The emergence of Data Science – All About Data Science


What is Data Science?

Okay, so in this part of the series, we are getting down the main question, “what is data science”. This one question open up numerous other questions like how can one become a data scientist? What skills do you require to be data, scientists? How can data science help in making decisions? Well, here we are going to find answers for all these questions and many other unsaid questions as well.

“Data Science”

Okay, so tackle our topic of the day, data science is a well-balanced cocktail of various tools, algorithms, and machine learning principles. The main objective of this cocktail is to discover hidden patterns beneath the mountain of raw data. Well, if you are wondering that if the role of data scientist is to find patterns in raw data, then what the hell are statisticians doing for so long?

Now, this a very important question so let’s discuss it in the depth. The answer to this question lies between the difference between explaining and predicting.

Data analytics usability explains what is going in the processing history of the data. On the contrary, the role of data scientist is little vast, then this, they perform exploratory analysis to identify the insight of the data. But, they also use advanced algorithms to identify the cause of a particular problem or even in the future. Data scientist explore data from different angles and sometimes they go beyond set angles to explore data in the perfect way.

So, data science is used to make solid decisions and predict the future with the correct data analysis. All these operations are accomplished by this science with the help of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning.

Predictive Casual Analytics

Do you want to create a model with the ability to predict future events? Then, you need to perform predictive casual analytics. Suppose, if you have a business of giving money on credit to the people, then you always have a concern that whether the person is going to return your money in time or not. So, here you can create the future predictive model on the basis of the past financial history of the person and can predict that is person financially reliable or not.

Prescriptive Analytics

If you want to create the model which is self-efficient and intelligent to make its own decisions and can modify it according to the change in the circumstances, then you definitely need prescriptive analytics. This analytics offers a series of advice to you which can be applied in the situation. Here, we can take the example of self-driven cars, they give suggestions like when to turn, but it depends upon you whether to turn or not. These cars only offer you suggestions on the basis of data.

Making Predictions with Machine Learn

Suppose, if you have data of financial firm and you want to create a model which can predict its future trends, then you should predict it with the help of machine learning. This falls under the paradigm of supervised learning. It is called supervised because you already have the data based on which you can train your machines. Using this approach, you can create a fraud model to detect the number of fraudulent using historical records.

Pattern Discovery for Machine Learning

If you have parameters on which you will predict, then you have to find the hidden pattern in the data to find any pattern. This is going to be an unsupervised model as you don’t have any parameters. Clustering is the most common algorithms used in this case.

So, peeps, that was all fuss about data science. Now, we are not ending our series here because you have still a lot to learn about this awesome field. That’s why follow us to the next installment of this series to learn more.

About the author

Arpit Agarwal

I am a freelancer content writer, web developer and Video editor who loves to write technical stuff and on the other hand makes awesome videos as well. I like to make people happy with my writing and also try to make sure, you come back to read more.

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By Arpit Agarwal

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