Analytics & Data Science

Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions.

Statistics is at the core of sophisticated machine learning algorithms, capturing and translating data patterns into actionable evidence. Data scientists use statistics to gather, review, analyze, and draw conclusions from data, as well as apply quantified mathematical models to appropriate variables

A NoSQL (originally referring to "non-SQL" or "non-relational") database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases.

Hypothesis testing is an act in statistics whereby an analyst tests an assumption regarding a population parameter. The methodology employed by the analyst depends on the nature of the data used and the reason for the analysis.

Python's built-in analytics tools make it a perfect tool for processing complex data. Python's built-in analytics tools can also easily penetrate patterns, correlate information in extensive sets, and provide better insights, in addition to other critical matrices in evaluating performance.