6 ways in which data science has changed the world: significance of data science

What is data science? Is data science that much important? Why so? We will address all these questions. It is a term that is used about everywhere, but since it’s a buzz word, it’s easy to misinterpret. Data science requires a very specific set of skills that any organization can use for its advantage. Data science allows any organization or business to use every kind of data they have, whether it is customer’s data or the financial data, in a useful manner for its own growth. But just because we know that data science can be used as a potential launch pad for any business, it doesn’t mean that it’s easy to apply. This is the reason that multiple data science projects crashed badly and failed to deliver results because of not doing the job properly.

Data science  is one of the most fast-paced developing career choices in the world nowadays. There are a few other career options which provide such a hefty paycheck combined with massive versatility within the ever-evolving job profile. Data scientists’ work involves collecting, organizing as well as analyzing massive amounts of data coming through channels into the organization’s domain. On top of this data, scientists deal with the framing of the problem as well. All of this makes a data scientist’s job profile more respectful. 

But the most important job of a data scientist is to be able to predict the future trends of a system accurately. In fact, they answer the questions about the future. Their work essentially involves working with machine learning algorithms and making accurate models that can perfectly describe a real-life system. These models can successfully learn from their own datasets, figure about various mistakes they commit and improve themselves over time without the need of a supervisor. 

Data science in the real world is everywhere starting right from path optimization of a cargo ship in the Atlantic Ocean to the prediction of the potential side effects of a medicine. Data science also helps to predict the potential of an athlete as well as using various data of athletic to build career success for the generations.

Data science course and data analytics course are similar but are so vastly different fields of studies. Data analytics deals with answering the questions similar to what was the growth of a particular company like on a year. But data science treads an entirely different path and deals with answering the questions of the future by using data sets from the past. This basically means they’re constantly learning from various data sets. Although both of them utilize the same software like python and R, they follow completely different routines. Certain parts of them do sometimes overlap. 

Who is a data scientist?

A data scientist is a person who has all the technical skills required to solve complex issues by not just stopping there, but continuing to think about the various ways in which more problems can arise and providing solutions before they arise.

A big part of a data scientist’s skill involves mathematics. They should be able to spot trends in data. In some ways, data scientists resemble excavating machines and spend their time by digging up useful information from piles of not-so-meaningful gibberish. This is the reason that data scientists are so much valued. But not so long time ago, data scientists were not that much popular. But suddenly it became popular and have completely changed the way in which various companies work.

Of course, it’s impossible to predict all the aspects of a particular problem and try to be as accurate as possible. Some of them work better than others. Data scientists have to choose the best option they can perform.

Career options as a data scientist and their significance

1. In the Healthcare department

Data science has made progress in leaps and bounds. Back in 2008, a few Google staff discovered that they can actually track flu in real-time. CDC tried to create the FLU map by using FluView but it was updating only once in a week. Google figured out that, it can roll out updates much quicker with this new system called Google Flu Trends. 

Although the attempt failed, it was useful to show how much data science can actually help. There are many other healthcare services that were brought out and were based upon data science, such as Clue: Predicting Periods app.

2. Road travel

Road travel is one of the most essential parts of any citizen’s life. The number of automobiles is increasing day by day and they are causing air pollution. To reduce these carbon emissions, various methods are suggested to utilize the public transport. How data science can help in this aspect? 

Think about the number of roads that we need to travel. It is actually a loop. Consider your home to office and back to home or to school and back to your home. All these routes make a loop. Data science can help us to optimize our travel in a such way that we travel less and this helps in reduction of fuel. Yes, the minor adjustment might feel really small today. But it will actually help to save tons of fuel, if we consider multiple citizens across the country. 

One of the best examples is Uber: eats

This company uses machine learning and various other algorithms and statistical modeling to optimize any delivery. This saves tons of fuel consumption by considering every variable such as traffic and holiday rushes.

3. Sports

In the early part of the century, there was a team called Oakland athletics which was known for bringing miracles. The recruitment budget was much lower than any other team for Oakland Athletics. Their financial status didn’t allow them to buy quality players. However, they refused to shut down in sorrow. They bought players who they thought were statistically more capable of delivering results. They made it to the playoffs. The famous book, named Moneyball, mentioned about it. Today there is a market of 4.5 million dollars globally for sports analytics.

Another example of data science in sports is that of Liverpool FC. 2019 cup Premier League was almost won by FC Liverpool single-handedly using data science. We know how difficult it is to quantify football, especially considering the nature of it. Ian Graham, a data scientist, found out a way to do that exactly. He figured out a way to quantify and calculate how pass or goal attempts can improve a team’s chances to win. It was effectively used for increasing Liverpool FC’s chance of winning the Premier League. They had used it to buy or sell players as well as for their in-game strategy.

4. The government

The government constantly keeps a record of all the citizens’ data. The government has data in its database about every citizen in the country. They have sources to obtain any data. They can figure out what anyone is doing online or offline. That’s how powerful the government is. When there is a criminal case, the government can ask for records from any website or any organization to validate their suspect.

5. E-commerce

At one point in this century, people used to go to the market for shopping. But today’s online marketing has taken over their place. The data scientist usually keeps a record of what every person browses on any commerce website. They keep record of everything they searched, discover a pattern and then show them similar results on the homepage. This attracts customers and hooks them to their web pages. This is, in turn, has helped to increase profit for the online sellers.

One of the best examples of e-commerce applications is the data science-driven commercial ads on such sites. You will notice that the ads are always customized for you. Have you ever noticed that if you search for a specific stuff on an online marketing site or even type it somewhere like in an email, then it will instantly result in getting more advertisements on social sites like Facebook?

6. Social sites

All social sites have completely changed the way people viewed the internet. Nowadays friendships, as well as relationships, take place with the help of social sites like Facebook, Tinder, etc. Sites like Facebook or Instagram use every user’s interaction data, person’s pictures, likes or the post on which a person is commenting to create a custom newsfeed for every individual.

Another example is Tinder. Tinder has an amazing network of algorithms that help every user to get a match that matches their personal preferences. Earlier these algorithms used to be based on any user’s attractiveness level but now they are based upon the distance between them. Effectively people who are near to each other might get matched faster than the people who are at greater distances.

The wonders that data science can do are mind-boggling. Data science has advanced a lot over the years, but it is still in its infancy. Applications of data science, including artificial intelligence, deep learning, internet of things, are growing day by day. 

That is the reason everyone are looking to learn the data science course and to shift their carriers into data science.

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Author bio:

Senior Data Scientist and Alumnus of IIM- C (Indian Institute of Management – Kolkatta) with over 25 years professional experience ,Specialised in Data Science, Artificial Intelligence, and Machine Learning.

PMP Certified

ITIL Expert certified

APMG, PEOPLE CERT and EXIN Accredited Trainer for all modules of ITIL till Expert

Trained over 3000+ professionals across the globe

Currently authoring book on ITIL “ITIL MADE EASY”

Conducted myriad Project management and ITIL Process consulting engagements in various organizations. Performed maturity assessment, gap analysis and Project management process definition and end to end implementation of Project management best practices.