Duration 3 days – 21 hrs
Overview
A practical, business-focused introduction to data analytics. Participants learn the full analytics lifecycle from framing questions and preparing data to analyzing, visualizing, and communicating insights using familiar tools (Excel/Google Sheets) plus beginner-friendly SQL.
Objectives
- Explain the analytics lifecycle and the roles of descriptive, diagnostic, and prescriptive analytics.
- Evaluate data quality (completeness, accuracy, consistency, timeliness) and document data with simple dictionaries/metadata.
- Clean and reshape datasets (filter, sort, deduplicate, join/merge) in Excel/Sheets and via basic SQL (SELECT, WHERE, GROUP BY, JOIN).
- Compute and interpret core descriptive statistics and explore distributions/outliers.
- Run exploratory analysis (segments, cohorts, correlations, Pareto/80-20) and avoid common causation pitfalls.
- Apply fundamentals of inference: sampling concepts, confidence intervals, and simple hypothesis tests (t-test, proportion test, chi-square) to business questions.
- Build clear, audience-appropriate dashboards and tell a concise data story (SCQA, Before/After, “So what?”).
- Translate analysis into decisions with recommendations, assumptions, risks, and next-step plans.
Audience
- New or aspiring data analysts, BI/reporting specialists, and business users (marketing, operations, HR, finance, product) who work with data and dashboards.
- Team leads/managers who need to interpret analyses and make data-driven decisions.
Pre- requisites
- Comfortable with spreadsheets (basic formulas, sorting/filtering).
- Basic numeracy; no prior statistics or coding required.
- Laptop with Excel (or Google Sheets) installed; course datasets provided. Optional: DB Browser for SQLite (or equivalent) for SQL labs.
Course Content
Day 1 Data Literacy, Wrangling & Descriptive Analytics
Analytics Foundations
- Analytics lifecycle: business question → data → analysis → insight → action
- Types of analytics: descriptive, diagnostic, prescriptive
- Data ethics, privacy, and governance (essentials)
Working with Data
- Concepts: data types, structure, granularity, primary/foreign keys
- Data quality dimensions: completeness, accuracy, consistency, timeliness
- Documentation: data dictionaries & lightweight metadata
Wrangling Essentials (Hands-on)
- Techniques: reshaping, filtering, sorting, deduplication
- Joins/merges: inner, left, right—when/why to use each
- Calculated fields & date operations
- Tools: Excel/Sheets; Intro to SQL — SELECT, WHERE, GROUP BY, JOIN
Descriptive Analytics & Visualization
- Central tendency & variability: mean, median, mode, stdev, percentiles
- Exploring distributions & spotting outliers
- Visualization principles: chart choice, readability, avoiding clutter
Lab: Build a profile dashboard (sales/ops) with tables & charts
Day 2 Exploratory Analysis & Inferential Statistics
Exploratory Data Analysis (EDA)
- Relationships: scatterplots, correlation; pitfalls of causation
- Segmenting & slicing: cohorts, funnels, Pareto (80/20)
- Lab: Explore a real-world dataset; derive 2–3 actionable insights
From Questions to Tests
- Sampling: populations vs samples, bias, sampling error
- Confidence intervals: intuition & managerial interpretation
- Hypothesis testing basics: t-test, proportion test, chi-square—business use cases
- Lab: Run and interpret a simplified A/B test (e.g., Campaign A vs B)
BI Dashboards & Data Storytelling
- Dimensions vs measures, KPIs, lagging/leading indicators
- Filters, drilldowns, and layouts for execs vs analysts
- Communicating findings: SCQA, “Before/After,” and the “So what?”
- Lab: Build a simple KPI dashboard with a short narrative & call-to-action
Day 3 Decision Analytics & Capstone Project
Business-Focused Analysis & Interpretation
- From insight to decision: recommendations, assumptions, risks
- Analysis planning: experiments, follow-ups, deeper dives
- Packaging insights: exec summaries, charts, reproducible workflows
Practical Tools & Templates
- Structured analysis templates in Excel/Sheets
- SQL snippets & pivot tables for repeatable queries
- Checklist for data-driven presentations
Capstone (Team or Individual)
Choose a business scenario (marketing, operations, HR, etc.) and perform an end-to-end analysis:
- Data cleaning & wrangling
- Descriptive/EDA techniques
- Visual reporting & KPIs
- Framing and presenting insights
