Data is everywhere ..Every company ,industry, profession stores tons of data like if we talk about small sectors , even a shopkeeper keeps records of data and can use analytics to predict which product could fulfill customers satisfaction which helps him to increase revenue and decrease costs.
A life of data scientist is all about numbers and predicting and also to make sure what you predict is absolute correct like if am a stock broker how will I decide that the price of share will bullish or bearish ? So again its all about prediction at the end of the day.
●Saying it in a professional way : Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data.
● By 2018 the demand for data scientists are as much as 60% greater than supply. Data Science and Data Mining skills are among the top 2 job openings in most parts of the world.
●Key Functions performed by Data Scientists : Understanding the business problem , Data mining and analysis design , Descriptive and predictive analytics , Devising business strategies from the insights.
#As we earlier saw actuaries deal with data, they even serve an important role with predictive analytics by using modelling and data analysis techniques on large data sets to discover predictive patterns and relationships for business use. They use analytics extensively in their working to transform data into useful information. Therefore, there is a close proximity that actuaries have with analytics and data science fields.
●While they both revolve around data, there are certain lines drawn between these two. For instance, actuaries are found primarily in the insurance industry, primarily for risk assessment, data scientists can be found in virtually any industry. While AS covers financial modelling and studying data around it, DS covers aspects of databases, data mining, machine learning, data visualisations, and others. Time taken to be a data scientist is around 20 months , wheras in actuaries it take a minimum of 4-5 years.
●Lets take an example , there is a customer who took a car insurance , so what can you say is the probability that the customer is the reckless customer and and there is a high chance that the customer involve in a accident and will claim ? Well, we can only be able to predict this in advance and which would help insurance companies to set premiums and accomodate risks with the help of theoretical knowledge (possessed by actuaries) and by using statistical techniques of data science.
#Though there are differences between the two fields, the actuarial employers are increasingly expecting their staff to have same skill sets as data scientists. With the two fields heading towards creating blur lines between themselves, it wouldn’t be surprising to see actuaries being called data scientists in the coming future.
Sent from my Mi A1 using Actuarial Info mobile app