Data analytics

Data analytics is the systematic study of raw data to extract meaningful information. It involves a range of processes and methods, the main purpose of which is to improve business efficiency and facilitate informed decision-making.

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

• Descriptive analytics. This form of analytics interprets historical data to understand past behavior.
• Diagnostic analytics. This allows for a deeper examination of data to determine the cause of a specific result.
• Predictive analytics. As the term suggests, this type of analytics uses data to predict future events.
• Prescriptive analytics. This advanced 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 lumped together, 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 that focuses on analyzing and interpreting data sets.

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.
• Data cleaning. This step ensures the quality and accuracy of the data by eliminating errors and inconsistencies.
• Data analysis. The cleaned data is subjected to statistical analysis.
• Data visualization. The results of the analysis are visualized using graphical representations to facilitate understanding and interpretation.