The Noders PTNK
Mini-Course • Coming Soon

Introduction to Data Science
Module 1

Build a solid foundation in data science thinking and gain comprehensive knowledge of the 3 pillars of data (Structured Data, Computer Vision, NLP). This course focuses on problem fundamentals, standard data processing workflows, and practical applications to give you the most hands-on perspective of the field.

High School Students (Grade 10-11)
January 2026
4 Sessions × 1.5 hours

Course Overview

Target Audience

Grade 10-11 students

Approach

Foundation & Real skills

Organizers

The Noders PTNK × PRISEE

Duration

4 sessions (Jan 2026)

What You'll Achieve

Data Thinking

Form correct analytical mindset

Workflow

Standard professional process

Foundation

Ready for advanced levels

Comprehensive

Structured, Vision & NLP

Course Curriculum

4 comprehensive sessions, each 1 hour 30 minutes

1Session

Data Science Thinking & Standard Workflows

Understand the big picture and professional working processes
Topics Covered

Overview

  • Definition & Analytics comparison
  • 3 Pillars: Structured, Vision, NLP

Standard Workflow

  • Collection & Pre-processing
  • Modeling & Visualization

Case Study

  • IELTS score analysis
2Session

Data Processing & Visualization

Master tools for working with tabular data
Topics Covered

Tool Ecosystem

  • SQL for data extraction
  • Pandas for processing
  • Matplotlib for visualization

Hands-on Practice

  • Basic SQL queries
  • Data cleaning with Pandas
  • Insightful chart creation
3Session

Computer Vision & Basic Machine Learning

Image processing and classification algorithms
Topics Covered

Image Fundamentals

  • Pixel Matrix representation
  • Preprocessing: Resize, Grayscale

Algorithms

  • KNN principles & classification
  • CNN & Deep Learning intro

Practice

  • Handwritten digit recognition
4Session

Natural Language Processing & Model Evaluation

Text processing and model assessment
Topics Covered

NLP Fundamentals

  • Text cleaning & Tokenization
  • BoW, TF-IDF, Word embeddings

Model Evaluation

  • Key metrics: Accuracy, F1, etc.
  • Context-based metric selection

Course Material

Lecture Notes

Lecture 1Course Note
Lecture 2Course Note
Lecture 3Course Note
Lecture 4Course Note

Teaching Slides

Lecture 1Presentation Slide
Lecture 2Presentation Slide
Lecture 3Presentation Slide
Lecture 4Presentation Slide

Practice Notebooks

Lecture_2_Demo.ipynb

SQL, Pandas & Visualization

Hands-on practice with data querying, manipulation, and creating insightful visualizations

Lecture_3_Demo.ipynb

KNN Algorithm & Computer Vision

Build classification models and explore image processing fundamentals

Lecture_4_Demo.ipynb

Sample Auto Essay Scoring model

NLP application demo with model evaluation metrics