One day Ankita, pregnant for six months now, was browsing the Internet while sipping a cup of coffee. Among the litany of baby product advertisements, which she was getting used to, one particularly caught her eye. It was an application which claimed that it could tell how her baby would look like—right from the day they are born to their old age. She was amazed.
Such applications of AI are very common nowadays. In fact, your entire life rotates around AI, ML, and Analytics. You wake up and glance at your mobile: the facial recognition algorithm identifies you and unlocks your phone. You ask Alexa to turn on the light and play some music. Realizing who is speaking using voice recognition technology, Alexa plays from the personalized playlist. You talk to the remote of your SmartTV and it starts playing your favorite TV show right from where you have left it. It uses Analytics to segment you and on the basis of viewing history of others who belong to your segment, it even suggests some other shows you may like. Once done, you book a cab to go to office; you know the prices fluctuate due to demand. Google guides the driver of the cab to the destination. At the office, you mark your attendance by scanning your retina and log in to your system by scanning your fingerprint. I can go on and on and these are the very basics. We haven’t even come to the world of Social Media or the post-COVID transformations.
While some organizations have been exploiting this new-age technology from their very inception, a large number are now jumping on the bandwagon to transform the way they do business and derive competitive advantage from data. And both are hiring for such roles from business schools and engineering colleges in droves. It is, in fact, becoming so popular that a few institutes have started offering specialized courses in Analytics and Data Science.
What Do You Mean by Data Analytics, Data Science, Machine Learning, and Artificial Intelligence?
We all use Google, or Bing, for our search engine needs. On the basis of the earlier searches that you have done, your online activities, your demographics, etc., these search engines can create a profile of you and tag you to a customer segment.
Let’s say you are searching for a location you want to visit. As soon as you start typing the name of your destination, the engine would start prompting you various options. Once you have chosen your destination, the system will then show you the fastest route to that place.
Simply put, Data Analytics uses historical data on which analysis can be performed to derive actionable insights. It is used to answer questions that have been asked in a business context. In this case, the profile segmentation shows Analytics at work. There are four types of Data Analytics.
Artificial Intelligence is actually computers carrying out tasks which would otherwise require human judgement. In the instance cited earlier, it is about finding the fastest route between two points. It does not necessarily require any historical data or a predictive model; it just takes into consideration the distance between the two points and tells you the way without needing any input from you.
Machine Learning, a subset of Artificial Intelligence, does not require any human intervention but predicts the outcome of any event on the basis of the historical data and keeps learning as we feed more data into it, i.e., it is self-learning. In this case, Machine Learning (deep learning, a subset of Machine Learning which uses neural networks—a technique to replicate human brain) is behind the search engine predicting your destination. There are three types of learning problems in Machine Learning.
Data Science deals with the study of data for uncovering patterns, generating actionable business insights, predicting future events, amongst other things. While it may leverage AI/ML, there is more to Data Science than this, and all AI/ML is not Data Science. It is a broad and evolving field that is hard to define. However, the key to Data Science projects is the use of advanced algorithms which leverage Mathematics, Statistics, and an understanding of business context. Some common activities include framing the problem, data collection, data engineering, exploratory data analysis, model development, and insight generation.
Fig 1. AI vs ML vs Data Science
Excited? Next Question in Your Mind Is: “Would This Be a Good Career Option for Me?”
To answer this, let’s look at certain things that a career in Data Analytics/AI/ML demands:
What Are the Career Options in Data Science and Data Analytics?
As this is an evolving field, a lot of such opportunities keep on arising from different fields of business. However, there are some prominent areas where Data Science roles are offered to MBA and engineering graduates.
Apart from this, one may be working on learning and implementing new technologies, going through regular up-skilling sessions, and exploring additional opportunities. While some projects might be technology-intensive, others may be reporting-driven. As one progresses in the organization, the focus shifts more from actual delivery to stakeholder management and business development.
The industries these companies may cater to are Insurance (Property and Casualty, Life and Annuities), Banking, Financial Services, Retail, Healthcare, Travel, Transport, Sports, etc.
Skills required in all these cases are almost always hands-on experience with coding in Python/SAS/R/SQL, etc., along with sound understanding of financial markets, financial products, such as equities, derivatives, and the macroeconomic factors, among other things. While in some cases, MBA graduates are required to do hands-on work, in other cases their role is of project management.
Apart from these, there are many fields, such as sports, real estate, telecom, ride-sharing, hospitality, etc., where data science and analytics are becoming key to their operations and corporate decision-making.
History is rife with examples of humans collecting and analyzing data to make decisions. With the advent of electronic format of data collection in the last few decades and the omnipresent Internet, this is surely just the beginning. I wonder what the future holds!
Nitin Chandak is a graduate from the Indian Institute of Management Calcutta, where he majored in Finance. He has been a consistent academic performer throughout. Post completion of his MBA, he has worked as an analytics consultant for three years. Prior to his MBA, he worked with Backspace (as Breakspace was then known) where he was instrumental in strengthening the Communications and Education Consulting divisions. Nitin is an ardent Manchester United fan and a chocoholic. He wishes to travel the world.