data science is the processing and analysis of data that you generate for various insights that will serve a myriad of business purposes. For instance, when you have logged in on Amazon and browsing through a few products or categories, you are generating data. This data will be used by a data scientist at the back-end to understand your behavior and push you re-targeted advertisements and deals to get you purchase what you browsed. This is one of the simplest implementations of data science and it keeps getting more complex in terms of concepts like cart abandonment and more. Data science involves the processes of:
- Data extraction
- Data Cleansing
- Analysis
- Visualization
- And actionable Insights generation
machine learning is part of data science. It draws aspects from statistics and algorithms to work on the data generated and extracted from multiple resources. What happens most often is data gets generated in massive volumes and it becomes totally tedious for a data scientist to work on it. That is when machine learning comes into action. Machine learning is the ability given to a system to learn and process data sets autonomously without human intervention. This is achieved through complex algorithms and techniques like regression, supervised clustering, naïve Bayes and more.
Machine Learning is a particular method of Artificial Intelligence. It is based on training weights of neurons on a Training Dataset, checking the so computed parameter set on a Checking Dataset, and once verified it is used on incoming Datasets to find features, properties of the data in the set.
Do you have a more specific application where you are using machine learning for data analytics?
This mainly depends on your task, but briefly I can say that a data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected datasets.
HTH.
Dr. Samer Sarsam
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