Data Science – Myth vs. Reality

In this age of easy access to high speed networks, the volume of information we come across daily through different media is enormous, but the reality is what comes across information to us most of the time has a lot of clutter added to it. People easily swipe through different sources of information blindly without verifying whether the source is trustworthy or not. Buzzwords which are totally unrelated to the topic are often used to attract people to the (mis)information, which most often ends up creating confusion among readers. This confusion and misrepresentation of information under the cover of buzzwords have a convoluted effect on a reader’s decision-making capability and takes them on a route altogether different from what they intended to. This trend can be highly visible currently in the field of Data Science as well.

As Data Science evolved into the most demanding and sort of a “talk of the town” profession from where it was a decade ago, it has been surrounded by a lot of myths and misunderstandings. Analyttica Datalab here highlights and dispels few myths, both for data scientists as well as for organisations. These might prove to be helpful for aspirants looking forward to be a part of it. 
 

#1

Myth: Data science is exclusively for experts in Statistics and Mathematics.

Reality: Cross-disciplinary experts with curiosity to learn new things make better data scientists. 
Firstly, we would want you to understand that Data Science is not proprietary to some limited disciplines. It can be looked at like huge square in the middle of a crowded city where paths from multiple disciplines such as Mathematics, Statistics, Computer Science and Programming, Data Modeling, Visualization, Technology, Domain knowledge etc. pass through it. 

While an expert in statistics or mathematics may get a good head start, cross-disciplinary experts bring with them the advantage of moving parallelly through different topics as a result of their past experiences. 
As Data Science in a short span has expanded exponentially, there is a lot that one can learn and explore. It is the curiosity to explore and ability to drive things in one’s favour to reach an ultimate objective is what makes a good data scientist. You need to catch up with the pace of rapid developments in this field and translate it into getting a competitive edge over others.
So, all together if you have the right intent to learn, explore, and excel then Data Science will be a cakewalk for you.
 

#2

Myth: Data Science is a Science.

Reality: It’s a mix of Art and Science.
From the outset, it might appear data science is about using the scientific method to solve practical problems in a business setting. The problems can be of the sort: “what steps can we take to reduce customer churn by 50%?” or “how much of our inventory losses are due to fraud, and how can we reduce that?” 

While tackling these questions, it is important to know that knowledge about Statistical learning, or machine learning methods alone are not sufficient. Along with it you also need different skills, experience, certain degree of logic, reasoning and storytelling (you read it right!!!) capabilities.

This where Data science stands out differently as a practice and not as a specific skill.

A data science project, much like software development has a lifecycle. The science aspect comes through when it is necessary to write code to collect and clean data, run traditional statistical analysis to verify that your data can answer a given question, build predictive machine learning models, visualize the data in creative and expressive ways, and build a data story to explain the results to the clients who are eager to know what you’ve discovered. 

But the art aspect of data sciences shines from the beginning through your creative thinking of tackling a problem and devising a solution, and further in many ways when you confidently weigh the subjective benefits of a decision with the quantitative benefits of techniques to make the right choice based on your experience. The decision maybe with respect to what statistical tool you choose or a format of the output particularly preferred by certain businesses or the underlying assumptions that you make while approaching a business problem. 

Transforming subjective logic and creative reasoning into a tangible outcome with the help of Statistical and machine learning methods shows that Data science is equally blended by art and science.

Analyttica TreasureHunt is one such platform that helps you in evolving your creative and scientific aspects of Data Science by a patented simulation-based learning approach.

#3

Myth: It is hard to find Data Science resource for your organisation.

Reality: Anyone can learn and be a good Data Scientist. 
The reality is, every data scientist, new or experienced, certified or not, learns on the job. This applies to those with PhDs in Math or Statistics as well. 

Resources outside the organisation have always been scarce or expensive. Instead of investing time and money in hiring external talent, a wise strategy for an organisation would be to identify cross-disciplinary experts, who have an analytical bent of mind and help them learn data science methods by providing appropriate resources. The best place to start is to look at dedicated and disciplined software development teams. These teams specialize in providing business solutions that deliver value, so pivoting a team to focus on data science would not be an unreasonable ask. 

With well-structured and hands-on learning modules and guidance from a community of aspiring data scientists, one can become a data scientist in 3-6 months. You can find out more information, here

It is important to remember that data scientists constantly interface with departments. An existing team would have already built the necessary rapport to bypass the inherent bureaucracy in all departments and move work at a fast clip. Additionally, grasping the width and depth of the business context would be far easier for your existing team than that for a new one.

Looking inward is one way of building a good data sciences talent pool.
 

#4

Myth: Data Science is all about complex coding using different tools.

Reality: Data Science is all about understanding and solving problems. 
Having some strong coding skills might be advantageous but it is not a necessary condition. What is more important is your ability to frame the business problem into actionable insights, collecting good data and understanding it etc. Coding just becomes a small part of your whole journey and you can survive with beginner to intermediate level skills of coding. 

Though a Data scientist needs to have hard skills such as statistics and coding at his disposal, but his day to day job also requires the less-tangible hard skills the such as ability to look at data and understand bias, problem solving with messy data mostly created by third parties, validating findings, working in a team, and communicating effectively to present results in simpler terms.

As long as you enjoy playing with data, ask and answer important questions, translate your findings into a data story you are going to find Data Science fulfilling. 

We hope the above points might have reduced the clutter and added more clarity to the way you perceived data science. 

If you have more questions on implementation of data science projects, best practices or resources, please write to us at info@analyttica.com.

 

This blog was first published here.
Author: Nikhil Nene, Principal Analyst, Client Solutions, Analyttica Datalab Pvt. Ltd

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