## 1 - 1

## Introduction to Python: I. Basic Programming

## Our introductory coding course in Python is a departure from tedious legacy programming exercises. In this course, students will develop a solid foundation in coding for Scientific Computing and AI applications through our modern, streamlined lectures to pursue higher-level applications in Data Science, Machine Learning, and Robotics.

## Total Lecture Time: 10 lectures

## Prerequisites: N/A

**Lecture 1**: Introduction to Computer Programming

**Lecture 2**: Python Numeric Variable Types

**Lecture 3**: Strings and Text Input/Output

**Lecture 4**: Lists

**Lecture 5**: Conditions and Loops

**Lecture 6**: Functions

**Lecture 7**: Tuples and Dictionaries

**Lecture 8**: Sets and Hashing

**Lecture 9**: Classes and Object-Oriented Programming I

**Lecture 10**: Classes and Object-Oriented Programming II

## 1 - 2

## Introduction to Python: II. Data Structures and Algorithms

## Students who have mastered basic programming skills will advance to study classic data structures and computer algorithms widely used in developing solutions in AI, Data Science, and Robotics.

## Total Lecture Time: 10 lectures

## Prerequisites: 1-1

**Lecture 1**: Basic Data Structure

**Lecture 2**: Debugging Skills

**Lecture 3**: Sorting Algorithms

**Lecture 4**: Queues and Breadth-First Search

**Lecture 5**: Stacks and Depth-First Search

**Lecture 6**: Priority Queues and A* Search

**Lecture 7**: Tree Structure

**Lecture 8**: File I/O

**Lecture 9**: Dynamic Programming I

**Lecture 10**: Dynamic Programming II

## 2 - 1

## Python Scientific Programming

## Students will learn how to perform complex data analysis and visualization, as well as basic regression and classification in linear/nonlinear problems.

## Total Lecture Time: 10 lectures

## Prerequisites: 1-1, 1-2

**Lecture 1**: Numpy

**Lecture 2**: Visualization

**Lecture 3**: Vectors and Matrices I

**Lecture 4**: Vectors and Matrices II

**Lecture 5**: Linear Regression I

**Lecture 6**: Linear Regression II

**Lecture 7**: Gradient Descent I

**Lecture 8**: Gradient Descent II

**Lecture 9**: Classification I

**Lecture 10**: Classification II