Data science has been in demand the market for quite some time, it has even been labelled the best career of the 21st century. Data Science is a multi-disciplinary study, and heavily utilizes scientific methodologies. Data Science exists at the junction of statistics, business knowledge & technical skills. It is a way to extract important information from structured & unstructured data. Data Science also focuses heavily on being able to derive informed decisions and strategic moves from data often termed as insights. Insights are one of the biggest products of practicing data science and offer numerous advantages. This makes statistics one of the biggest parts of data science, as it stands as a fundamental part of the approach. When trying to make sense of data, statistics is an invaluable tool as it wrangles the data in an approachable manner.

It is critical to understand the differences between a Data Analyst and a Machine Learning engineer. In simplest form, the key distinction has to do with the end goal. As a Data Analyst, we are analyzing data in order to tell a story, and to produce actionable insights. The emphasis is on dissemination like charts, models, visualizations. The analysis is performed and presented by human beings, to other human beings who may then go on to make business decisions based on what has been presented. This is especially important to note that audience for our output is human.

As a Machine Learning engineer, on the other hand, our final output is working software (not the analyses or visualizations that you may have to create along the way), and our audience for this output often consists of other software components that run autonomously with minimal human supervision. The intelligence is still meant to be actionable, but in the Machine Learning model, the decisions are being made by machines and they affect how a product or service behaves.

This is why the software engineering skill set is so important to a career in Machine Learning. Machine learning is the natural progression of artificial intelligence using extensive data. In machine learning, we develop computer programs which automatically learn from the data set available to them and do not need to be explicitly programmed.

An example of Machine learning (ML) would be the kind that Uber or Ola use on the ride sharing apps. The app automatically estimates the cost of our ride, the distance between our locations and also the surge pricing depending on various factors. All these are possible due to machine learning capability of the app with the availability of different data like users in the area, past price trends etc.


The world is going through what is popularly called digital transformation and this is revolutionizing the way we live, the way we communicate, consume, use time, and work. A lot of what we know today as work is being taken over by progressively intelligent machines. A career in data science and machine learning makes sure you are also part of the revolution.

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Who is an Actuary ?

The future is an uncertain place full of risk, but where there’s risk there’s also opportunity. The role of an actuary is to help companies manage and reduce the risks facing their business. Using numbers, facts and careful analysis, actuaries evaluate the likelihood of future events in an effort to avoid the “worst case” scenario from occurring.
When risk cannot be avoided, actuaries offer creative ways to reduce the likelihood that undesirable events will occur. Being an actuary is one of the highest paid professions.
It's a career where you can use your talents to solve real world problems. It's a commitment to uphold certain standards of performance, professionalism and ethics. It's a qualification you can take anywhere in the world.
You could help solve problems in business - like pricing products or managing risk. Or find solutions to social and economic dilemmas.
You are right into the Actuarial profession if
You would like to "earn while you learn."
You want a highly competitive salary and excellent benefits.
You have a mathematical bent of mind.
You want a career that is dynamic and challenging.
You want a career with many opportunities that will provide you with skills that are transferrable across multiple industries as mentioned below.

What to study in Actuarial Science:

Core Principles (CP)

CS1:Actuarial Statistics
( CS1A Theoretical Exam + CS1B Computer-based Exam using 'R Programming')

CS2:Risk Modelling and Survival Analysis
( CS2A Theoretical Exam + CS2B Computer-based Exam using 'R Programming')

CM1:Actuarial Mathematics
(Actuarial Mathematics CM1A Theoretical Exam + Actuarial Mathematics CM1B Computer-based Exam using 'Excel')

CM2:Loss Reserving and Financial Engineering
(CM2A Theoretical Exam + CM2B Computer-based Exam using 'Excel')

CB1:Business Finance

CB2:Business Economics

CB3:Business Management

Core Practices (CP)

CP1:Actuarial Practice
CP2:Modelling Practice
CP3:Communications Practice

Specialist Principles (SP)

SP0: Masters Level Thesis
SP1: Health and Care
SP2: Life Insurance
SP4: Pensions
SP5: Investment and Finance
SP6: Financial Derivatives
SP7: General Insurance: Reserving
SP8: General Insurance Pricing
SP9: Enterprise Risk Management
Two of these SP modules have to be passed to be eligible to qualify as a Fellow. You do not need to take any SP subjects to qualify as an Associate.

Specialist Advanced (SA)

SA0: Research Masters Thesis
SA1: Health and Care
SA2: Life Insurance
SA3: General Insurance
SA4: Pensions
SA7: Investment and Finance
One of these subjects has to be passed to be eligible to qualify as a Fellow. You do not need to take any SA subjects to qualify as an Associate.


Personal and Professional Development (PPD) is the new requirement which applies to all learners.PPD is the replacement program for work-based skills (WBS). This is a mandatory requirement for members of IFOA.

Work of an Actuary

As an actuary, you have to use mathematical equations, statistics and financial theories to determine the risk and uncertainty of involved financial costs. You have to assess risks and help the company to take measures to minimise the risk.

Main Areas of Practice

Life Insurance
General Insurance
Health Insurance
Finance and Investment
Enterprise Risk Management
Predictive Analytics
Statistical Modeller
Quant Analyst
Typical work

Analyse the events and its risks that can increase the economic costs for the company; for instance, untimely death or a natural disaster will cause an insurance company to pay the insurance amount to the nominee and this pre-mature payment can cause losses to the insurance company.
Design, test and implement various business strategies like pension plans and insurance investments to maximize profit and minimize losses. The actuary has to create in-depth reports containing charts and tables to explain the business strategies and its benefits.
In investment, actuaries are involved in a range of work such as: pricing financial derivatives, working in fund management, or working in quantative investment research. Often investment actuaries work in fields where their understanding of insurance or pension liabilities helps them to manage the investment of the corresponding assets.
Actuarial consultancies offer a whole range of services to their clients on issues such as acquisitions, mergers, corporate recovery and financing capital projects. Many also offer advice to employers and trustees who run occupational pension schemes. v Actuaries 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. The actuarial profession has been actively advancing the use of predictive analytics methods in its work.

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Disclaimer : The information contained in this website is for general information purposes only. We have no association with any Actuarial Society including IAI or IFOA and we don’t represent any Organisation. We support, help and guide students to get success in Actuarial Exams. This is completely a support centre. We use our own teaching methods for the purpose.