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Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data.

Data Science
Data Science

Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies. Mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization's competitive advantage.

The increase in the amount of data available opened the door to a new field of study called Big Data or the extremely large data sets that can help produce better operational tools in all sectors. The continually increasing sets of and easy access to data are made possible by a collaboration of companies known as fintech, which use technology to innovate and enhance traditional financial products and services. The data produced creates even more data which is easily shared across entities thanks to emergent fintech products like cloud computing and storage. However, the interpretation of vast amounts of unstructured data for effective decision making may prove too complex and time consuming for companies, hence the emergence of data science.

Let’s Understand Why We Need Data Science

Traditionally, the data that we had was mostly structured and small in size, which could be analyzed by using the simple BI tools. Unlike data in the traditional systems which was mostly structured, today most of the data is unstructured or semi-structured. Let’s have a look at the data trends in the image given below which shows that by 2020, more than 80 % of the data will be unstructured.

Unstructured data
Why Data Science?

Every technology has a life, right? So, now its time to come up with something new. But you don’t know what should be innovated, so as to meet the expectations of the users, who are eagerly waiting for your next release?

Somebody, in your company comes up with an idea of using the user generated feedback and pick things which we feel users are expecting in the next release.Comes in Data Science, you apply various data mining techniques like sentiment analysis etc and get the desired results.

Who is a Data Scientist?
Data Science

Data Scientist is the master of all trades! He should be proficient in maths, he should be acing the Business field, and should have great Computer Science skills as well. Scared? Don’t be. Though you need to be good in all these fields, but even if you aren’t, you’re not alone! There is no such thing as “a complete data scientist”. If we talk about working in a corporate environment, the work is distributed among teams, wherein each team has their own expertise. But the thing is, you should be proficient in atleast one of these fields.

How we solve a problem in Data Science?
Solution Data science

should one approach a problem and solve it with data science. Problems in Data Science are solved using Algorithms. But, the biggest thing to we judge is which algorithm to use and when to use it


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