This course is an introduction to Python programming, with focus on algorithms and also data science.
After this course, a student should be able to write computer programs in python, design algorithms for solving problems, and be able to work with existing libraries. Hopefully, after this course, a student is mature enough in programming to solve his/her problems using existng resources (online or books) and also be able to learn other languages more easily.

Teacher Assistants: Amirmahdi Zhalefar, Ahmad Movahedian-Darvishani, Ali Kianfar, Mohammad reza Eslami

Course syllabus Resources

This course is an introduction to Data Mining. Knowledge discovery process, including data preprocessing, pattern extraction and data mining techniques, pattern evaluation, and a brief introduction to visualization will be discussed in this course.
This course is project-based, and after this course, a student should be able to be a part of data analysis projects in general context.

Course syllabus Resources

This course is an introduction to data mining that focuses on matrix methods and features real-world applications ranging from classication and clustering to dimensionality reduction and data visualiation. Mathematical topics covered include: linear equations, regression, the singular value decomposition, and iterative algorithms. Students are expected to have taken a course in calculus and have exposure to numerical computing (e.g. Matlab, Python, R).

میانترم در روز بیست و پنجم بهمن برگزار میشود. زمان 14 الی 15:30.
توضیحات پروژه پایانی در گروه تلگرام قرار داده شده است. ددلاین 15 اسفندماه است.

Course syllabus Lecture slides

This course will provide a genral overview on skills such as doing scientific research, writing a scientific report, and presentation.

Course syllabus Resources