ECE 592602 Topics in Data Science 
This course will acquaint students with some core
basic topics in data science. Specific topics covered will include computational complexity,
basic data structures, scientific programming, optimization, wavelets, sparse signal processing,
dimensionality reduction, and principle components analysis.


• Prerequisite  

The main prerequisite is eagerness to learn about data science. True technical prerequisites are somewhat informal, and include comfort in math (especially linear algebra and probability) and comfort with computers (specifically, we will be using Matlab). 

• Course Objectives  
By the end of the semester, the student should be able to:
More detailed objectives that are relevant to specific chapters covered in the final exam will be posted on the course website prior to these tests. 

• Course Requirements  
Homework: Students will submit homework individually or in pairs. Assignments and the schedule for submitting them will be posted on the course web site. Projects: We expect 23 “homework style” projects during the semester, and one individual project. Matlab: The projects will involve Matlab programming. A free Matlab download is available on the EOS website: http://www.eos.ncsu.edu/software/downloads/ Tests: There will be a comprehensive final exam. The test will be openbook, opennotes. Computers are absolutely not allowed; calculators are allowed. Students who are unable to take the test at those times should inform the instructor at the beginning of the semester and an alternate arrangement may be formulated. Extra credit: Up to 2% of extra credit will be allowed. Extra credit will be allocated based on factors such as class participation, message board participation, and feedback about assignments. The bottom line is that you are encouraged to contribute to a pleasant and energetic atmosphere in class! Grading:


• Textbook  
N/A 

• Computer and Internet Requirements  
NCSU and Engineering Online have recommended minimum specifications for computers. For details, click here. 

• Instructor  
Dr. Dror Zeev Baron, Assistant Professor 
Phone: 9195137974 
