BrainBit Infotech TeachingFoundations · Beginner · 2 hours
Data Science for Beginners
A first session on data science — what it is, what a full course covers, and why now is a good time to start — for a room that has never written a line of code.
Syllabus
| Module | Covers |
|---|---|
| Part 1 · What is Data Science? | A simple definition, the three skill areas, everyday examples, the lifecycle, AI vs ML vs deep learning, job roles, the four types of analytics |
| Part 2 · What a course covers | An 8-module, 24-week roadmap from Python to machine learning, hours per module, the tool stack, and portfolio projects with local data |
| Part 3 · Why start in 2026? | Job-growth figures, six reasons to start, a career ladder, myths vs facts, and a 30-day starter plan |
What your students will be able to do
- Define data science in one sentence and give everyday examples of it
- Explain how statistics, programming and domain knowledge meet in data science
- Name the six steps of the data science lifecycle
- Tell AI, machine learning and deep learning apart
- Compare the data analyst, data scientist, ML engineer and data engineer roles
- Describe descriptive, diagnostic, predictive and prescriptive analytics
- Follow a 30-day plan to start learning Python, pandas and charts
Questions teachers ask
Is this a full data science course?
No. It is a single introductory session of about two hours: an orientation or seminar that explains the field and lays out what a full course would cover.
Do students need to know programming or maths?
No. The session assumes neither. It ends with a 30-day plan that starts from Python basics.
Where do the job-growth figures come from?
The US Bureau of Labor Statistics projections for data scientists (2025 to 2035) and the World Economic Forum Future of Jobs Report 2025, both cited on the closing slide.