Data Analytics for Non-Tech Users

About Course

This beginner-friendly course demystifies data for non-technical learners, empowering them to understand, interpret, and communicate data confidently. This masterclass focuses on practical data literacy, real-world applications, and decision-making skills. Learners will explore how data flows through organizations, how to ask the right questions, and how to use data to solve problems without writing code.

What data is and why it matters in everyday life and work
Types of data: structured, unstructured, qualitative, quantitative
How data is collected, stored, and managed
How to read and interpret charts, tables, and dashboards
The four types of analytics: descriptive, diagnostic, predictive, prescriptive
How to ask good questions and make data-driven decisions
Data ethics, privacy, and responsible use
How to communicate insights using visuals and storytelling

Course Content

Module 1 — What is data and why does it matter?
Understand the role of data in everyday decisions.

Definitions and examples of data
Structured vs. unstructured data
Data in business, health, education, and society
Data lifecycle: collection → storage → analysis → action

Module 2 — Reading and interpreting data
Learn to make sense of tables, charts, and dashboards.

Module 3 — Asking good questions with data
Frame problems and explore data meaningfully.

Module 4 — Types of analytics and decision-making
Use data to understand, explain, and predict.

Module 5 — Communicating insights with visuals
Share findings clearly and persuasively.

Module 6 — Data ethics and responsible use
Understand the risks and responsibilities of working with data.

Module 7 — Capstone: Data in action
Apply what you’ve learned to a real-world scenario.

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About Course

What Will You Learn?

Course Content

Module 1 — What is data and why does it matter? Understand the role of data in everyday decisions.

Definitions and examples of data

Structured vs. unstructured data

Data in business, health, education, and society

Data lifecycle: collection → storage → analysis → action

Module 2 — Reading and interpreting data Learn to make sense of tables, charts, and dashboards.

Common chart types: bar, line, pie, scatter

Tables and cross-tabulations

Dashboards: what they show and how to read them

Spotting trends, patterns, and outliers

Module 3 — Asking good questions with data Frame problems and explore data meaningfully.

Types of questions: what, why, how, what if

Data sources and reliability

Bias and assumptions in data

Building a data inquiry framework

Module 4 — Types of analytics and decision-making Use data to understand, explain, and predict.

Descriptive analytics: what happened?

Diagnostic analytics: why did it happen?

Predictive analytics: what might happen?

Prescriptive analytics: what should we do?

Module 5 — Communicating insights with visuals Share findings clearly and persuasively.

Choosing the right chart for the message

Visual design principles: clarity, contrast, context

Storytelling with data: structure and flow

Presenting to different audiences (managers, clients, public)

Module 6 — Data ethics and responsible use Understand the risks and responsibilities of working with data.

Privacy and consent

Misuse and manipulation of data

Inclusive and fair data practices

Real-world examples of ethical dilemmas

Module 7 — Capstone: Data in action Apply what you’ve learned to a real-world scenario.

Choose a topic (e.g., school performance, local business, health trends)

Frame a question and explore available data

Create a visual summary and decision memo

Present findings in a short report or slide deck

Earn a certificate

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