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Big data has transformed the way companies and individuals make certain decisions about different things. In the olden days, there was no competition in terms of products and services. Today, there are thousands of options available in the market. Customers have choices and businesses have an uphill task to target their audiences. This is where data science and predictive analysis come in.
So, what do these terms imply? This is s question which troubles a lot of people. The terms seem to mean one and the same thing. In reality, they don’t. Data Science is a general field while Predictive Analysis is a specific one where the same methods are used but the outcomes are different. Here we discuss the major differences between them allowing people to know how they can tell these two concepts apart:
“Every company has big data in its future and every company will eventually be in the data business”
Thomas H. Davenport
The first factor which is a cause for differentiation among these two techniques is the type of analysis which is performed under each. Data Science and Predictive Analytics broadly refer to the same thing. It is about data management but the way the are used is different. Data Science applies to a holistic view about handling data so that companies can use it to make effective decisions. Predictive Analysis on the other hand is a technique which is used to perform analytics in a specialized context like for a project etc.
When we compare the definitions and what both these concepts entail, things remain a little bit vague. In order to paint a clearer picture, it is more convenient to compare what type of data is managed and how it is done under both these techniques. In Data Science, raw data is used. It is completely processed from sorting all the way to analysis and presentation. On the other hand, Predictive Analysis makes use of already organized data which is applied to a specific condition to derive an outcome.
Data Science and Predictive Analysis are both systematic processes. There is a certain flow which experts stick to in order to obtain the relevant results. In Data Science, the steps followed are: collection of data, inspection, cleaning, transforming and then drawing conclusions. On the other hand, in Predictive Analysis, the data is modelled, then the model is trained for the scenario at hand and finally the outcome is predicted. The model in Predictive Analysis refers to the framework which is created for the project at hand.
The results and outcomes of both these techniques also varies to some extent. Data Science can be used to make predictions or it can be left at just the conclusion for the current situation. It all depends on what the organization is looking to achieve. In Predictive Analysis, the project team can make calculated assumptions, test hypothesis using various tools and then conclude whether it is appropriate or not. With the models and hypothesis, Predictive Analysis can be used to make forecasts about the future.
Last but not the least is the difference in usage of the data which is obtained through either. Data Science is about finding trends and patterns, understanding consumer behavior, making informed assumptions about outlook and other significant business decisions. The data from Predictive Analysis is used to answer highly specific questions like what will happen if demand goes up by 2% or how the market will react if the quantity of a certain product is decreased by 5% etc.
Data Science and Predictive Analysis may be a stretching of the same domain but the refer to different fields. The requirements, functions, outcomes and usage of both are considerably divergent from one another. While both help businesses understand the respective target markets, Data Science does it for the entire strategy of the organization while Predictive Analysis does the same for a project, scenario, brand or promotion etc.
If you want to learn more about these fields or want to be part of one of the best data science bootcamps in the country, get in touch with us today.