Data Analytics

Data analytics is a methodical study of raw data to extract meaningful information. It includes a number of processes and methods, the main purpose of which is to increase business efficiency and facilitate informed decision-making.

The spectrum of data analysis
Data analytics is a broad field covering several different types:

  • descriptive analytics. This form of analytics interprets historical data to understand past behavior;
  • diagnostic analytics. Allows you to study the data more deeply to find out the reason for a specific result;
  • predictive analytics. As the term implies, this type of analytics uses data to predict future events;
  • prescriptive analytics. This extended branch of analytics recommends actions to achieve optimal results.

Data Analytics and data science: the difference between them
Although data analytics and data science are often combined, they represent different aspects of the data spectrum. Data science is a broader field that includes data analytics, machine learning, and other related disciplines. Conversely, data analytics is a subset of data science focused on the analysis and interpretation of datasets.

The data analysis process
Data analysis consists of several stages, each of which is an integral part of the overall process:

  • data collection. The initial step involves collecting data from various sources;
  • data processing. The collected data is systematized and prepared for analysis;
  • clearing the data. This step ensures the quality and accuracy of the data by eliminating errors and inconsistencies;
  • data analysis. The cleared data is subjected to statistical analysis;
  • data visualization. The results of the analysis are visualized using graphical representations to facilitate understanding and interpretation.
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