Data science is proving to be a paradigm-shifting technology in today’s world. Data Science, being a domain agnostic filed, has seen implementation in never-thought-before areas, opening up a plethora of existing research possibilities.
Which brings into question, what is Data Science?
Data science is the art of making data-driven decisions. So what are the decisions? And why should they be data-driven? Decisions are the choices we make, the course of action that we undertake during our day-to-day activities. Now, I don’t want my decisions to be driven by my prejudices or my emotions, neither do I want my biases to influence my decisions because that may not always lead to a good result.
For example, a biased movie Director might cast a wrong actor for a character in their movie; a biased Judge may pass polarised judgements. Angry and aggressive drivers may find themselves in an accident.
So rather, I want my decisions to be influenced by factual, true and unbiased data. That is our best shot at achieving the optimal results. Hence, the need for Data-Driven Decisions, ergo Data Science.
Finance (more aptly Fintech) is using Data Science to predict future stock prices, detecting potentially fraudulent transactions, building better customer support, identifying bad loans, etc. Healthcare industry has been using Data Science to identify intricate patterns demonstrated by different diseases, manufacturing better drugs, identifying diseases at their early stage etc. E-commerce uses Data science for customer segmentation, offering customised discounts, providing recommendations, predicting sales and volume, etc. Logistics industry uses Data Science to design optimal paths for their delivery executives, resolve customer address issues, efficient usage of the different means of transportation, etc.
When you talk to your voice-assistant Alexa or Siri, they are the implementation of Natural language processing, applications of Data-Science. Same goes for any chat-bots you may have interacted with on apps or service-based websites. When Netflix shows you some recommendations or when Facebook & Instagram give you friend suggestions; whether it is Amazon/Flipkart with their ‘People who bought this also bought’ sections or when Gmail provides sentence completion when typing emails; when LinkedIn suggests message replies or when your Email/ SMS app filters your Personal, Transactional & Spam messages: remember there’s Data Science working under the hood.
So how does Data Science do all this?
Data Science is a field of Mathematics and Statistics that does this all by looking at data. It’s not a magic wand, just a smart way of identifying patterns from the data. Now, here’s the problem we have a huge amount of data. Huge! So much so that our existing hardware too sometimes is incapable of handling. Or in other words, our conventional computing devices would take too much time to solve these problems.
So companies like Nvidia, Google etc are coming up with specialized hardware such as TPUs (Tensor Processing Unit) to make things much faster. Even GPUs are no longer considered mandatory only for gaming and such. Infact, Parallel Computations offered by GPUs make them extremely important in machines that work on Data Science projects.
Google has started providing free cloud platforms with free computational power to drive-up Data Science Projects amongst end-users. One such service is Google Colab. Amazon AWS is also one such platform where one can build predictive models, create chatbots, etc amongst other plethora of services.
This peaking interest towards Data Science, coupled with recent technological advancements in hardware, plus some amazing algorithms being given by Mathematicians and Statisticians, is really bringing Data Science to a level where it has started to overshadow other fields in technology.
The fact that Data Science is a domain agnostic field, makes it even more interesting, making it permeate almost every walk of life. You and I (for example) could use Data Science to analyse our sleeping patterns and bodily functions based upon the data provided by Fitness Bands; or we could use Data Science to design an optimal Investment plan (Mutual Funds, Stocks, etc) for ourselves to maximise returns on our investment. In my personal opinion, we are going to see a lot of implementations of Data Science and Artificial Intelligence, whether it may be tailored advertisements shown to us or a household robot to take care of use (still far-fetched).
This is just the tip of the iceberg, some prominent examples of how Data Science and AI is working and already trying to make a better world for us. Still, it’s just the beginning. Data Science and AI are still at the nascent stages of their evolutionary scale, we call it Narrow AI. The next stages are General AI and Super AI (it will take us a lot of time to reach here).
Data Science is rapidly spreading in every field – from manufacturing household electric bulbs to driverless cars. Innovation in Hardware, our own improved understanding of the human brain (that we try to replicate in AI) and continual improvements in algorithms, is accelerating adoption of Data Science. Technology will, in the future, know and understand us humans, even better than we ourself do.
And hopefully, when it does, we might be able to delegate our boring chores to the machine. If we could trust the machines enough to let them drive us places, I think the possibilities of what AI could do for us are pretty exciting!
