Data Science and Data Analytics
Data Science and Data Analytics are related subjects but they have distinct differences. Data Science is a broad term for developing and using scientific methods, processes and algorithms to analyse large sets of raw and structured data such as big data. Data scientists look for answers to questions that businesses and organisations don’t know, or establish solutions to problems that haven’t yet been thought of. Data Analytics is more focused and concentrates on carrying out statistical analysis on existing data sets. It can be used to measure events in the past, present, or future. Data Analytics tends to be slightly more business and strategy focused.
Core skills needed in Data Science and Data Analytics include programming, machine learning, statistics and statistical analysis. Artificial Intelligence (AI) is also becoming important due to advances in data driven AI.
Data analytics focuses more on viewing the historical data in context while data science focuses more on machine learning and predictive modeling. Data science is a multi-disciplinary blend that involves algorithm development, data inference, and predictive modeling to solve analytically complex business problems.
Graduate careers in Data Science and Data Analytics
- ecommerce and retail
- digital technologies
- robotics
- healthcare
- financial technology (Fintech)
- legal technology (Lawtech)
- automotive (self-driving cars)
- cyber security
- energy and utilities
- public sector
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