This page is subject to change before the start of the course.
This course focuses on both concepts and practice. We will introduce (a) the core data mining concepts and (b) practical skills for applying data mining techniques to solve real-world problems.
Topics to be covered:
See the course schedule for weekly topics.
This course will use R for computing. R is freely available online. We will be using R Studio as our default IDE, which can be downloaded for free. We will use R Markdown for creating reproducible data science documents.
Students are expected to be familiar with the basics of Linear Algebra, Probability and Statistics, and should be comfortable with programming. We will use R for computing, and hence familiarity of R is preferred. If you have never programmed before, get started by checking a list of learning resources on the course website here.
Grades are based on three major activities listed below. Assignments are due as scheduled, and grades on late work will be decreased by 10% per day late. See the assignment page for more details.
Class participation will be assessed through online quizzes assigned each week.
This course will use materials from several recommended books listed below. These books are available online (some are available online over Pitt network). There will be reading assignments over the course of the semester. Links to the electronic copies of these readings will be provided. There are also other recommended books for further reading and for learning R.
Readings will be assigned throughout the semester – roughly one reading assignment per week. Each reading assignment is relevant to the weekly topic, and is chosen to help you connect the technical tools to more practical and creative use in real world. The reading assignment is to enrich your data science problem-solving skills and help you develop project ideas. The tentative list of readings to be assigned is available here.
The reading assignment will be evaluated via post-class quizzes.
See the university policies page.