date_rangeMay 28, 2020
Simply put, Data Science is a field that is used to analyze large amounts of data and discover hidden insights. It makes use of various methods and techniques based on Mathematics, Statistics, and Computer Science and Engineering. The sudden increase in the amounts of data being created daily is one of the main reasons for the increased popularity of Data Science. Data Science uses advanced and sophisticated tools to discover hidden insights and help make decisions.
In the past, there was a lesser amount of data, so companies made use of simpler tools for analysis. Now, with the sudden increase in structured and unstructured data, researchers need more complex methods and tools to work. Data Science can help uncover many patterns in data that allow the business to make essential business decisions. Since data is being generated across fields, Data Science has found its way to a large number of industries.
These days, most of us shop online. Sites like Amazon, and Flipkart have been popular for a long time now. The reasons for the popularity of ECommerce sites are many.
ECommerce companies use Data Science to uncover customer buying patterns, their interests, likes, etc. They provide product suggestions, customized offers, holiday offers, etc. based on the information from the data. Companies have made huge profits due to Data Science.
Financial Institutions have to be very careful and alert since they deal with money. With a lot of online transactions becoming common, online fraud has also increased. Companies have to be extra vigilant and keep a note of every transaction taking place. Data Science helps financial institutions keep track of transactions. Some of the uses of Data Science in Finance are:
Fraud Detection: If a fraudulent account has done a transaction, Data Science Algorithms can help detect it. The company can then trace the details of the transaction, block the account, and take other necessary steps.
Algorithmic Trading: Big financial companies rely on complex algorithms that use mathematical formulae and other techniques to create new trading strategies.
Customer Services: Data Science can generate insights about customers' habits, and financial companies can devise personalized services for them. Companies can also use data and ML algorithms to provide interaction with customers.
Risk Analytics: Financial institutions face business risks, just like any other company. Common risks involve competitor companies, world economy, global incidents, and stock markets. Data Science can help analyze the risks and come up with solutions to help protect financial companies. Risk Analytics commonly uses Data Science.
Data Science has been used in the Health care sector for a variety of purposes like the invention of drugs, predictive diagnosis, and genetics. Access to a large amount of data has made it possible to make a lot of advancement in the field of health care. Algorithms can process vast amounts of data to come up with computations and discoveries that the human mind will not be able to do.
We see recommendations and suggestions almost everywhere these days. If we go to Youtube, we can see a list of recommended videos. On Netflix and Prime, we see movie suggestions based on what we have watched previously. On social media sites like Facebook, we receive Friend Suggestions. These things are possible due to Data Science and the availability of large amounts of data.
The data provides the likes and dislikes of people of a particular age group, the consumer behavior based on various factors like geographical locations and seasons of the year. Data Scientists use algorithms to discover useful insights.
Agriculture is a sector that has been around for a very long time. It faces a lot of challenges like climate changes, natural disasters, pests, soil problems, etc. A lot of people are refusing to work in farming now, and are increasingly moving to urban cities to take up other jobs. With so many problems, farmers have not started turning to AI and Data Science to help them out.
Data Science helps analyze the condition of the soil, pests that can affect crops, possible climate issues that can occur during the crop season, etc. All these insights can help farmers make better-informed decisions to ensure the growth of their crops.
A large amount of data has resulted in tremendous progress and innovation in the field of Data Science. Data comes with a lot of security concerns like privacy, safety, etc. But if it is stored and used well, it can help different fields and applications a lot. Data Scientists are going to be highly in-demand in the future since more and more companies are turning to Data Science to help with the business. The future is bright for the field of Data Science.
We at Coding Elements offers courses in advanced technologies like Python for Data Science, Data Science with R, Machine Learning, Full Stack Web Development, Mobile App Development. Industry experts teach all courses, and the curriculum is very comprehensive. We offer LIVE and online Python certification courses. You can learn and upskill at your own pace.
The Key Differences Between Data Science and Machine Learning
The Highly Paid and Lucrative Field of Data Science
Transformation of Agriculture Sector by Artificial Intelligence (AI)
How Artificial Intelligence (AI) Powers Restaurant Businesses
The Top 6 Artificial Intelligence (AI) Trends in 2020
The Role of Artificial Intelligence (AI) in COVID-19 Battle
What You Must Know Before You Dive Into Machine Learning
Importance of Artificial Intelligence in The Modern world
Share that you have read this blog