The data analytics life cycle is a structured process used to extract insights from data. It begins with data discovery and collection, followed by data preparation and cleaning. Next comes data analysis and modeling to uncover patterns and trends. Finally, insights are interpreted, visualized, and shared for decision-making. This cycle ensures a clear, repeatable approach to turning raw data into valuable business intelligence.

Understanding data types and formats is essential for effective data analysis. Common data types include numerical (integers, floats), categorical (labels, categories), text (strings), and boolean (True/False). Data can come in various formats such as CSV, JSON, Excel, or SQL databases. Recognizing the type and format helps in choosing the right tools, cleaning methods, and analysis techniques for accurate results.

Basic statistics help summarize and understand data. Key measures of central tendency include:

• Mean: the average value of a dataset,

• Median: the middle value when data is ordered,

• Mode: the most frequently occurring value.

These measures provide insights into the typical values in a dataset and are foundational in data analysis.

A data analyst plays a crucial role in helping businesses make data-driven decisions. They collect, clean, and interpret data to uncover trends, patterns, and insights. By creating reports, dashboards, and visualizations, they communicate findings to stakeholders clearly. Their work supports strategic planning, performance tracking, and process improvements across departments.