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Data Science vs Big Data

So what is different about these two big boys in the world of data? Big data mostly includes data of sizes that the traditional data-processing application software is incapable of handling. Big data is often characterized by Volume - large volumes of data, Variety - wide Variety of datasets, Velocity - the velocity with which data is generated. Big data deployments often include terabytes, petabytes, and even exabytes. Businesses that use big data have a competitive advantage over those who do not because of the accumulated data which can provide valuable insights to help create marketing campaigns to increase engagement & sales.

Data Science is a tool used to extract information from big data that is subjected to further processes such as data cleansing, analysis, and preparation. Data scientists use different techniques to obtain answers which include predictive analytics, statistics, and ML to parse through data sets and establish solutions to problems that haven't been thought of yet. Data Science is largely used for a goal, one which does not look for solutions or answers but finding the right question to ask.

Applications Big Data Vs Data Science

Big data

  1. Precision Medicine

    IBM is predicting heart diseases with Big Data. Analysis of health record data could now reveal symptoms much earlier or warn the patient even before he/she shows symptoms. IBM uses Apache Unstructured Information Management Architecture (UIMA) to extract the signs/symptoms of heart failure from the data sets. Even without a single dominating indicator, several contributors (weak/comorbidities) such as diabetes, hypertension, genomic data can be analyzed. Drawing probabilities from size-differing and disparate databases are what Big Data does.

  2. Retail

    Retail is a dynamic and competitive market and thereby the increase in adoption of Big Data by retail channels. This has helped retailers in reaching the target customers, understanding their needs, and providing them with the best possible solutions to improve sales, customer satisfaction, etc. Big data has hugely helped in personalizing the customer experience, predicting demands, operational efficiency, and customer journey analytics which culminate in a purchase.

  3. Media and Entertainment

    The growth of active users on digital media has increased from 20% to over 80% in the last five years. The data generated by the Media & Entertainment companies are large volumes with a variety of information and big data technologies can harness these datasets. M & E companies leverage these data assets to drive more value to their consumers by curating and personalizing content for anyone on the internet. This valuable data provides a significant impact on improving customer satisfaction & operational efficiency.

Data Science

  1. Manufacturing

    Data Science is used by manufacturing companies for optimizing products, reducing costs, and increasing revenues/profits. Industries use Data Science to constantly monitor energy costs & for optimizing production hours. A thorough analysis of customer reviews can help industries make informed decisions to improve their products. With the increased use of automation in industries with the help of historical & real-time data, companies have increased production capabilities more than before.

  2. Fraud & Risk Detection

    Data Science can be used to power algorithms that can predict patterns and behaviors which can help identify frauds. With the use of these predictive models, companies can save money and time with increased efficiency.

  3. Social Media

    Have you heard of sentiment analysis? Analyzing data of a person's social media behaviour is a form of sentiment analysis. This enables companies to build a persona of you without ever interacting with you. Christian- Rudder, co-founder of OkCupid (online dating site) revealed in one of his interviews that with all the data that was collected, they could make outlandish predictions. For example, whether the customer's parents have divorced before they were 21 years old. This information enhances customer engagement and satisfaction, thereby improving company growth and revenue curve.

In conclusion, Big Data and Data Science play a big role in each of our lives who have access to the internet. There are some common tools and languages for both Big Data and Data Science. Skill Sigma offers Certification courses with projects and internships for our students in each of these fields. Know more about our Data Science- AI ML Specialization course here.



Complete Introduction to Data Science Course
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