- Instructor: Prof.
Sung-Hyuk Cha
- Email: scha@pace.edu
- Office: 15 Beekman st. rm 1119
-
Office Hours: 3:25pm~5:55pm on Tuesday and Thursday
- CRN: 22149
- Meeting:
- Meeting Times:R 06:10 - 09:00 PM,
Spring 2024
- Place:1PP W210
- Textbook:
Lecture notes will be distributed.
Suggested book: Algorithms for Data Science by
Brian Steele, John Chandler, and, Swarna Reddy,
Springer, 2017. ISBN: 978-3319457956
- Description:
This course focuses on the algorithms needed for data analytics,
and has a computational emphasis.
Students will develop proficiency in Python and R,
as they build algorithms and analyze data.
Topics include data reduction: data mapping, data dictionaries,
scalable algorithms, Hadoop, and MapReduce;
gaining information from data: data visualization,
regression modeling, and cluster analysis; and,
predictive analytics: k-nearest neighbors, naïve Bayes,
time series forecasting, and analyzing streaming data.
- Learning Outcomes: After taking this course students will:
- Develop an understanding of the key algorithms used in data science
- Develop proficiency in Python and R
- Learn the foundations of data reduction and scalability
- Develop an understanding of the methods used in data analysis
- Develop an understanding of forecasting and predictive modeling
- Prerequisites: CS 675 with a minimum grade of C, CS 660 with a minimum grade of C
- Lecture Notes: can be accessed using the http://classes.pace.edu
- Tentative Schedule:
| Week |
Topic |
| 1 (1/25) |
Ch 1 intro & Ch 2 Central tendancy
|
| 2 (2/1) |
Ch 3 More Descriptive Statistical Parameters |
| 3 (2/8) |
Ch 4 Correlation coefficient
|
| 4 (2/15) |
Ch 6 Proximity based classifier |
| 5 (2/22) |
Ch 7 Proximity based regressor |
| 6 (2/29) |
Ch 5 Proximity measures and Dynamic programming |
| 7 (3/7) |
Midterm |
| 8 (3/14) |
Ch 8 Proximity based clustering
|
| 9 (3/21) |
Ch 8 Clustering evaluation |
| 10 (3/28) |
Spring Break |
| 11 (4/4) |
Ch 9 Bayes |
| 12 (4/11) |
Ch 10 Fuzzy theory |
| 13 (4/18) |
Density based reasoning |
| 14 (4/25) |
Passover |
| 15 (5/2) |
Review |
| 16 (5/9) |
Final Exam |
- Evaluation:
- Attendance and Participation (10%):
- Homework Assignments (30%):
- Midterm Exam (30%):
- Final Exam (30%):
- Accommodations for Students with Disabilities
The University's commitment to equal educational opportunities for students with disabilities includes providing reasonable accommodations for the needs of students with disabilities. To request a reasonable accommodation for a qualified disability a student with a disability must self-identify and register with the Office of Disability Services for his or her campus. No one, including faculty, is authorized to evaluate the need for or grant a request for an accommodation except the Office of Disability Services. Moreover, no one, including faculty, is authorized to contact the Office of Disability Services on behalf of a student. For further information, please see Resources for Students with Disabilities at www.pace.edu/counseling/resources-and-support-services-for-students-with-disabilities.
- Academic Integrity:
(From the Student Handbook)
Students are required to be honest and ethical in satisfying their academic assignments and requirements.
Academic integrity requires that, except as may be authorized by the instructor, a student must demonstrate independent intellectual and academic achievements.
Therefore, when a student uses or relies upon an idea or material obtained from another source, proper credit or attribution must be given.
A failure to give credit or attribution to ideas or material obtained from an outside source is plagiarism. Plagiarism is strictly forbidden.
Every student is responsible for giving the proper credit or attribution for any quotation, idea, data, or other material obtained from another source that is presented (whether orally or in writing)
in the student's papers, reports, submissions, examinations, presentations and the like. Individual schools and programs may have adopted additional standards of academic integrity. Therefore, students are responsible for familiarizing themselves with the academic integrity policies of the University as well as of the individual schools and programs in which they are enrolled. A student who fails to comply with the standards of academic integrity is subject to disciplinary actions such as, but not limited to, a reduction in the grade for the assignment or the course, a failing grade in the assignment or the course, suspension and/or dismissal from the University.
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