CSC 233 - Large Dataset

Class information:

Class hours: 12:30 PM – 1:45 PM (Tue/Thu) Location: ITC 223

Instructor: Prof. Benz Tran, Ph.D. Email: bq.tran@assumption.edu 

Office: 125 Founders Hall Phone: 508-797-7000, Ext.7501

Office hours: 11:30 AM–12:30 PM (Mon/Wed); 4:30 PM–5:30 PM (Mon/Tue/Wed/Thu)


Course description:

This course gives the student a detailed introductory experience in skills required for performing data analytic. These skills may include but are not limited to: data extraction and import; data tidying and transformation; data visualization for exploratory analysis; constructing statistical models from the data; assessing and improving the models; and communicating the results. The programming language using in this course is Python


Course Objectives:

Upon successful completion of this course, you will able to


Textbooks:


Course policies:

Attencdance: Students are expected to attend and actively participate in every class, and I will make every effort to ensure that class attendance is worthwhile. Please note that, based on the instructor’s experience, a lack of class attendance is strongly correlated with poor grades.

I will use  iClicker to record your attendance in every class. You will be able to mark yourself present using your laptop/computer or smartphone within the first 15 minutes of the class. If you forget to check-in while physically attending the class, please inform me verbally during the class; otherwise, I will rely on the  iClicker logs.

Missed exams due to unexcused absence will result in a score of zero. Makeup exams will be allowed only with the pre-approval of the instructor or for acceptable, documented reasons. Acceptable reasons for makeup exams include severe illness, family emergencies, or other unavoidable events, such as dangerous weather conditions and car accidents. The format of makeup exams may differ from the format of the original exam.


Late submission: Homework/assignments are due on the date indicated in the assignment, there will be zero tolerance policy for late assignments. Exceptions will be made only in extreme circumstances.


Academic honesty:  Academic dishonesty undermines the educational mission of the course and reflects disrespect to your classmates and to your instructor. Therefore, you are expected to practice the highest possible standards of academic integrity. The minimum penalty for cheating is a -50% score on the assignment. Additional, more severe penalties may be applied for repeated or egregious violations. This policy includes using unauthorized materials, information, notes, study aides or other devices in any academic exercises. Details on the University cheating policy can be found in the Section II in the "Academic Integerity Policy and Process'' at this URL. 

In the absence of instructions to the contrary, it is permissible to consult Internet resources to complete the homework assignments and projects in this class, provided that you give adequate citations of every resource you consult. However, it is not permissible to copy code or anything else directly from the web. Representing the work of others as your own is never permissible. When in doubt, ask the instructor first. 


Class technology:

Brightspace: 

Like many other courses at the AU, we are mainly using Brightspace as a gradebook, a place to find lecture slides, assignments, and all important announcements. All registered students have access to the online BrightSpace class site. BrightSpace can be accessed from the school portal. 
Please make sure to check frequently the Announcements on BrightSpace as well as the school email. 


iClicker: We rely on iClicker to record your attendance in class. You will be able to mark yourself present, using your laptop or smartphone, during the first 15 minutes of the class. In addition, I will take paper-based attendance on three unannounced days. If I find any discrepancy in your attendance record on those days (ie, you were marked present on iClicker but absent on paper, you will receive a 0 for your overall attendance score.


GradeScope: We use Gradescope as the tool to turn in assignment works. There will be an autograder implemented by the instructor to assess your solutions. In order to pass the testcase, your must submit required script files; your script must produce outputs that are identical to what the autograder wanted. 

You are not limited in submissions as long at you did it before the deadline. The last submission will be counted as your final answer. 


Evaluation

Grading scheme:


Grading scale:


Accommodation Policy

Assumption University is committed to ensure full participation of all students in theirs programs. If you have a documented disibility (or think you may have a disability), and, as a result, need a reasonable accommodation to participate in the classes, to complete course requirements, or even benefits from the university services, you are encouraged to contact to the Director of Student Accessibility Services, Julie LeBlanc at jm.leblanc@assumption.edu as soon as possible.


Academic Support Center (ASC)

The ASC provides one-one service to assist students at Assumption University with strategies for academic success. Students will have a choice of meeting a tutor in the ASC on-ground (2nd floor d’Alzon Library) or via Zoom.  

You can make appointments for tutoring at https://asctutoring.assumption.edu or stop by at the Academic Support Center, 2nd floor d’Alzon Library, or calling the ASC at 508-767-7071 during on-ground operational hours (Mon-Thurs 8:30 am-10:00 pm; Fri. 8:30 am-4:30 pm and Sun. 6:00 pm-10:00 pm). For assistance with general study skills or academic planning, please contact either Amy Hurley, Associate Director for Student Success ahurley@assumption.edu or  Allen Bruehl, ASC Director abruehl@assumption.edu


Important dates