Data Science and the Different Roles of a Data Scientist

The Legend:

Chandler Bing's job was statistical analysis and data reconfiguration. It was a boring job. He was dead inside. He was just a regular employee in a huge organization - ill-fitted, unhappy. Apparently, statistical analysis and data reconfiguration, made his life boring.
The thing is, it was not the job in itself that set him off. He was witty, funny and dynamic, he thought it would be better if he tried something where he can use all that qualities.

Present day:

Chandler Bing was a Data Scientist before it got its present name. Statistical analysis and data reconfiguration is just technical jargon now. The boring desk job around computers, spreadsheets, graphs, data sets, Data Science has lost all of these characterizations. In fact, it did a good job growing up. Data science is presently the "sexiest job" on the planet, of the 21st century. Harvard University gave this label.

To put it simple, the world is data driven. It's the oil of the 21st century. We don't have to go over the same "Haven't you noticed that the search engines give you results and ads on the basis of your previous searches?" again. The practical uses and results, you can just look around to see them.  Be it the medical world, or the engineering, HR .. data science can make you smarter.

Its right there.

But then what's not there? Today, lets take a look at that.

How do the search engines collect and store all these data, how do they use the collected data to form opinions and predictions.? Who does all this work?

In every multi national organization, these activities are performed by a group of creative and highly skilled individuals, spread over different areas of expertise and skill sets, collectively known as the Data Science team.

Since the data science or data analytics, (your choice on the name, you'll see why), is still an evolving sphere, the job roles are more or less loosely defined; even within the same industries. So it would be difficult to enumerate the activities on roles played by a person as a data scientist/engineer/analyst. (again, the choice of name is yours.)

An easier thing to do is to list the levels of activities in the whole process.

Note: Data Science involves creativity, critical thinking, logical reasoning. Data Science is ArT.

The Data Science Project:

1. Identifying the business problem

Resolving the business problem is the primary objective of the whole project, the analyst will have to get into talks with the business managers/strategists and gather what kind of information will aid them in making an informed decision. Discussions, meetings.... it goes on.

2. Acquiring the Data.

Logs, Databases, web domains (to name a few) are all sources for data. The gatherer will also be keen on the genuineness and the efficiency of the data.

3. Data Preparation

The most time consuming of them all. Cleaning, removing the duplicates, seek missing and misspelled attributes and values etc. are all a gargantuan task. This is something that's actually capable of getting you bored. It's repetitive, slow and exasperating. This is where you have to be a neat freak, not just clean, MONICA CLEAN.!


4. EDA & Data modelling

Exploratory Data Analysis helps the professional in determining the variables to be employed in a data model. The model created is then tested on various data sets using Python, or R (quite the buzzwords these days)

5. Visualization and Communication

The artistic side. The professional has a huge canvas. He can draw and paint his findings. Tools like PowerBi and tableau are major aides here.

Note: The activities mentioned are generic and in a real data science project, they will be further segregated.

Epilogue:

The data science jobs are too damn attractive for the reasons:

1. Every role of a data scientist is a treat for an inquisitive mind.
2. The pay is good. (We're talking six figures here, Just, to call sexy huh?)





PS.  No one said the life of a data scientist is easy.