Differentiating between Data Science, Big Data and Data Analytics
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Jun 29, 2018 | 10206 Views
Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Data science and data analytics, people working in the tech field or other related industries probably hear these terms all the time, often interchangeably. However, although they may sound similar, the terms are often quite different and have differing implications for business. Knowing how to use the terms correctly can have a large impact on how a business is run, especially as the amount of available data grows and becomes a greater part of our everyday lives.
Mining a large amounts of structured and unstructured data to identify patterns and it includes a combination of programming, statistical skills, machine learning and algorithms. It basically deals with data cleansing, preparation and analysis.
What data scientists do?
Predicts the future based on past patterns.
Explores and examines data from multiple disconnected sources.
Develop new analytical methods and machine learning models.
Leverage data for business.
Deliver actionable insights from the data.
Various skills are used for data science like programing skills SAS, R and Python, statistical and mathematical skills, story-telling and data visualization, Hadoop, SQL skills and machine learning skills.
Refers to humongous volumes of data it includes capturing data, data storage, data sharing and data querying. It deals with the analysis of insights for better business decisions.
What big data professionals do?
They analyze system bottlenecks and find solutions.
Build large scale data processing systems.
Architect highly scalable distributed system.
Articulate pros and cons of various technologies and platforms.
Analyze business problems and technical environments.
Various skills are used for big data are programming languages like Java, Scala, NoSQL databases like MongoDB, Cassandra DB, framework like Apache Hadoop and excellence grasp of distributed systems.
Process and perform statistical analysis of data and discover how data can be used to draw conclusions and solve the problems. This is basically science of examining raw data and it deals with deriving insights from the information.
What data analysts do?
They acquire process and summarize data.
Store the data for insights.
Design and create data reports using various reporting tools.
Manage the quality assurance of data scraping.
Query database and package data for insights.
Various skills are used for data analytics are programming skills like SAS, R and Python, statistical and mathematical skills, data wrangling skills, data visualization skills.