What is data science and why do we need this now?
What are the fields or let’s say where it all can be applied?
Data science is a multi-disciplinary field that uses scientific methods processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.
So, this is just by the book definition of data science. However, to understand data science, if I need to use a layman language so that everyone can understand,
“Data Science is a way of getting insights from structured and unstructured data”.
Data science is something that we do with the data to solve a business problem. If it put it in a different way, data science is used by businesses for :
- Enhance product/services offerings
- Augment customer/client base
It can be anything, depending upon the request you get from a client.
Why do we need data science?
You may not be aware of it, however, data science existence has been more than for 50 years, but we never look at it the way we look at it right now. It’s because when it was discovered, there was not a lot of data and we also didn’t have the sophisticated computers and devices back then as well.
So, we could not understand how useful data science could be.
But now, we have computers, tools, and sophisticated devices. Moreover, the amount of data that has been generated in recent years has fuelled the need for data science. As per the recent study, more than 90% of the world’s data has been generated in the last 2 years.
Some of the examples of the volume of data that is being continuously generating at present are :
- 7 billion is the volume of shares traded on the US stock market each day.
- 10 Terabytes are the amount of data generated in one flight from Ney York to London.
- 400 Million is the number of tweets per day on Twitter.
- 3 Billion is the number of “likes” each day on Facebook.
It is the volume of this huge data that have been generated in the last 2 years which has compelled the Organizations and the Governments to start investing in data science, artificial intelligence, machine learning, and automation.
It’s now more important for organizations to stay competitive in the market. They are doing all sorts of work to harness the power of their data in the most meaningful way for the company growth.
Governments are now using the Census data to bridge the gap between the government and the masses. The aim is to get closer to its people. This helps them further in distributing the state resources equally amongst the people.
Applications of Data Science
Let’s discuss some of the widely used and talked about applications of data science:
- US Elections: Trump campaigns were believed to be dependent on polling research. His team of data scientists conducted more than 800,000 live as well as online surveys across seventeen battleground states during the campaign period.
The voters have then attributed 500 nodal points on the basis of their personality traits.
Localized and highly targeted video and audio campaigns were then developed and privately shared with the voters which generated a positive aura for Trump as a future president and thus resulted in his ultimate win.
- Social Networking Sites: Have you ever imagined how social networking sites like Facebook, Twitter or Linkedin suggest whom to follow and who might be your friend. Well, they use data science.
Starting from simple rules like :
- Which place do you belong to?
- Who are your friends?
- Who are your friends of friends?
- Your interest areas
All of these data points help these social networking companies recommend you who might be your friend and who do you want to follow.
- Loan Approval: Do you know how banks decide how much credit needs to be given to which Customers? What are the criteria?
Well, you have guessed the answer already. Yes, by using data science. Lending institutions like banks and the NBFCs uses the customer credit rating/score provided by the credit bureau companies like Sibel to decide if they can give a customer a loan or not. Also, It helps them in deciding the number of loans that can be disbursed to a customer.
A typical bank looks at 100 data points of an applicant before deciding on the loan approval.
- Banking Fraud: Banks and credit card companies also use data science to detect fraudulent transactions in real-time. Every transaction you do with your bank gets analyzed in real-time and if it looks fishy or suspicious, the system highlights these transactions and you get a phone call from your bank asking if you have attempted that particular transaction.
Similarly, banks use data science to contain anti-money laundering activities determining the source of funds, where and how they are being used.
- Ecommerce: Have you thought of how these e-commerce companies like Amazon, Flipkart, and Snapdeal show you the right product on their platform which you happen to buy straightaway. Well, they use a Recommendations engine which in turn uses past data of the customer behavior happening on their website. In addition to this, eCommerce sites use data science to optimize discounts on various products, thus enhancing their cross-selling and up-selling strategies.
They also use data science to generate demand forecast so that they can fill up their warehouse inventory up to the maximum extent.
I am sure; you guys are now getting enough idea on how data science is impacting our day to day life. Moreover, this impact is getting stronger each day.