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dscamp_public
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Materials for Data Science camp
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# Data Science Camp materials This repository contains code and materials for Data Science camp. Please see outline of the modules below. To learn about specific module and access its learning materials, navigate to the corresponding directory. # Outline |Day | Morning Module | Afternoon Module | |:----|:---------------|:-----------------| |Monday | Intro to Data Science/ Motivating examples for each session/Set-up in Google Colab | Python Programming/Q&A for Google Colab Set-up | |Tuesday | Python Programming | Advanced Modeling I (Linear Models, Neural Networks) | |Wednesday | Advanced Modeling II (Convolutional Neural Networks, Tree-based Models) | Object Detection: introduction and data collection | |Thursday | Object Detection: model initialization and structure design | Object Detection: parameter tuning and optimization | |Friday | Object Detection: preparation and final presentation | # Modules Each of the modules is designed to be a self-contained units, with themes that are connected across the models. ## Introduction to Data Science (1 unit) **Motivating Example(s)**: Many AI in the world today; **Topics:** -- Introduce Data Science and AI; Examples from the modern world; Give highlight/overview of project; **Objectives:** -- Learning from Data w/Computers **Project(s):** No Project **Curriculum Threads:** What is a model; show how do computers fit it **Materials:** Introductory material; motivating examples ## Introduction to Python **Motivating Example(s)**: Lint Code **Topics:** Starting and Using Jupyter Notebooks; Assignment, Control Loops, Data Structures **Objectives:** -- Introduce basic syntax of Python programming language and its ecosystem; **Project(s):** A competition on Lint Code **Curriculum Threads:** Optimization -- Computer does it for you **Materials:** Pet examples from Lint Code ## Advanced Modeling I (1 unit) **Motivating Example(s)**: Photo Classification on the Social Network **Topics:** Intro to Linear Models and Motivation, Biological and Artificial Neural Networks, Splines and Nonlinear Regression, Artificial Neural Network Structure(s) **Objectives:** At the conclusion of the unit, students will be able to * Conceptualize simple linear models * Describe the relationships between artificial neurons and biological neurons * Build simple neural networks using TensorFlow * Build a neural network to classify images using TensorFlow **Project(s):** Object Detector **Curriculum Threads:** Modeling (Biological model vs Mathematical), Optimization (Gradient Descent) ## Advanced Modeling II (1 unit) **Motivating Example(s)**: Photo Classification on the Social Network **Topics:** Convolutional Networks, Trees, Ensembles **Objectives:** At the conclusion of the unit, students will be able to * Use a Neural Network model to build an object detector. * Learn tree-based Models * Introduction to ensemble method and model averaging **Project(s):** Object Detector ## Project: AI Startup (1 unit) **Motivating Example(s)**: Applications from the course of the week **Topics:** Applications of Machine Learning and AI **Objectives:** Teams of students design an AI startup company, using the principles of AI and machine learning from the course of the camp. **Project(s):** Student-generated startup "pitches" to convince a group of venture capitalists to invest in their startup. **Curriculum Threads:** Applications of AI ## Presentation and Wrap-Up (1 unit) **Topics:** Applications of Machine Learning and AI, Careers in AI **Objectives:** * Students give their pitches to a panel of "investors"/judges. The top group may win an edible prize, if available. * A panel of instructors describe their personal journeys to become data scientists, giving students a picture of the diverse options ahead of them. * Open Question and Answers about Careers and AI careers * Information on additional resources and how to continue learning **Project(s):** Student-generated startup "pitches" to convince a group of venture capitalists to invest in their startup.
