In 2006, the world’s largest professional network — LinkedIn — resembled a startup even after four years of its functioning. At that time, the company had close to 8 million accounts. Although the number of users was steadily rising, members weren’t looking to grow their networks beyond their friends and family.
It was then that a team of data scientists found the missing link, and created features like ‘people you may know’, ‘people who viewed’ and ‘who’s viewed your profile’, and since then, LinkedIn’s growth trajectory has been much talked about. Today, it has 722+ million members in more than 200 countries and territories worldwide.
That was data science working at its best. But, what exactly is data science and how will it help the future?
According to Martin Schedlbauer, PhD and data science professor at Northeastern University, data science is used by “computing professionals who have the skills for collecting, shaping, storing, managing, and analyzing data [as an] important resource for organizations to allow for data-driven decision making.”
The title of a data scientist was coined in 2008 by DJ Patil and Jeff Hammerbacher, who were data and analytics leads at LinkedIn and Facebook, respectively. For the last four years, data science has been featured as a top career by Glassdoor. What’s more, Harvard University has named a data scientist as the ‘sexiest job title of the 21st century’.
Why has the demand for data science increased exponentially in the last few years?
That’s because there has been a surge in the generation and consumption of data — 2.5 quintillion bytes of data are processed every day. It is data scientists who make sense of all this data and build something meaningful with it.
Here’s an example we’re all too familiar with – Amazon. It uses data science to make the experience more seamless for the average shopper. Based on what shoppers have searched for, paid for, or are looking to purchase, Amazon customise offerings that fit its customer’s needs – that data play at work.
The other common example you’ve probably come across is when you are “reminded” to take action (like make a purchase) that you do regularly. For instance, on an e-commerce platform, if you tend to order the same items at a certain time every month, you’ll come across attractive deals on it. This is where data science comes into play. It may look like serendipity – but that’s no co-incidence. It’s basically the company’s strategy to prompt the customer to purchase the product right away.
All in all, data science aims to identify the right data sources, join them with other incomplete data sources, and accordingly, filter out what’s really needed. In a world where data is constantly generated, ‘data scientists help decision makers shift from ad hoc analysis to ongoing conversation with data’ (HBR).
There are several ways in which data science can help businesses unlock their true potential. For one, it can help to mitigate risk and fraud. In the insurmountable pool of data available online, it’s not easy to identify the right information. When data scientists are a part of an organisation, they can create several predictive fraud propensity models, and use those as alerts, every time suspicious data is recognised.
Apart from this, data science can help organisations to understand where and when their products sell best. With this methodology, they can refine their offerings to deliver the right products at the right time, keeping in tune with their customer’s needs.
Sales and marketing teams can take the help of data science to understand their audience better. In this way, customers can enjoy seamless experiences in accordance with their likes and preferences.
Apart from these use cases, data science has played a key role in matters of the economy. It has helped to improve public health through wearable trackers, potentially alerting users about any critical health issues, based on their parameters.
Interestingly, it has also proved effective in finding cures for certain diseases or even stopping the spread of a virus. In 2014, when the Ebola virus broke out in West Africa, scientists were successful in tracking the spread of the disease and also identifying areas that are most prone to the illness. Eventually, this diagnosis helped public health officials to contain the virus.
Although there is limitless data to be analysed, there is a shortage of qualified data scientists today. “The word on the street is there’s definitely a shortage of people who can do data science,” Daniel Gutierrez, managing editor of insideBIGDATA, had earlier shared with Forbes.
Here are some data science careers that are critical and much in demand. An aspirant can either pursue an undergraduate degree in data science, or in a related field to get an entry-level job in the industry. Most high-level positions require professionals to pursue a master’s degree in data science.
As mentioned above, data scientists are responsible for finding, filtering, and organising data for companies. They sift through large piles of data generated every single day to find patterns that will benefit an organisation, and at the same time, help to fulfill their strategic goals.
Banking is one of the most prominent areas where data scientists are increasingly needed. They do not just help banks to effectively manage their resources, but also help them make more educated decisions through fraud management, dealing with customer data, risk modeling, as well as real-time predictive analysis.
Although most people consider this as an entry level job, it isn’t necessarily true. A data analyst, as the name suggests, studies company data and leverages it to answer business questions. For instance, he could be asked by the money to assess the effectiveness of a marketing campaign. This involves studying the data properly, cleaning it, and then performing analysis to come to a conclusion.
Although this role might resemble that of a data scientist, it sits at the intersection of software engineering and data science. A machine learning scientist makes use of big data tools and resources to ensure that the raw data that is collected is refined in the form of data science models. These professionals also have the responsibility of running tests and conducting experiments to assess the performance of various models.
Machine learning engineers are in demand to utilise their capabilities for fraud detection, as well as image and speech recognition. Some of the most successful examples include smart personal assistants like Siri and Alexa, as well as autonomous cars.
Machine learning scientists primarily work in the research and development of algorithms that are used in adaptive systems. At Amazon, they build methods for predicting product suggestions (recommendations) and product demand (forecasting), and explore Big Data to automatically extract patterns (large-scale machine learning and pattern recognition).
This role is one of the most critical and can be considered the backbone of an organisation. The role does not require too much statistical analysis, and is more inclined towards software development and programming. A data engineer might be given the responsibility of building data pipelines whether in the fields of sales, marketing, and revenue, and providing it to data analysts in a usable format.
Here ae the top ten universities to study data science at the masters level, as per QS rankings 2021:
||Los Angeles, USA
||ESCP Business School
||Berlin, Germany & Paris, France
||Imperial College Business School
||Los Angeles, USA
||ESADE Business School
Today, data science is amongst the top jobs in the world today. Whether it is security or shopping, dating apps or getting a cab, everything relies on big data. With increasing proliferation of technology and rising demand for consumer services, there seems to be no stopping for data science careers.