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How is data processed into calculated and accurate prescriptions?

Data is ubiquitous in 2022, and available in an abundance. A huge portion of the same is generated by us! That too, while undertaking everyday activities like seeking help from or simply browsing the internet. This huge amount of data is then collected by ethical means. And utilized for predicting the future depending on the trends in the past. However, this seemingly easy process demands cutting-edge, state-of-the-art infrastructure. And an adept data scientist at the helm of operations. Preferably, a data professional with years of experience in the relevant sector, and who can be bestowed with responsibilities of paramount importance.

This article will try to discuss the various stages of making data into prescriptions. And try to cover all the relevant components and aspects of the data science steps

Data accumulation

Data about a region, its population, or simply about the weather can be obtained from a plethora of public and commercial sources, both for free and through paid means. The data we are talking about is unstructured and cannot be utilized for analysis directly. This data can be completely unlabeled, riddled with repetitions, artifacts, and confusing noises that can affect the analysis process and may be an automation entity that is being trained by the same dataset.

Data structuring

The unstructured data is then arranged and structured in this stage. The data volumes we utilize to predict and prepare for day-to-day tasks are gargantuan. And the same is humanly impossible to analyze. For this very purpose machine learning, deep learning, and other automated tools are extensively used. These tools are usually trained to work with specific data set models and formats of organization. And if presented anything else cannot perform as well or maybe not at all. Therefore, this unstructured data should be molded into structures that the automated analysis entities can comprehend and work with.

This stage involves, removing noises, repetitions, false positives, errors, biases, and irregular occurrences. Exceptions are also marked and labeled for reducing inconsistencies and the overall noise is reduced by extensive formatting and editing.

Data analysis

The data analysis process is long and arduous. And often impossible to conduct by human effort alone. The structured data is presented to machine learning and deep learning entities that can detect patterns and trends in the data set. And subject the same to extensive statistical analysis.

Splunk, Apache spark, and Power BI are the common and popular tools used to analyze huge amounts of data for specific and unique purposes.

For example, a data set is used by a smartphone company for optimizing its display. Can help an ophthalmologic lens manufacturer in terms of production and modification. Similarly, fuel consumption data of an entire population can be helpful for fuel companies in terms of planning commercial operations. And the same can be utilized by pollution control organizations to spread awareness among vehicle owners.

Data visualization

Data visualization is the presentation of the analytics results or visualizes the data sets, in terms of certain criteria. So, the sets can be presented and understood from the right perspective. This representation must be lucid and easy to understand. So that all the involved parties can figure out their responsibilities in the grand scheme of things and plan accordingly.

Making predictions and prescriptions

Prescriptions are made depending on the predictions achieved by extensive data analysis. To make predictions that can guide a business through uncertain times into a future, full of promises of stability. A lot of data from various domains must be acquired and made into sensible information. And the same must be delivered and explained to the workers so that they can perform with the bigger picture in mind. And devote themselves to achieving a clear goal. Therefore, this step is arguably the most important of all the data science steps.


For the satisfactory conclusion of all the data science steps, adeptness infrastructure and leadership must come together as one entity. And perform with absolute synchrony! Given the unpredictable nature of the times, the dependency on data will increase. And the roles of data professionals will only become more rewarding and demanding of more sense of responsibility. And the acumen necessary for being the adept professional the industry needs, it is important to research the market and choose the institute that can propel a student toward professional relevance and absolute finesse.

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