Data Science vs Big Data vs Data Analytics

11th July 2018

In a region where there is so much talk around the Big Three – Data Science, Big Data, Data Analytics, I thought I’d share some information that will help you understand the different between the three. Trust me… they’re different.

1.    What they are?

Data Science – Mining large amounts of structured and unstructured data to identify patterns. Includes a combination of programming, statistical skills, machine learning and algorithms.

Big Data – Refers to humongous volumes of data; includes capturing data, data storage, data sharing, data querying.

Data Analytics – Process and perform statistical analysis of data; discover how data can be used to draw conclusions and solve problems.


2.    What they do?

Data scientists – Predicts the future based on past patterns; explores and examines data from multiple disconnected sources; develop new analytical methods and machine learning models.

Big data professionals – Analyse system bottlenecks, build large-scale data processing systems, architect highly scalable distributed systems.

Data analysts – Acquire, process, and summarize data; package data for insights; design and create data reports using various reporting tools.


3.    Where it is used?

Data science – Search engines, financial services, e-commerce

Big data – Financial services, communications, retail

Data analytics – Healthcare, travel, IT industry


4.    What skills you will need.

Data science – Programming skills, like SAS, Python; statistical and mathematical skills, storytelling and data visualisation; Hadoop, SQL skills; machine learning.

Big data – Programming languages like Java, scala; NoSQL databases like MongoDB, Cassandra DB; Frameworks like Apache Hadoop; Excellent grasp of distributed systems.

Data analytics – Programming skills like SAS and Python; statistical and mathematical skills; data wrangling skills; data visualisation skills.

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