What this course is about?

The course will introduce students to the most import techniques and methods used by data scientists. The course will closely flow selected chapters from following books:

·         An Introduction to Statistical Learning: with Applications in R (available online)

·         Mining of Massive Datasets (available online)

         In addition, some introductory material will be presented using:

·         The Art of R Programming: A Tour of Statistical Software Design (Amazon)

·         The Grammar of Graphics (Amazon)

What are the course’s prerequisites?

The course is intended to undergraduate students at advanced stages of their studies and to graduate students that are interested in the field of data analysis. Students are assumed to know basic probability theory and to have some algorithmic and programming background.

What about homework assignments?

                There are no formal requirements for submitting any homework. However, for successfully passing the course, the student are expected to spent few hours per week for reviewing class material and solving theoretical and practical after-the-chapter problems in the textbook. The final exam’s questions will be very similar to after-the-chapter problems.

How the course grade is determined?

                The course grade is the grade of the final exam. Beware; there is a catch (see above)

When and where?

                Wed 8:00-11:00, @Dan-David 110.

Contact the lecturer

                apartsin@gmail.com.

Please join the course group @ http://groups.google.com/d/forum/tau-data-science-course-2015s