Laha said the boundaries of analytics and data science are not water-tight, and there is significant overlap. While analytics focus on modelling data using statistical and machine learning tools and techniques, data science focuses on efficient storage/retrieval/processing of data using tools and methods drawn mainly from computer science, he explained.
Laha, who specialises in advanced data analytics, quality management, and risk modelling, said, analytics essentially aids in decision-making. Analytics provides a systematic way to choose the best course of action from the available alternatives.
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Laha said analytics is used in various domains, starting from business management to healthcare and engineering. “These three are the major domains of application for analytics techniques,” he added.
Further, some of the emerging domains include sports, entertainment, law, education, politics etc.
Role of Big Data
Once you grasp the basics of analytics, the next logical step is to understand the ‘how’ bit of analytics and the use of Big Data tools. Laha said Big Data has many facets and differs in three aspects — volume, variety and velocity.
He said many exciting applications need massive datasets for building good models, and in some domains, data is scarce OR costly to acquire. “Good models need to be built on small data in such situations,” said Laha.
Data analytics for problem solving
Analysts directly impact the bottom line of the business. Laha cited few real-world examples across industries and how analytics can be used to solve them.
- Telecom company: Is this customer going to leave me and go to my competitor?
- Diagnostic lab: Does this chest x-ray exhibit abnormalities?
- Bank: Is this customer going to default in payment of the loan?
- Hospital: How long will this patient stay in the hospital?
- Automobile company: Will this vehicle need emergency servicing within the next three months?
- Sports: What would be the size of the television audience of the India-Australia T-20 cricket match?
- Entertainment: What would be the box-office collection of an upcoming action film starring Tom Cruise and Scarlett Johansson?
Career in analytics & data science
Analytics and data science is helping businesses solve significant problems. The domain is rapidly progressing, and so is the remuneration for analysts and data science professionals.
“After five to six years of experience, candidates also have opportunities to interact with (and occasionally advise) the organisation’s top management on tactical/strategic decisions,” said Laha.
For that, individuals need to have good knowledge in statistics and machine learning, proficiency in programming and handling databases, along with a reasonable understanding of the domain.
Besides these, the candidates should understand calculus, linear algebra, probability, statistical inference and regression modelling. Proficiency in R and Python are highly desirable. “These are some of the skills needed for a career in analytics,” said Laha.
Today, candidates are expected to know algorithm design, programming, database management and cybersecurity, alongside knowledge in basic statistics and domain expertise.
Analytics and data science are evolving rapidly, and candidates interested in pursuing a career in this space should update their skills to stay relevant.
Also, the nature of data is changing quickly, and there is more unstructured data than structured data. “New tools and methods are being developed at a very rapid pace — a very active field of research is happening globally,” said Laha, stating learn-unlearn-relearn is the only viable parameter for long-term career success.