Data Blog by Lizeo
What is data science?
The use of data science is beneficial to making informed business decisions and projections using predictive analytics, prescriptive analytics as well as machine learning. A data scientist uses their knowledge of statistics and associated models to explore and discover raw data using a variety of statistical algorithms.
Creating algorithms and examining data from various perspectives allows a data analyst or data engineer to identify and predict future events. Using this form of modern technology is essential to a business’ growth to find solutions and to make informed assumptions.
A data scientist can then present their findings transformed from raw data into more accessible forms such as data visualisations, which are more accessible to operational teams. This methodological process includes:
- Predictive causal analytics: the model of predicting possible outcomes or future events.
- Prescriptive analytics: the intelligence model that makes proactive decisions and adapts to dynamic changes.
- Predictive machine learning: the model which ascertains any trends in the future.
Data science projects are categorised by the following 4 steps:
- Defining the scope of the project: products, price range, timeline, markets, location, etc.
- Implementing the Data Lake: collection of raw data, analysis, cleaning, preparation.
- Data analysis: statistics and modelling (learning and validation).
- Data industrialisation: deployment of the model, data visualisation.
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