sets. Nouns are entities names. Adjectives are entities descriptions. Verbs are entities related actions. Constant and variables are numbers behaving as their labels suggest. Functions are a set of instructions. Parameters are properties. Arguments are methodologies. Objects and arrays are containers of data namely a string. Elements are the data stored in objects or arrays. Hierarchy is the data classification order. Operators are symbols used to undergo actions.
So, with this point considered; all this jargon got simple and my pondering got simpler too.
If I may quote Dr.Eric Thomas:
“Things take time and we should give them the required time.”
Hence, I could have followed the simpler path revealed by Jeremy Achin. However; I would like to not deceived Jeffrey Hammerbacher by being a pseudo-data scientist who have practical knowledge and uses it only to solve simple issues like: increasing clicks on an add. Thus, I will go the hard way and will follow the track as it should be: Statistics, Programming, Algorithm, Practical Knowledge leading to Real World Experience.
Indeed, although I master statistics and that I do have some practical knowledge while tackling real world experience; I committed myself to complete the loop. Thus, I need to be able to do programming.
I want to do Python, I want to do R, I want to apply more SQL, I want do play with Hadoop, I want to create apps, I want to create algorithms and even brag about this knowledge if needed!
Although knowing it will be hard; I must complete this as I do not want to be an incomplete Data Scientist. Reaching a goal do takes time; but dedication is the key and as goes the saying:
“Calm seas never made a skilled sailor!”