Welcome to DS7730 - Fundamentals of Data Science

Instructor:	Dr. Thomas Wischgoll  thomas.wischgoll@wright.edu 303 Russ Engineering 937-775-5128
Office Hours:	Tue/Thur 10:30am - 12:30am (or by appointment)
Textbooks: (not required)	Data Science: Concepts and Practice Vijay Kotu & Bala Deshpande Morgan Kaufmann ISBN : 978-0128147610
Webpage:	http://avida.cs.wright.edu/courses/DS7730/
Lecture:	Tue 09:30 am - 10:30 am Thur 09:30 am - 10:30 am (Russ 339)
Exams:	Midterm: Tue, October 8th, 09:30 pm (in class or online) Final project: due Wed, December 11th, 11:59 pm
Grading Policy:	30% (assignments) + 30% (midterm) + 40% (final project)= 100%
	Each class is different. Therefore, no absolute grading scheme can be defined in advance. However, the following guarantees will always be made: 90% 80% 70% 60% 50% A B C D F

Course Goals/Objectives

By the end of this quarter, you will be able to:

• Develop algorithms or software using common Data Science environments, such as Python and RExtract and pre-process data from different sources, such as databases
• Apply basic AI techniques for data processing and analysis
• Handle large-scale data sets

• Introduction

Prerequisites

• Program requirements

Course Format

The course consists of two lectures a week. Attendance of the lectures is not strictly mandatory. However, you are responsible for all materials, announcements, assignments, etc. covered in either the lecture or assignments. If you miss a class, consult a classmate for any missed materials.

The purpose of the class is for everyone to understand the issues involved with data science. To this end, if you don't understand something during class, please ask. If you are confused, it is likely that a few of your classmates are as well. Also, listen to others' questions. Many times you'll think you understand a concept until you hear someone else's question about it. Dialogue is the best way to learn things, so don't be afraid to speak up. I will not specificly assign reading assignments. However, it is advisable to read along in the text book as we advance in the class.

There will be two assignments to be returned on the specified date, one in class midterm, and one final project. Students taking this course at the 600-level are required to incorporate lighting based on the Phong illumination model in the volume renderer for the final project. The grade will be determined as stated earlier.

Assignments

Three assignments will be given which are due on the following dates:

Assignment 1:	due: Fri, October 1st, 11:59 pm
Assignment 2:	due: Fri, November 5th, 11:59 pm

For implementing the assigments, you can request a your own computer or the resources provided by the university. You are welcome to exchange top-level ideas with your peers. However, it is not appropriate to exchange code or base your solution to a significant amount on code you found online. On the day the assignment is due, please turn in a screenshot of your software and your source code, including makefiles or project files, packed as a zip or tar file via Pilot. Your submission will not only be graded based on correctness but also its effectiveness in terms of visualizing the data.

Office Hours

Other Resources

The class web page is maintained at http://avida.cs.wright.edu/courses/DS7730/. It will keep information, assignments, announcements, etc. There is also a class mailing list. Make sure your email address is registered with the registration system. Please check the web page and read your email. I will try to make any announcements in both places as well as in class, but you don't want to miss anything.
 

Class Policy

Fine Print

Exams Exams will emphasize insight and problem solving ability rather than memorization. Exams will be closed notes, closed book, and no laptops or calculators.

Missed Exams Makeup exams will only be given for the gravest of reasons. If you must miss an exam due to extreme illness, etc., contact the instructor (email is fine) or leave a message with the Department of Computer Science and Engineering office (937-775-5131) before the exam. Be sure to leave both the reasons for missing the exam and how to reach you.

Add/drop Policy A copy of the add/drop policy is available at the main office or online.

Cheating Please do not. I am not obsessed with looking for cheating, but if I see something suspicious, I will refer it to the Office of Judicial Affairs. This is more work for me, and is embarrassing for everyone.  Again, please don't; this has been a problem in the past. If the rules are unclear or you are unsure of how they apply, ask the instructor beforehand. The acacemic integrity policiy as available online.

Feedback If you like, dislike, or don't understand something I'm doing with the course, please stop by my office hours, send me email, or paste together a note from newspaper clippings and drop it in my mailbox. I won't always change things, but I will always explain why I'm doing them the way I am.

Copyright All federal and state copyrights reserved for all original material presented in this course through any medium, including lecture or print. Individuals are prohibited from being paid for taking, selling, or otherwise transferring for value, personal class notes made during this course to any entity without the express written permission of the lecturer.