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Why data science is a simple solution for your complex data?

Data science was taken the centre stage and for all valid and compelling reasons. We are currently witnessing how these methods can provide solutions that are generic, agile, and conceptually simple to implement. Solutions provided by using data science as your core tool tend to have a short time-to-validation while obtaining performances near or above the state-of-the-art in some applications. These tools and methods are even more fascinating for data scientists who are supposed to find solutions to a diverse set of problems in a short period of time.

To form that foundation where data science is helping you reach your targets or resolve matters for you, there are three simple steps one must follow:

The focus should be on solving real-world problems while measuring your solution.

Picking the right and simplest tool for the job. 

Repeat consistently.

Firstly let’s understand why we need Data Science?

Traditionally the data that we had was structured and small in size. Data could be analyzed by using simple BI tools. But today most of the data is unstructured or semi-structured. So we assume that unstructured data needs complex tools to support it. But the right tool for the work undertaken should always be the simplest one, at least in the initial stages. Complex data can be broken down, their behaviour may be hard to develop an intuition for and implementing them can be time-consuming. We ought to focus on simplicity and have a track record of providing real-world value, this gives a tremendous boost.

When you start a project, you would want to see it through to the end. That project could be as simple as a SQL query, so data science will make a list of possible projects in and out of the company and will have a go at each. Generating and measuring value should take the front seat over almost everything. Here it’s important to remember that when you measure your solution, it depends on what problem you’re trying to solve. For instance, If you are trying to create a mortgage approval model, you will probably care more about correctly screening out fraudsters than mistakenly rejecting those with good intentions. 

How do I measure my solution? 

It should reflect your priorities. This approach is very vital to your portfolio and is a crucial part of any Data Science work. Data science helps you talk to stakeholders, get to the root of any problem, and find the best way to measure the value your solution provides.

It’s important to build a solid base of skills. Data science breaks into your complexities find real-world problems and aim to make a dent in them as quickly as you want. It helps you finish more of what you have started, and gets you to your desired result faster. Data scientists make data-driven decisions on a daily basis. It’s vitally important these data scientists are sufficiently competent at gathering good data, properly interpreting it’s meaning and scale data pipelines or automate their efforts. 

Data science is more capable of not only basic skills but also is used to check basic probability and statistics, distributions, confidence intervals, sampling, correlation, and regression. 

In the end, it won’t be wrong to say that Data Science will be used as a powerful tool to strategize businesses. More data means providing more opportunities to drive key business decisions. So keeping it simple is the key to a successful business and it will change the way we look at the world deluged with data around us.

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