Big Data Analytics and Data Science – Banishing the Confusion

Big Data Analytics & Data Science. The job sphere is always buzzing with the possibilities of new and exceptions. And with the present onset of data technologies, more and more people are turning to this newfound land with avidness and enthusiasm. But, as is customary, the confusion with the aforementioned is also quite rampant. It often becomes difficult to differentiate between the terms, particularly when they are used synonymously. The ambiguity stems from the prevalent reason that, both the job roles are relatively new. However, big data analytics and big data science do not mean one and the same and must also not be used interchangeably.

Big Data Analytics & Data Science – Traditional Definition

Big Data Analytics focuses on generating useful insight from an available dataset. The one universal purpose is to solve queries and be able to make business-friendly decisions. Also, the usage of queries and various processes related to Data Aggregation are a part of Data Analytics. However, when one refers to Data Science, it becomes a comparatively broader term. Everything from Data Mining to Data cleansing and even visualizing and ideating over the usage of a dataset is the genre of a Data Scientist.

big data analytics
Picture courtesy: Google

Still, it becomes disquieting to understand the difference merely from a traditional definition. Where do statistics end and science start? Broadly categorizing the two, Data Analytics has more specific fields where it is used. A major part of industries such as healthcare, gaming, travel and energy management, it deals more with the hard numbers and data sets of these sectors. While Data Science lacks a clear-cut demarcation in terms of industry usage. It finds more implementation virtually and is an important part of Internet Searches, Search Recommends and Digital Advertisements. The motive for its use is to identify patterns based on data and henceforth function on it.

Big Data Analytics & Data Science – Core Competencies

Coming to the hardcore needed by the two professionals, it is imperative for a Data Analyst to have an in-depth knowledge. Knowledge of Programming Skills, Statistical Skills, Mathematics, Machine Learning Skills, Data Wrangling Skills, Data Intuition, and Communication & Data Visualization Skills. While a Data Scientist needs to be fluent in Python Coding, Hadoop Platform. Complete Knowledge of SAS and/or R, SQL database and coding, and Working with Unstructured Data.

Apart from the same, often the basic skill set for a Data Analyst is quoted to be, SQL/Regular Expression, Analytics/BI Packages, Intermediate statistics. While a Data Scientist is expected to know Data Acquisition, movement, manipulation, programming, advanced statistics. The common skills that must be present in both the professionals are curiosity, ability to derive insights and tell a story through data. It is perhaps, because of this that both the terms are taken to be one and the same.

Lastly, it often depends on an organisation, what professional do they require more. Seldom does it happen that a company can pick and choose only one particular skill set in this hyper-competitive environment of today. Which is why both the fields continue to be so arcane and enigmatic to many.

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