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.