What is Data Science?
The simplest Data Science meaning would be, applying some scientific skills on top of data so that we can make this data talk to us.
Now, what we exactly mean by ‘applying scientific skills on top of data’? Well, to put it precisely, Data Science is an umbrella term which encompasses multiple skills and scientific techniques.
When we combine all of these scientific skills into one, what we get is nothing but Data Science. Now, let’s go ahead and have a look at these different scientific techniques in this blog on ‘Introduction to Data Science’.
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We’ll start with data visualization. Data visualization is an essential component of a Data Scientist’s skills set. So, in simple terms, data visualization can be considered an amalgamation of science and design in a meaningful way.
Next technique in Data Science is data manipulation.
Normally, the raw data which we get from different sources is extremely untidy and drawing inferences from this untidy data is too difficult. This is where data manipulation comes in. Data manipulation techniques help us refine the raw data and make it more organized so that finding insights from the raw data becomes easy.
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Next up in this blog on ‘Introduction to Data Science’ is statistical analysis.
Simply put, statistical analysis helps us understand data through mathematics, i.e., these mathematical equations help in understanding the nature of a dataset and also in exploring the relationships between the underlying entities.
Finally, we have Machine Learning.
Machine Learning is a sub-field of Artificial Intelligence, where we teach a machine how to learn on the basis of input data. This is where we build scientific models for the purpose of prediction and classification.
Now that we have properly understood the Data Science meaning, it’s time to look at the life cycle of Data Science in the below section: ‘Life Cycle of Data Science’.
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Life Cycle of Data Science
Let’s look at the stages involved in the life cycle of Data Science.
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We already know that data comes from multiple sources and it comes in multiple formats. So, our first step would be to integrate all of this data and store it in one single location. Further, from this integrated data, we’ll have to select a particular section to implement our Data Science task on.
So, in this step we are acquiring data.
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Once the data acquisition is done, it’s time for pre-processing. The raw data which we have acquired cannot be used directly for Data Science tasks. This data needs to be processed by applying some operations such as normalization and aggregation.
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Once pre-processing is done, it is time for the most important step in the Data Science life cycle, which is model building. Here, we apply different scientific algorithms such as linear regression, k-means clustering, and random forest to find interesting insights.
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After we build the model on top of our data and extract some patterns, it’s time to check for the validity of these patterns, i.e., in this step, we check if the obtained information is correct, useful, and new. Only if the obtained information satisfies these three conditions, we consider the information to be valid.
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