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What are the Differences between Data Science and Big Data?

Let us talk about What Data Science actually is?

According to Wikipedia Data Science, also known as data-driven science, is an interdisciplinary field of scientific methods, processes, algorithms, and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.

Data Science is the combination of statistics, mathematics, programming, problem-solving, capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing and aligning the data.

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Let us talk about What Big Data actually is?

Big Data refers to humongous volumes of data that cannot be processed effectively with the traditional applications that exist. The processing of Big Data begins with the raw data that isn’t aggregated and is most often impossible to store in the memory of a single computer. Big Data is something that can be used to analyze insights which can lead to better decisions and strategic business moves.

The definition of Big Data, given by Gartner is, “Big data is high-volume, and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation”.

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Applications of Data Science:

• Table of Contents

  Digital Advertisements: The entire digital marketing spectrum uses the data science algorithms – from display banners to digital billboards. This is the mean reason for digital ads getting higher CTR than traditional advertisements.

• Recommender systems: The recommender systems not only make it easy to find relevant products from billions of products available but also adds a lot to user-experience. A lot of companies use this system to promote their products and suggestions in accordance with the user’s demands and relevance of information. The recommendations are based on the user’s previous search results.

• Internet search: Search engines make use of data science algorithms to deliver best results for search queries in a fraction of seconds.

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Applications of Big Data :

• Big Data for financial services: Credit card companies, retail banks, private wealth management advisories, insurance firms, venture funds, and institutional investment banks use big data for their financial services. The common problem among them all is the massive amounts of multi-structured data living in multiple disparate systems which can be solved by big data.

• Big Data in communications: Gaining new subscribers, retaining customers, and expanding within current subscriber bases are top priorities for telecommunication service providers. The solutions to these challenges lie in the ability to combine and analyze the masses of customer-generated data and machine-generated data that is being created every day.

• Big Data for Retail: Brick and Mortar or an online e-tailer, the answer to staying the game and being competitive is understanding the customer better to serve them. This requires the ability to analyze all the disparate data sources that companies deal with every day, including the weblogs, customer transaction data, social media, store-branded credit card data, and loyalty program data.

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The Skills you Require

to become a Data Scientist,

• Education: 88% have a Master’s Degree and 46% have PhDs

• In-depth knowledge of SAS and/or R: For Data Science, R is generally preferred.

• Python coding: Python is the most common coding language that is used in data science along with Java, Perl, C/C++.

• SQL database/coding: Though NoSQL and Hadoop have become a major part of the Data Science background, it is still preferred if you can write and execute complex queries in SQL.

• Working with unstructured data: It is most important that a Data Scientist is able to work with unstructured data be it on social media, video feeds, or audio.

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to become a Big Data Professional,

• Analytical skills: The ability to be able to make sense of the piles of data that you get. With analytical abilities, you will be able to determine which data is relevant to your solution, more like problem-solving.

• Hadoop platform: Although not always a requirement, knowing the Hadoop platform is still preferred for the field. Having a bit of experience in Hive or Pig is also a huge selling point.

• Creativity: You need to have the ability to create new methods to gather, interpret, and analyze a data strategy. This is an extremely suitable skill to possess.

• Mathematics and statistical skills: Good, old-fashioned “number crunching”. This is extremely necessary, be it in data science, data analytics, or big data.

• Computer science: Computers are the workhorses behind every data strategy. Programmers will have a constant need to come up with algorithms to process data into insights.

• Business skills: Big Data professionals will need to have an understanding of the business objectives that are in place, as well as the underlying processes that drive the growth of the business as well as its profit.

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Now let’s talk about salaries!

Though in the same domain, each of these professionals, data scientists, big data specialists, and data analysts, earn varied salaries.

The average a data scientist earns today, according to Indeed.com is $123,000 a year. According to Glassdoor, the average salary for a Data Scientist is $113,436 per year.

The average salary of a Big Data specialist according to Glassdoor is $62,066 per year.

Now that you know the differences, which one do you think is most suited for you – Data Science? Or Big Data? 

Sources – google, simplilearn.com

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