Opinion

Is Data Science too Easy?

Look before you leap, or something like that.

Data science can be a highly engaging and interesting field to venture. With the increasing popularity that it is receiving right now, there is a huge rush to become a data scientist. The endless possibilities that big data unveils before us sure excites everyone to have a peek at what it actually entails. What does it mean to add a tag of a data scientist to one's name?

But being a data scientist is not child’s play.

Read about what’s it like to be a data scientist:

A year as a Data Scientist right after college: An honest review

🤹🌐Mix of Everything

I am not trying to discourage, but it is just that data science should not be taken on a light note. After all, one doesn’t become a doctor by just watching some videos on the profession nor does one become a lawyer by merely observing a courtroom, you cannot be entitled a position by the mere surface knowledge of it. No, you have to go through a set of guidelines and proper curriculum to pass those gates and become to be a doctor or lawyer or any professional in that matter.

But data science comes with a twist. Unlike most professions where there is a defined route, here you will find that it is quite unorganized and there is more than one way to achieve the same goal.

Here you will need the combined knowledge of various fields to have a proper understanding of it.

🔧🔨📐Just the Tools won’t Suffice

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Sure you can learn R and Hadoop and call yourself a data scientist. Learning these alone will not qualify you to be a data scientist, these are just tool kits that help you process the data.

You can compare them to screws and spanners. After all just learning to use them will not make you a mechanic.

So you must have an idea on how to use these to achieve the objectives. Then again the objectives must be clear and precise because in the world of data you are dealing with an enormous amount of it and any confusion might render the whole operation impossible.

Learn more about R and Hadoop:

R Hadoop - A perfect match for Big Data

🖊️🔢It’s all down to Mathematics

Understanding statistics is highly important if you are keen on embarking a journey in data science. Although statistics is associated with testing hypothesis using data, in data science you need to go deeper than just testing.

Here the main focus is on implications on systematic deviations from the hypothesis and the conclusions that we can derive based on these deviations.

So in short, it could be summarized that statistics and data science go hand in hand.

🍝🥗Special Ingredient

Apart from the technical knowledge, one must be able to bring these to bear in answering various business questions and generate business outcomes. It is what actually measures your rating as a data scientist.

You could imagine it as the special ingredient which makes the dish, a gourmet’s delight.

❤️💖💕Falling in Love with Imperfection

So naturally, after all this, the obvious question that pops into your mind is whether anyone is really able to become a data scientist. Yes anyone can. But the catch is, you have to be able to dive deep and understand the divergence between the reality and the prediction using mathematical models. For this, just being an expert in using the tools will not work. You have to awaken the lover within you, the lover of imperfection.

After all, no one is perfect. So let’s just say that data science is definitely for everyone.