CIS 3715 Data Science - Spring 2018

• Announcements
• Course Info
• Lab Assignments
• Projects
• Resources

Lectures

Tuesday & Thursday 09:30 AM - 10:50 AM in BEURY 00162
Date Range: Jan 16, 2018 - May 9, 2018
Instructor: Dr. Slobodan Vucetic
Contact: vucetic@temple.edu
Office Hours: TBD or by appointment
Office: SERC 304
Phone: 215-204-5535

Labs:

Wednesday 3:00 PM - 4:50 PM in SERC 204 (Section 001) SERC 206 (Section 002)
TA: Tian Bai (Section 001) || Shanshan Zhang (Section 002)
Contact: Tian Bai: tue98264@temple.edu || Shanshan Zhang: zhang.shanshan@temple.edu
Office Hours: Tuesday 2:00pm-3:00pm (Tian Bai) || 3:00pm-4:00pm (Shanshan Zhang) or by appointment
Office: SERC 303

Course Description

Internet and advances in technology allow us to collect massive amounts of diverse types of data. There is an increasing recognition that data could be translated into valuable insights. A data scientist is a person who has the skills, knowledge, and ability to extract actionable knowledge from the data -- either for the good of society, advancement of science, or profit in business. This class is an introduction to the practice of data science. The student will leave the class with a broad set of practical skills for doing data science including collecting, cleaning, and preprocessing data, exploring the data, applying methods from machine learning to analyze the data, and communicating the results. The students will gain experience in dealing with “big data,” which are too big to fit in the computer’s memory.

Course Website

https://cis3715-temple-2018.github.io/

Textbook

This course does not have a designated textbook. The readings will be assigned from free web sources as needed. The following two books are highly recommended:

• Wes McKinney. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O'Reilly Media, 2012.
• Foster Provost, Tom Fawcett. Data Science for Business: What You Need to Know about Data Mining and Data-analytic Thinking. ISBN 1449361323.

Syllabus

• Using Python for data science
• Data preprocessing
• Exploratory data analysis
• Statistical analysis of data
• Dealing with big data
• Machine learning
• Unsupervised learning
• Supervised learning
• Evaluation
• Data visualization and communicating results

Grading

TBD

Regulations

• Class and lab attendance is mandatory and will be recorded. Absences for legitimate professional activities and illnesses are acceptable only if prior notice is given to the instructor by e-mail or phone. Scheduling conflicts with your work, extra-curricular activities, or any other such activities is not a valid excuse.
• Class participation means that you attend class regularly and have completed your assigned readings. It means that you ask relevant questions and make informed comments in class.
• We will have weekly quizzes that test your progress.
• Late assignments are not accepted.
• All work submitted for credit must be your own. Feel free to use resources from the web, but make sure to acknowledge the sources. You may discuss the problems with your classmates, but you should acknowledge their help too. Do not copy their solutions. Please ask if you have any questions about the Honor Code. Violations of the honor code will be treated seriously. Please check the Temple University policy on Plagiarism and Academic Cheating.

Disability Disclosure

Students with disabilities, including "invisible" disabilities such as chronic diseases and learning disabilities, are encouraged to discuss with us any appropriate accommodations that we might make on their behalf. Student must provide me with a note from the office of Disability Resources and Services at in 100 Ritter Annex, 215-204-1280, regarding their disability.