Catch 22: Machine Learning, Data Science and Experience Part 1

Data Scientist, is a new type of work, the way organisations, individuals define, it mostly is wrong. Data Scientist is not an analyst, is not a programmer, not a report writer, or not even a report gazer.

Data Scientists are paid for thinking about business processes. Most of the times, to understand the existing processes, influences and impacts of various internal (Org restructures, changes etc..) and external events (Strikes, floods, extreme cold, extreme hot, falls, earth quakes or absence of these), eventually predicting influence on internal processes based on external events, which used to be a very highly difficult tedious task before the data science

For example:

Rise or Fall of profits of an Insurance company based on external events like a hurricane or Tsunami or a large snow storm, which caused a 75 Cars pile up on the most important motorway (Interstate) in a specific country.

 Working as a data scientist is not necessarily what people think. A profession that some people think as “sexy” is, more often, a difficult job involving long hours, tight budgets, limited staff, daunting tasks, shifting requirements, endless meetings, and "inflated expectations" .

"Great" Inflated Expectations:

Most of the people who want to become "Data Scientists" and most organisations who wants to hire a data scientist often misunderstand the mandate or definition of the role. If I can explain with an analogy, a pathologist or equivalent, based on doctors orders diagnoses the ailment through various tests and will handover the patient to a specific specialist doctor, based on the detected health issues, if we use the same analogy, a data scientist like a pathologist, based on the orders of business stakeholders, identify the internal and external events, processes etc.. influencing or impacting the business, then will handover the findings to the responsible stakeholders for appropriate decisions and actions.

"The Data Scientist will not change anything in the enterprise", the Data Scientist only will "Advise the business", what to do and what will happen if , the stakeholders take actions in different scenarios

Technology Trigger:

Big Data, enabled an alternative thought process, where we can analyse not only the unstructured data, but also semi structured and unstructured data, in large volumes, coming very fast and with wide ranging information, which will "try" to give you a near 360° view of the business, provided the stakeholders know what they are doing. The explosion of Big Data concepts and subsequent technological implementations enabled the thinkers to use this technology to aid their thinking (Data Science is never a thinking process on its own, but just an aid to the thinking process). Just like a cardiologist (Data Scientist), will not be an expert in understanding the human heart (business processes), a cardiologist, just because of expertise in using the Colour Doppler, which just enables him/her to see the heart without cutting open (Stopping) a living person (business processes) and identifies ailments (Issues and problems) and prescribes appropriate medication or surgery(remedies, actions, reorganisation).

In the second part, I will explain how to gain experience in Data Science without really working in any data science project in an organisation and how to claim that experience, without the need to create a fake experience