Difference among Data Science vs. Big Data vs. Data Analytics

The amount of digital data which is existing now is growing at a swift rate, according to the studies it’s getting doubled in every two year, because data is everywhere and changing the way humans live. Data is growing faster than ever before as stated by an well known article by Forbes. About 1.7 megabytes of new information will be created every second for every human being on the planet by the year end of 2020. As because our future lies here that’s why it’s important to know. 

In this article, we will differentiate between Data Science, Big Data, and Data Analytics, based on what it is, where it is used, the skills you need to become a professional in the field, and the salary prospects in each field.

Based on what it is , where it is used, and the applications we will differentiate between Data Science, Big Data, and Data Analytics. So lets understand the concepts of all three of them.

Data Science?

A combination of statistics, mathematics, programming, problem-solving, capturing data in creative ways, the ability to look at things differently, and the activity of cleansing, preparing, and aligning the data is called Data Science. Dealing with unstructured and structured data, Data Science is a field that comprises everything that relates to data cleansing, preparation, and analysis.

In other and simple terms, it is the umbrella of techniques used when trying to extract insights and information from data.

Big Data?

Big Data reference to a very large volume of data that cannot be processed effectively with the traditional applications that exist. The beginning process of Big Data is with the raw amount of data that isn’t aggregated and is most often impossible to store in the memory of a single computer.

To describe huge volumes of data a buzzword is used to address it, unstructured and structured both of them, Big Data overpowered a business on a day-to-day basis. Big Data is something which can be used to analyze insights which will lead to better decisions and strategic for business moves. Definition of Big Data, which was given by Gartner, is, “Big data is high-volume, and high-velocity or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” which is actually good enough to understand Big Data in short.

Data Analytics?

Data Analytics is the conclusion of the information of raw data by using science of examining raw data.

Applying an algorithmic or mechanical process to derive and monitor insights and, for example, running through several data sets to look for meaningful correlations between each other all these modules are involved in Data Analytics.

It is used in many industries which allow organizations and companies to make better decisions as well as verify and disprove existing theories or models. The focus of Data Analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.

These definitions are not enough to show the exact difference between all three concepts so Now, let us move to applications of Data Science, Big Data, and Data Analytics.

Data Science Applications:-

  • Internet Search
    In search engines the best results for search queries in a fraction of seconds comes by using of data science algorithms
  • Digital Advertisements
    Data science algorithms are the main reason for digital ads getting higher CTR than traditional advertisements.The whole digital marketing spectrum uses the data science algorithms – from display banners to digital billboards over the websites. 
  • Recommender Systems
    The recommender systems not only make it easy to find relevant products from billions of product choices available but also adds a lot to user-experience which is a plus point for an offering website as because it gives a good experience to your user which inspires them to come on the website again and again. A lot of companies use this system to promote their products like Amazon, Flipkart etc…. and suggestions in accordance with the user’s demands and relevance of information. The recommendations are always based on the user’s previous search results of the user.

Big Data Applications:-

  • Big Data for Financial Services
    Big Data is being used for financial services in Credit card companies, retail banks, private wealth management advisories, insurance firms, venture funds, and institutional investment banks. The very common problem among all of them is the massive amounts of multi-structured data living in multiple disparate systems, which can be solved by big data. Thus big data is used in several ways like:
    1. Customer analytics
    2. Compliance analytics
    3. Fraud analytics
    4. Operational analytics
  • Big Data in Communications
    The importance of Big Data in telecommunication services is in terms of  Gaining new subscribers, retaining customers, and expanding within current subscriber bases because these are the  top priorities for telecommunication service providers. The solutions to achieve 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
    Mortar and Brick or an online e-tailer are service based, the answer to staying in the game and being competitive is understanding the customer needs better to serve them well. Which 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.

Data Analytics Applications:-

  • Healthcare Sector
    The big challenge for hospitals with cost pressures is to treat as many patients as they can treat efficiently, also keeping in mind the improvement of the quality of care. Instrument and machine data are being used increasingly to track as well as optimize patient flow, treatment, and equipment used in the hospitals. 
  • Travel Sector
    Personalized travel recommendations can also be delivered by data analytics based on social media data. As again recommendation features are being used in the travel sector as well. Data analytics can optimize the buying experience through mobile/ weblog and social media data analysis. Travel sights can gain insights into the customer’s desires and preferences. Products can be up-sold by correlating the current sales to the subsequent browsing increase browse-to-buy conversions via customized packages and offers. 
  • Gaming World
    Data Analytics helps in collecting data to optimize and spend within as well as across games. Game companies gain insight into the dislikes, the relationships, and the likes of the users.
  • Energy Management
    Most of the firms are using data analytics for energy management, including smart-grid management, energy optimization, energy distribution, and building automation in utility companies. The application here is centered on the controlling and monitoring of network devices, dispatch crews, and managing service outages. Utilities are given the ability to integrate millions of data points in the network performance and lets the engineers use the analytics to monitor the network.
Shivam Awasthi
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