Data Science is all about applying quantitative and analytical models to the information on hand and come back with an outcome which is of business value. This has been happening for years now and has improved immensely both in quality and quantity.
The below mentioned process is now accelerated due to some factors which have acted like catalysts:
- Cost-effective, reliable access to data storage, in other terms the evolution virtual storage and cloud.
- Open source tools with the ability to combine data inputs and computation coupled with statistical methods. In short, the evolution of practices around Artificial Intelligence and Machine Learning
- Connected devices for commercial and personal use. The evolution of smart wearables, IoT and IIoT.
- Data driven computation for automated decision support or the evolution of Robotic Process Automation (RPA).
The above developments have been fairly recent and are still evolving. We could see at least 20 years for these to stabilise.
With the advent of these areas, the need for people who can program or code while understanding statistics has arisen. The need is also for people with the skill and ability to research on newer and much-improved technologies for data analytics and storage.
So, what is in it for me and can I pursue a career in Data Science?
Forbes and IBM in a research have given some inputs:
- The number of data science professionals in the US alone would be around 700,000 by 2020.
- This is expected to grow at an average rate of 28% year on year.
- The average income for a Data Scientist is projected to reach USD 145,000 by 2020 from the current average of USD 90,000 in 2018.
For more detailed inputs you may want to read the complete article at Demand for Data Scientists.
McKinsey reports on Data Science give some interesting facts:
- The worldwide requirement for Data Science and Analytics professionals is forecasted to be 4 million by 2020.
- At the current rate of learning and readiness world will still have a shortage in demand by 30% till 2025.
So, the future holds good, but where is the application of Data Science?
The application of Data Science & Analytics is increasing across multiple industries. For example, one of the foremost applications is being focused on understanding consumer behavior and providing insights for footprint management. The leaders among these are Facebook, Google, and Amazon.
While most of us using a smartphone are now used to saying one of these:
“Ok Google”, “Hello Cortana” or “Hi Siri”.
And your phone wakes up with a response that is most personalized to you. Your phone today knows more than you know. Smartphones and other connected devices know your daily schedules, business appointments, shopping patterns and personal preferences.
The bunch of apps always surprise you by showing options and shopping choices even before you ask them for one.
Have you ever noticed that once you land on Amazon or eBay on your laptop or mobile device you immediately start getting prompts about products that you have searched for on Google or other marketplaces?
This development is only made possible with research and development using data, Artificial Intelligence & Machine Learning. While these are more personal and consumer-specific, another pertinent point to be considered is the enormous contribution of data large scale industrial and global initiatives.
Data science and analytics is paving ways to prominent research and product areas as below:
- Enhanced Drug Development
- Water Resource Management
- Connected Vehicles and Road Safety
- Safe Living and Urban Development
- Faster & Reliable Transportation
All this and more, while we are yet to scrape the surface, many a possibilities are yet to be discovered.
Its prevailing to see the trend of data science evolving into a daily workplace skill rather than just being limited to one department or function in an organization. Slowly, but surely every function may it be HR, Sales, Finance, Supply Chain will surely be exposed and necessarily start working on being a Data Scientist.
So, if you are willing to have a look in and want to be called a Data Scientist. Just begin and be assured that you can proudly proclaim to be a qualified Data Scientist for at least next few decades.