Week 4 - BSIT380-T303 System Hardening and Network R - Define data analytics.

Data analytics is the process of examining raw data to uncover patterns, trends, and insights that inform decision-making. It involves collecting, organizing, and analyzing data to generate meaningful conclusions and predictions. This field plays a critical role across industries, enabling businesses to optimize operations, improve customer experiences, and drive innovation.

The data analytics process typically follows four key stages:

  1. Data Collection: Gathering relevant data from various sources such as databases, social media, or IoT devices.
  2. Data Cleaning: Preparing the data by removing errors, duplicates, or inconsistencies to ensure accuracy.
  3. Data Analysis: Applying statistical, mathematical, and computational techniques to interpret the data.
  4. Data Visualization: Presenting the results through charts, graphs, and dashboards to make the insights actionable.

Data analytics can be descriptive (what happened?), diagnostic (why did it happen?), predictive (what will happen?), or prescriptive (what should we do?). These approaches empower organizations to anticipate challenges, seize opportunities, and stay competitive in an increasingly data-driven world.

As technology advances, the demand for skilled data analysts continues to grow, making it a vital and dynamic career field.

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