Credit Hours: 3 credits
Meetings: Mondays, 5:10pm-7:40pm
Prerequisites: None
Office: CSL 422A
Phone: 202-319-6277
Email: syn [at] cua [dot] edu
Office Hour: by appointment
This is an introduction course to provide foundations in the area of data science with discussion on methods, concepts and opportunities in Data Science. Students will learn foundational data analytics concepts and techniques through recent work and publications as well as projects. Topics of the course include types of datasets, data management and processing, methods for data analysis including machine learning and data mining, data visualization, and issues around data including ethics and policy. The course will combine theoretical foundations with implementation and evaluation of data analysis algorithms on real data.
This course is designed to:
Introduce the fundamental concepts and methods of data science,
Explore characteristics and types of data,
Introduce basic concepts and modeling skills in data mining and machine learning,
Explore tools for data analytics,
Introduce practical skills for data management, analytics, and communications for information professionals, and
Promote professional and ethical use of data.
Upon completion of the course, students will be able to:
Discuss the ecosystem of data science and big data, and identify opportunities in data science,
Understand and use terminology related to data science,
Understand the types of data and manage and process datasets accordingly,
Understand the core statistical and computational methods and algorithms to analyze datasets,
Design and conduct basic analysis of datasets,
Understand the basics of tools and software such as Hadoop and R to manage and process data,
Present and communicate outcomes of data analysis with skills such as visualization, and
Discuss the privacy, policy, and ethical issues related to data.
Required Materials:
Stuart, D. (2020). Practical data science for information professionals. Facet Publishing. (ISBN 9781783303465)
Recommended Materials:
Toomey, D. (2014). R for Data Science: Learn and explore the fundamentals of data science with R. Packt Publishing. (ISBN 9781784390860)
Additional readings are assigned as necessary for each week's lecture topic.
The following capabilities are required for course delivery:
This course communication will be mainly done through the Blackboard. Make sure you are familiar with Blackboard features.
For course preparation, you need to be able for find assigned reading articles from the university library catalog system. Make sure you know how to search and find articles and books from library system and databases.
For synchronous online class meetings, Zoom will be used.
The following technologies are taught as essential parts of this course:
The basics of statistical tools, e.g., R
The basics of visualization skills and tools, e.g., Tableau, etc.
Each assignment contributes to your final grade. All assignments are individual exercises (no group work). Follow the Class Policies or Instructions of each assignment for submissions. The submission dates are strictly applied. Detailed description of each assignment will be provided when the assignments are given.
Assignment 1: Identifying Data and Datasets (10%)
Assignment 2: Research essay (20%)
Assignment 3: Data visualization (20%)
The final project is an individual project. On the last class meeting, you will present the final project product in class. The presentation will provide an opportunity to introduce the product of project with your classmates. The presentation should be prepared with good materials (slides, and if necessary, handouts) for a better discussion in class.
The class participation includes class attendance, class discussion participation, and completion of class activities such as Lab exercises. Each week’s class participation adds up and as a total, it will contribute 20% of your final grade.
All assignments must be submitted by 11:59 pm on the day they are due, unless otherwise noted.
Late submissions: If the assignment is submitted late, your grade will be reduced by 10%. Each day it is late thereafter you will lose an additional 5% point (e.g., submitting one day late would reduce your grade by 15%). You need to make a prior arrangement with the instructor if any cause of delayed submission is expected with legitimate reasons. The last submissions may not be graded until the end of the term.
Makeup work: If a student has a legitimate reason, such as a medical or family emergency, the instructor may allow a student to do makeup work. The amount and nature of the work is up to the instructor's discretion. It will be graded at term's end. Documentation of the emergency (e.g. a doctor's letter) may be required.
This course requires three assignments, a final project, and class participation. Each of these contributes towards your final grade. The individual contributions are as below.
Assignment 1: 10%
Assignment 2: 20%
Assignment 3: 20%
Final Project: 30%
Class Participation: 20%
Final grades will be assigned as follows:
A: 94-100
A-: 90-93.99
B+: 86-89.99
B: 82-85.99
B-: 78-81.99
C: 70-77.99
F: Below 70
The University grading system is available at http://policies.cua.edu/academicgrad//gradesfull.cfm#iii for graduate students. Reports of grades in courses are available at the end of each term on http://cardinalstation.cua.edu.
All members of the Catholic University community have a shared responsibility to know and to abide by the University’s policies, especially relating to: Academic Integrity Accommodations for Students with Disabilities Attendance Conduct Final Exams Grades and appeals All of Catholic University’s policies are detailed at https://policies.catholic.edu/index.html. Please follow up with the instructor if you have any policy-related questions. Of particular note are the policies regarding Academic Integrity, Accommodations for Students with Disabilities, and Final Exams, which are described below.
Academic dishonesty at The Catholic University of America is not tolerated (https://policies.catholic.edu/students/academicundergrad/integrityfull.html and https://policies.catholic.edu/students/academicundergrad/integrityprocedures.html)
As such, academic integrity is not merely avoiding plagiarism or cheating, but it certainly includes those things. Academic integrity means, above all else, taking responsibility for your work, your ideas, and your effort, and giving credit to others for their work, ideas, and effort. If you submit work that is not your own – whether test answers, whole papers, or something in-between – that is considered to be academic dishonesty. University procedures related to academic dishonesty are conducted with respect and dignity, while also preserving accountability, and they presuppose that all participants will treat each other with respect and dignity.
The following sanctions are presented in the University procedures related to Student Academic Dishonesty:
“The presumed sanction for undergraduate students for academic dishonesty will be failure for the course. In the context of graduate studies, the expectations for academic honesty are greater, and therefore the presumed sanction for dishonesty is likely to be more severe, e.g., expulsion. ...In the more unusual case, mitigating circumstances may exist that would warrant a lesser sanction than the presumed sanction.”
At times, you may do group work for an in-class presentation or group project. For that specific assignment, you are allowed to share material, ideas and information; however, for any related work that is to be submitted on an individual basis, your submission is expected to be your own in its entirety. If there is no group work in the class you should not collaborate with classmates on work that is to be submitted for an individual grade.
For more information about what academic integrity means at CUA, including your responsibilities and rights, visit https://integrity.catholic.edu/index.html.
Any student who feels s/he may need a reasonable accommodation based on the impact of a disability should contact the Office of Disability Support Services (https://dss.catholic.edu/index.html) by email at CUA-DSS@cua.edu or call 202-319-5211 to make an appointment to discuss possible accommodations. DSS recommends that a student with a disability meet with DSS staff during the first week of every semester since accommodations are not retroactive. Please note that instructors will only provide those accommodations included in the DSS accommodation letter. DSS is located in PRYZ 127.
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Types of data
Data structure
Data description