Data Science – the art of understanding and inferencing Big Data
Analyze, learn, and predict critical business objectives through machine learning.
The problems of understanding Big Data:
The first challenge of analyzing data is understanding whether it is useful.
Most data is like distracting noise.
Before you determine its utility, you need to access the data and understand what it even means in its raw form.
Then you need to clean and standardize the data before you deploy any machine learning algorithms or statistical models.
Once it’s all done there’s still the challenge of explaining your findings to normal people. Slow. Tedious. Repetitive.
Machine Learning Solutions
Machine Learning utilizes artificial intelligence to leverage data to continuously improve data understanding and building methods.
Generally, there are two types of ML algorithms – supervised and unsupervised.
A supervised algorithm analyzes the training data and produces an inferred function which can map new examples in a target column. Common examples include Linear Regression, Random Forest, and Extreme Gradient Boosting.
An unsupervised learning algorithm is used to find unknown patterns in the data without having any pre-assigned labels or scores.
Common examples include Clustering and Principal Component Analysis.
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