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"Math Meets Machine: High School Data Science Essentials 📊✖️➗
Step into the world of numbers and algorithms with our High School Data Science course, tailored to prepare you for the ChatGPT and Generative AI revolution!

This course is inspired from the renown lesson from Stanford University that has over 100,000 students enrolled

What is this course about?

Embark on a voyage into the heart of data science with our dynamic curriculum, designed specifically for students to navigate the vast seas of data. Utilizing complimentary tools like Google Sheets, Python, and Data Commons, our learners will transform into data detectives, unraveling mysteries in project-centered learning modules. They will cultivate a deep-rooted comprehension of data analysis, the art of sampling, the intricate dance between correlation and causation, as well as the concepts of bias, uncertainty, and probability. Students will hone their skills in modeling with data, crafting and critiquing data-driven arguments, recognizing the influential role of data in our society, and beyond

Topics Covered

Episode 1
  • Variability, data and models

  • Data Science inquiry

  • Univariate, bivariate and multivariate data

  • Creating visual representations

  • Data cleaning

Episode 2
  • Using measures of center and spread to model data

  • Distributions and normal distributions

  • Sampling and variability

  • Probabilistic thinking

Episode 3
  • Linear regression and bivariate data

  • Using probability to analyze the fit of a regression

  • Spurious correlations, confounding and mediating variables

  • Make connections between the trend and the context to make predictions

Episode 4
  • Algorithmic Thinking

  • Basics Programming

  • Simulation

  • Variability

  • Theoretical and Experimental Probability

  • Conditional Probability

Episode 5
  • Foundations in Linear Algebra

  • Introduction to clustering

  • Two-way tables

Episode 6
  • Model Bias

  • Normalization and weighting of data

  • Forming mathematical models

  • Sensitivity analysis

Episode 7
  • Predictive modeling

  • Linear Algebra

  • Conditional Probability

Episode 8
  • Gathering and organizing data

  • Modeling

  • Analyzing and synthesizing

Data Science Portfolio

We're passionate about project-based learning, and we're confident that the following projects will spark inspiration and ignite a love for hands-on learning!

Course Detail

Recommended Level                          Grade 9 - 12 | Year 10 - 13 | Matthayom 3 - 6

Number of sessions per episode        12

Session Duration                                  1.5 hour

Language                                              English and Thai

Type of class                                         Online and On-site

Instructor-to-student ratio                    1:1 (Private Class) และ 1:4 (Group Class)

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