Foundations of Data Analytics for Business
Learn how to frame business problems, understand data, define KPIs, follow a reliable analytics process and communicate decisions with evidence.
Your step-by-step learning path
Repeat these four steps for each chapter. Keep the reading separate from your activities.
Start with the instructor video, then study the textbook-style chapter.
Complete the guided task using the relevant business dataset.
Attempt 5 scored MCQs plus 2 written case questions.
Build the module assignment and complete the scored practical assessment.
Module 01 · Six chapters
Select a chapter to open the video and book-style lesson. All six are available in this standalone package.
Role of analytics in business; descriptive, diagnostic, predictive and prescriptive analytics; analyst versus business analyst versus data scientist; real-world applications.
Problem statements; stakeholder interviews; business requirements; SMART objectives; assumptions; scope; constraints; success criteria.
Structured, semi-structured and unstructured data; data types; CSV, JSON and Excel; database concepts; granularity; data lifecycle.
Revenue; gross profit; contribution margin; growth; conversion; retention; churn; CSAT; KPI trees; numerator/denominator definitions.
Business understanding; data sourcing; cleaning; exploration; validation; analysis; reporting; CRISP-DM-style iterative workflow; quality gates.
Audience analysis; chart interpretation; insight versus observation; evidence-based recommendations; assumptions; limitations; executive summaries.