QTM 151 - Introduction to Statistical Computing II

Welcome to QTM 151 - Introduction to Statistical Computing II! This repository contains all the materials for the summer course, including lectures, assignments, and tutorials.

Course Overview

This course is designed to introduce students to statistical computing techniques using Python and SQL. It builds upon the foundational knowledge from QTM150 and focuses on practical applications of data analysis, reproducible research, and database management.

Repository Structure

This repository is organised as follows:

• assignments/: Contains all course assignments
• lectures/: Includes lecture materials and code
• tutorials/: Step-by-step guides for the tools used in the course
• README.md: This file, providing an overview of the course and repository
• syllabus.pdf: Course syllabus in PDF format

Each lecture folder contains an HTML file and a Jupyter notebook (.ipynb) with code examples and explanations, along with any additional resources or datasets used in the lecture.

Assignments and Quizzes

Throughout the course, students will complete some assignments to reinforce their learning. These will be posted in the assignments/ folder as the course progresses. We will also announce these in class and on Canvas. Please refer to the syllabus for due dates and submission guidelines.

Tutorials

The tutorials/ folder contains step-by-step guides for various tools and techniques used in the course. These include:

• VSCode and Anaconda Tutorial
• Jupyter Notebook and Markdown Tutorial
• GitHub Tutorial
• PostgreSQL Tutorial

Course Requirements

• Prerequisites: None, only willingness to learn and explore new tools :smiley:
• Software: Anaconda distribution of Python 3.x and VS Code
• We will use SQLite for database management, which is already included in the Anaconda distribution. The PostgreSQL tutorial is here as a reference for those interested in learning about more advanced database management systems, but we will not cover it in class.

Grading

• Assignments (5x): 50%
• Class Quizzes (3x): 50%

Course Policies and Expectations

For detailed information on course policies, grading criteria, attendance requirements, and academic integrity guidelines, please refer to the syllabus.pdf file in the repository root.

Suggested Resources

To supplement your learning, you may find the following resources helpful:

Books

• Python for Data Analysis by Wes McKinney
• Python Data Science Handbook by Jake VanderPlas
• Elements of Data Science by Allen Downey
• Automate the Boring Stuff with Python by Al Sweigart
• Python for Everybody by Charles Severance
• SQL for Data Scientists by Renee M. P. Teate

Online Courses

• Coursera: Python for Everybody Specialisation
• edX: Python Basics for Data Science
• Codecademy: Learn Python
• DataCamp: Introduction to SQL
• Coursera: SQL for Data Science

Documentation

• Official Python Documentation
• NumPy Documentation
• Pandas Documentation
• Matplotlib Documentation
• SQLite Documentation

The syllabus also includes a list of additional readings and resources for each week.

Contact Information

• Instructor: Danilo Freire
• Email: danilo.freire@emory.edu
• Office Hours: At your convenience, please schedule an appointment via email.

Academic Integrity

Students are expected to adhere to the Emory University Honour Code. Any suspected violations will be reported to the Honour Council.

Accessibility

If you require any accommodations for this course, please contact the Department of Accessibility Services and the instructor as soon as possible.

Getting Help

If you encounter any issues with the course materials or have questions about the content, please:

1. Check the course syllabus and this README for relevant information
2. Review the lecture materials and tutorials in the repository
3. Consult with your classmates or post in the course discussion forum
4. Attend office hours or schedule an appointment with the instructor

Contributing to the Repository

While this repository is primarily maintained by the course instructor, everyone is welcome to contribute. Please feel free to suggest improvements or report issues by opening a GitHub issue, submitting a pull request, creating a discussion post, or contacting the instructor directly.

Acknowledgements

This course and its materials have been developed with inspiration from previous version of this course, as well as various open-source communities and educational resources. I am particularly grateful to Alejandro Snchez Becerra for his teaching materials and guidance. I am also thankful for the contributions of the Python, SQL, and data science communities that make courses like this possible.

License

This repository is licensed under the MIT License. You are free to use, modify, and distribute the materials as needed, with appropriate attribution to the original source.

We look forward to an engaging and productive semester! Good luck, and happy coding! :smiley: