Overview

Data analytics is the process of analyzing and interpreting data using statistical and computational methods to uncover meaningful insights and make data-driven decisions.

  • Involves a wide range of techniques such as:

    • Data mining

    • Machine learning

    • Data visualization

    • Predictive modeling

  • Used in a variety of industries such as:

    • Healthcare

    • Finance

    • Retail

    • Marketing

  • Data analytics allows organizations to analyze large amounts of data and identify patterns, trends, and correlations that can help them:

    • Improve operations

    • Reduce costs

    • Increase revenue

  • The data analytics process typically involves several steps, including:

    • Data collection

    • Data preparation

    • Model planning

    • Model execution

    • Communication and visualization

  • Professionals use a variety of tools and technologies to perform these tasks, including:

    • Programming languages like Python and R

    • Statistical software packages

    • Data visualization tools

Overall, data analytics plays a critical role in helping organizations make informed decisions based on data-driven insights and is becoming increasingly important as the amount of data generated by businesses continues to grow.

Course Competencies

  1. Careers and Roles in Data Analytics

  2. Data Analytics Lifecycle

  3. Defining Values for Success in Data Analytics