Actuarial Science Vs Data Science

In recent years, we've experienced new ways to gain insight from data through Data Science. We are able to understand and analyze actual phenomena through the use of techniques and theories drawn from many fields within the context of Mathematics, Statistics, Computer Science and Information Science. A question arises, Is Data Science related to Actuarial Science? And if so, How?
Follow up this interview to understand more!

An interview with Sylvera Justine, Data Analyst in Tanzania.

1. What is Data Science?

"According to Wikipedia, Data Science is a multidisciplinary field that uses scientific methods, processes and algorithms to extract useful information, patterns, insights from data. Data Science has become a well sought skill with someone required to have mathematics, statistics, business acumen, analytical and programming skills. It is among the highest paying jobs  as ranked by Glassdoor in America."

2. Tell us about your journey to become a Data Analyst.

"To provide context, I studied BSc in Actuarial Science at the University of Dar es Salaam, during this time, I was exposed to C++  and got interested in programming languages. However, I had limited knowledge and the massive classwork made it hard for me to research further. In my second year, I heard about a postgraduate degree in Mathematics from AIMS (African Institute for Mathematical Sciences). I applied with the sole aim of being exposed to more Mathematics and programming languages. I joined a Statistical Machine Learning and Data Science class taught by Prof Fokoue (RIT)  that opened my eyes to the world of Big Data. I have never been at rest since then."

3. What is the relationship between Actuarial Science and Data Science?

"Having being exposed to the Actuarial Science and Data Science fields, In my opinion there is a close knit relationship between the two. Both fields use mathematics and statistical tools to turn data into useful insights for decision making in different industries. Both fields require analytical skills to discern, interpret and find patterns in data to make informed decisions. While Actuarial Science is domain specific, relating to financial modelling, risk management Data Science is about finding relationships in data and drawing insights which is not field specific."

4. What interests you the most about Data Science?

"My interests in Data Science know no bounds. I find Machine Learning, Deep Learning, Data Mining and Data Visualization more interesting. I like to venture into a data science problem like a detective finding evidence for a crime."

5. Who is a Data Analyst? And how does he/she differ from a Data Scientist.

" A Data Analyst is someone who collects, processes, clean, analyze and interpret data using different mathematical and statistical analysis tools. A Data Scientist is expected to have more skills such as structuring business problems into questions, data engineering skills, data mining and strong mathematical skills. In my views,  a data analyst is a subset of a data scientist."

6. Do you think Data Science will replace the demand for Actuaries or rather compliment it?

" No, Data Science will not replace Actuarial Science. Even though they share skills, they are not substitutes rather complements to solving problems in different industries."

7. What is the current scope of Data Science in Tanzania and how do you see it in the near future?

"Data Science in Tanzania, like most places is still a new field. The skills set are rare and most companies do not have a clear understanding of the data scientist roles. Most of the data analysis works are done using SPSS, Stata and Excel. The future is promising as different institutes like the University of Dar es Salaam are training people to become experts in the area."

8. What's your advise to current actuarial students?

 "Looking back when I was in the University, I would say, learning is the best advice one can get. The internet is full of resources that one needs to acquire different skills. As an Actuarial Science student, they should learn to love working with data, drawing insights, finding patterns in order to build robust and data savvy models to solve problems in insurance, banking, investment and other actuarial prominent fields. "

Prepared by John Kitwika & Muhanad Suleiman