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1: Foundations of Data Analytics

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    Introduction

    In this chapter, we will establish the foundation of Data Analytics

    Learning Objectives
    • Define data analytics and explain its role in transforming raw data into actionable insights for decision-making.
    • Distinguish between the four types of analytics — descriptive, diagnostic, predictive, and prescriptive — including the key question each answers and how they progress in value and complexity.  
    • Apply the analytics type framework to real-world scenarios by identifying which type of analytics is being used in a given example.
    • Outline the stages of the data-to-insight lifecycle — from defining goals and data collection through to visualization and action — and explain the purpose of each stage.
    • Explain why the data analytics lifecycle is iterative rather than strictly linear, and identify scenarios where revisiting earlier stages is necessary.  
    • Differentiate between the roles of data analyst, data scientist, and data engineer, including each role's primary responsibilities, skill sets, and contribution to the analytics process.
    • Describe how the three key data roles collaborate to transform raw data into organizational value within a data-driven enterprise.

    Thumbnail: Extract Transform Load by Felix Amoruwa is licensed CC BY 4.0

    This page titled 1: Foundations of Data Analytics was last modified on Sat, 11 Apr 2026 22:22:54 GMT and is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Felix Amoruwa.