Nowadays most of the industries are depending on data and computer science for most of the work processes seeking automation and AI for faster and more precise workflow, however this Domain is still obscure to many people including university students and business owners that might get involved in such an industry. This article will cover the difference between Data Science, Big Data and Data Analytics.

1. What they are?

Data science: Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data.

Big Data: Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

Data Analytics: the process of examining datasets to draw conclusions about the information they contain. Data analytic techniques enable you to take raw data and uncover patterns to extract valuable insights from it.

2. Where is it used?

Data science: used for search engines and search browser, financial services, and E commerce.

Big Data: mainly for financial service, communication, and retail.

Data Analytics: healthcare, travel and IT industry.

3. What they do?

Data scientists: predict the future based on past patterns, explore and examine data from multiple disconnected sources, develop new analytical methods and machine learning models.

Big Data Professionals: analyze system bottlenecks, build large scale data processing systems and architect highly scalable distributed systems.

Data analysts: they acquire, process and summarize data, they also package data for insights, design and create reports using multiple and various reporting tools.

4. What skills you need?

Data science:

  • Programming skills like SAS, R, PYTHON.
  • Statistical and mathematical skills.
  • Storytelling and Data visualization.
  • Hadoops SQL skills.
  • Machine learning.

Big Data:

  • Programming languages like JAVA, SCALA.
  • NoSQL databases like MongoDB, Cassandra DB.
  • Framework like Apache Hadoop.
  • Excellent grasp of distributed systems.

Data Analytics:

  • Programming skills like: SAS, R, PYTHON, JAVA.
  • Statistical and mathematical skills.
  • Data Wrangling skills
  • Data visualization skills

For more information about these courses, you can contact Edugate Gateway Educational Consultants in Australia.