Introduction to Statistics

This is the course site for MATH1401 at University of North Georgia. This course combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.

The course is based on the UC Berkeley Foundations of Data Science course.

Courses

Each offering site includes links to assignments, slides, and readings.

• Spring 2021
• Fall 2021

Materials

All materials for the course, including the textbook and assignments, are available for free online under a Creative Commons license.

Textbook: Computational and Inferential Thinking: The Foundations of Data Science is a free online textbook that includes interactive Jupyter notebooks and public data sets for all examples. The textbook source is maintained as an open source project. The orginal textbook can be found here.

Assignments: All assignments from the Spring 2017 course offering are available as Jupyter notebooks. The notebooks assume a Python 3 installation with the standard modules from such as Numpy and Matplotlib, as well as the datascience and okpy modules.

Lecture Materials: All lecture material can be found on the course calendar.