Getting Started

Class Introduction

1. Course Website (Lecture) – An overview of the course website, detailing how to navigate resources, access lectures, assignments, and important announcements.

2. Course Slack Channel (Lecture) – Explains the role of the Slack channel in course communication, including discussions, Q&A, and collaboration.

3. Data Science Experience (Lecture) – Discusses data science experience assignment.

4. Course Intro (Lecture) - Lecture 0: Hello World A general introduction to the course, its structure, expectations, and an overview of data science concepts.

   • Course Intro (Slides) - Lecture 0: Hello World (very beginning of ppt) – The slide deck introducing the course, providing key details about objectives, topics, and learning outcomes.

5. Course Overview (Lecture) - Lecture 0: Course Overview – A deeper dive into course logistics, including grading policies, assignments, projects, and participation.

   • Course Overview (Slides) - Lecture 0: Hello World (2nd half of ppt) – The second half of the introductory slides, covering more details on expectations, topics, and learning goals.

Data Science Introduction

1. Data Science Intro (Lecture) - Lecture 0: Hello World – Introduction to data science, its real-world applications, and fundamental concepts that will be explored in the course.

   • Data Science Intro (Slides) - Lecture 0: Hello World (1st half of ppt) – The first half of the slide deck focused on defining data science, its importance, and the scope of the course.

2. Data Science from Scratch - Chapter 1 (Reading) – A foundational reading covering the basics of data science, including key terms, methods, and an introduction to Python-based data analysis.

Toolkit Introduction

1. GitHub Classroom Homeworks & Codespaces (Lecture) – Walkthrough on how to use GitHub Classroom for assignments, along with an introduction to Codespaces for cloud-based coding.

2. GitHub Classroom Application Exercises (AEs) & Codespaces (Lecture) – A detailed explanation of application exercises (AEs) and how to complete them using GitHub and Codespaces.

3. GitHub Organization & Configuring Git (Lecture)Overview of Jupyter Notebooks in VS Code – Covers GitHub repository organization, setting up Git, and version control best practices.

4. Python Tutorial – Official Python documentation on using the Python command line, basic scripting, and execution.

5. Python & Juypter Notebooks (Lecture) - Lecture 1: Meet the Toolkit – Introduces Python and Jupyter Notebooks as essential tools for data science.

6. Python & Jupyter Notebooks (Slides) - Meet the Toolkit (1st half of deck) – Slides covering the basics of Python programming and Jupyter Notebooks for interactive computing.

7. Intro to GitHub, Jupyter, Python, and Codespaces (Lecture) - Lecture: ae-00-votes – A live demonstration of setting up and using GitHub, Jupyter, and Python in Codespaces. (No supporting slides.)

Objective: get familiar with the Python & Jupyter working environment in VS Code

6. Tour: Python & Jupyter (Lecture) - Application Exercise ae-01: Meet the Penguins – A hands-on demo of working with Python and Jupyter in VS Code. (No supporting slides.)

   • Objective: get familiar with the Python & Jupyter working environment in VS Code

Objective: Re-enforce learning objectives from Tour

7. Tour Recap: Python & Jupyter (Lecture) - Lecture 1: Meet the Toolkit – A follow-up lecture reinforcing key concepts from the initial Jupyter Notebook and Python tour.

8. Python & Jupyter Notebooks (Slides) - Meet the Toolkit (2nd half of deck) – The continuation of the slide deck, covering advanced usage and integrations for Jupyter Notebooks in data science.