Analytics and machine learning in data science

Data science is the study of raw information called data. Data science involves identification of what data represents and transformation of the mined data into valuable resource for the progress and growth of the business organization. The data presented to the data scientist for study can be both structured and unstructured data. This study of structured and unstructured data helps to identify the textures and patterns in the data that can be useful for reining costs, boost efficiency, and increase competitive advantage of business and recognition of new market opportunities. The data science field deals with machine learning, mathematics, statistics, incorporation of techniques, etc. You can know more about data science from ExcelR.

What is Analytics?

Analytics is an important part of business without which it is impossible to run any business for a long time. Analytics is the tool that describes critical thinking of the organization. Analytics was not given much importance in the earlier years. However, with increase in competition in the business the need for analytics has risen speedily. The term is used to describe the critical thinking which is quantitative in nature. It is the practice of analysing data in different ways to make quick and smart business decisions. Use of raw data in order to build a predictive algorithm falls under the scope of analytics. However, it is not limited to building of predictive algorithm. Analytics is a broad term which goes beyond the semantics of a particular term.

What is machine learning?

Machine learning is closely associated with the field of data science. Machine learning revolves around data modelling with broad class of methods. It is generally used for making predictions and decipher of patterns.

  • Making predictions: Making predictions is the core objective of using structured and unstructured data. It is used for making predictive models from the available data. Observations that holds ground truth is known by the term tagged data. Training models that are formed from tagged data are used for predicting tags for unknown data points. It follows supervised learning paradigm.
  • Pattern discovery: It follows unsupervised learning paradigm to identify underlying patterns without any existing ground truth. In this broad category of machine learning methods are clustering technique. Clustering technique is used for detection of natural groupings existing in the data set. There are various other unsupervised methods like topic models, principal component analysis, hidden markov models, etc.

 

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