Data Science Tutorial: Why we need data science?
Do you know more than 90% of the data in this world has been produced only after the computing revolution in the last few decades? Well, the exponential speed of growth for digital data opened up new opportunities for the businesses and decision-making organisations to come with more informed, precise and accurate data-driven strategy and decisions that really deliver results. As data has become the new oil, data science became the new discipline to propel growth and opportunities.
As an aspiring data scientist or as a data science enthusiast it is absolutely necessary to know the principal value propositions that fuel the proliferation of data science. Here below we are going to explain why we need data science.
Data Formatting and Cleaning
Considering the overwhelming growth of data both in terms of quantity and variety, the mountains of data unverified and non-categorised data cannot be utilised for getting insights or deciphering meanings unless they are formatted, put into categories and managed with some frameworks and rules. Actually, most data gathered everyday by millions of computing machines and servers doesn't mean anything if they do not go through some kind of formatting or categorisation. Data is also required to be cleaned often by removing the irrelevant portions. Data cleaning also refers to mitigating all the inconsistencies in the data formats.
Data analytics is the principal discipline flourished out of the data science. A large pool of data representing a niche or from various niches can be analysed to explore their correlations, the possible causes and effects related to any event or outcome, the patterns and behaviours that can be envisaged through data, the trends showcased by various data sets, etc. The role of the data analytics is to decipher the potential insights, patterns, trends and meanings hidden in these large pools of data comprising both structured and unstructured data. Through data analytics a business can gain most relevant data insights that can help decision making and strategy building processes.
Data modelling or statistics us one of the most important fields related to the discipline of data science. Data is also about numbers and statistics. Now, going deeper into the pool of data a data modelling expert can actually get hold of numerical data that can be transferred into relevant and precise statistics or visual data models. Statistics and visualised data models are popular because they can be grasped quickly and utilised instantly to drive conclusions and make accurate reports.
Formatting data, putting the data into visual analytics and modelling the data for quick to grab relevant insights, all of these play an important role. But since everyone is not a data scientist, keeping a consistent flow of such data-driven insights often proves to be challenging. This is why, building a consistent and well-articulated interface where data can be gathered, analysed and modelled consistently is a very important part of the job of data scientist. Well, building an software or app product to make use of the relevant data is what we call data engineering. Data engineering can utilise data in numerous ways like making visual charts, building an analytics app or creating a niche data metric.
The above-mentioned disciplines of data science clearly shows the utilisation and reasons behind using data science. In reality, data science is a more multifaceted field with innovative and new niche utilisations of data continuously emerging.