DATA SCIENCE AND ARTIFICIAL INTELLIGENCE
As broad and complicated these two terms seem, don’t worry it’s nothing to do with Skynet and the rise of machines; Doom’s day is not here yet! Then what exactly is the objective of having sciences develop a field so complex such as Data Science itself? Let’s find out!To put it in simpler words, a Data Scientist plays with data and information in order to open a whole new world of possibilities, predictions and different outcomes. AI, on the other hand, runs through these different possibilities; records results and paves an achievable path to reach the most desired outcome. Must say, technology is pretty enticing, right?!
Without any further ado, let’s dig a little deeper in the relevance of these to fields and find out how they are impacting our world today and, given that no AI unit turns into a terminator, Data Sciences and Artificial intelligence might even have a role in revolutionizing the age of internet, i.e., the 21st century.
What can we expect from DATA SCIENCE?
The work of a Data Scientist is to understand goals of a business or company and determine ways to achieve goals, while dealing with and extracting the data that determines the market patterns. So, yeah, with this data you might as well be able to predict the future trends and practices. Excited already? Hold your horses, there’s a lot more to come further. Not to forget, the data might differ from time to time, the procedure of extracting and processing it still remains the same and involves extensive re- search, asking the right questions, dealing with data, designing modules, making changes according to feedback and repeating the process to solve a new problem. I need someone to wipe the sweat off my forehead.
Implications of Data Science.
Real life can be hard and without the existence of Data Science, trust me it can be very hard. In fact, Data Science has been deemed effective in tackling many problems faced in real life and has gradually been adopted across different industries in order to boost decision-making. While use of computers has increased from business to personal operations there is a significant high demand to learn and work on the patterns on which a human works and
behaves. Right there, that’s how interesting Data Science can be. Let’s see which all industries follow the trend of Data Science to make their lives easier and their work more fruitful.
● The Gaming industry has opted for the use of data science for a very long time. Games like FIFA, PES and MLB have highly been dependent on pure data and algorithms for players. Aren’t they the players of Sciences?!
● Sports industry has also copied the model of Data Science in order to get clear-cut information about viewership of different competitions like Cricket and Football World Cups, club competitions and many other sports. So now, Data science has penetrated its roots into sports as well.
● Data Science has made a major impact in Healthcare during COVID-19 outbreak and before that by detecting similar patterns and finding new solutions to tackle these situations. So, it did come to our aid when we needed it the most. What could be a better use of a Science than this?!
● Are you a binge watcher too? Well, guess what, the most visible use of Data Science is done by different OTT platforms. The recommendation system of your account is not really just random; it’s based on your previous watch and search history, after
analyzing the previous information Netflix and Amazon Prime suggests the same genre or category of shows and movies. Data Science’s contribution to Actuarial Science. Now, how can you leave a Science that takes you to the future, separated from the ones who tell you about the future? Seems like an absurd idea, doesn’t it? Although there are some differences between Data Science and Actuary but at times both these fields of work go together with each other from these similarities and resemblance came out the term Actuarial Data Scientist. Actuarial Data Scientist is an extension of Actuarial Science which uses machine learning techniques in problem such as pricing, claims and lapse predictions.
Data Science techniques can be used in the following areas of Actuarial Science
2) Loss and Risk Predictions.
3) Detecting fraud claims and Modeling in life insurance.