Data Scientist Jr. Courses

Train with real data scientists!

  • Students must be 9-12 years of age

  • All section enrollments are capped at 5 students

  • Each course covers the entire data science process so the student has a holistic understanding of the data scientist's duties

  • Classes are highly interactive and hands-on

  • Students must complete homework after each lesson

  • Each 5-lesson course ends with a final project, and a certificate of completion

  • We only use software programs that are available for free

  • Full refund if unsatisfied after first lesson

  • A student can always re-take a course already completed for a US$30 processing fee (when space is available)

  • $30 credit towards a future course when you refer a friend, who also receives a 10% Friends and Family discount

dsjr Level1

Introduction

  • the data table

  • identification of basic data issues

  • introduction to visualization

  • data calculation

  • introduction to analytical models

 

Students will complete a class project together

 

Register Now

dsjr Level 2

intermediate

  • reinforcement of skills and concepts from Level-1

  • identification of additional data issues

  • basic data transformation

  • bivariate visualization

  • process automation

Students will complete a class project together

dsjr level 3

advanced

  • reinforcement of skills and concepts from Level-2

  • identification of advanced data issues

  • advanced data transformation

  • complex visualization

  • data analysis

  • advanced process automation

Students will complete independent projects and present on last day

Machine learning 1

mr with scratch

  • Distinguish Machine Learning from traditional coding

  • Describe the process of learning

  • Calculate learning performance using Mean Absolute Error and Confusion Matrix

  • Discuss confidence in prediction

  • Experience a classification application (Titanic)

machine learning 2

ml to ai

  • Experience a more complex classification application (Zombie Hotel)

  • Examine the algorithmic inner workings of a simple neural network

  • Use confusion matrix to monitor learning progress

  • Experience applications in the vision, sound and motion domains

Interactive visualizations students will make in class!