Fall 2016

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CS583: Introduction to Data Mining


Course Information

DS501: Introduction to Data Science
Time: TBD
Location: TBD

Instructor Information

Instructor: Jiawei Zhang
Email: jwzhanggy [AT] gmail [dot] com
Office: TBD
Office Hours: TBD

Teaching Assistant: TBD
Email: TBD
Office: TBD
Office Hours: TBD

Course Description

The course on Introduction to Data Science provides an overview of Data Science, covering a broad selection of key challenges in and methodologies for working with big data. Topics to be covered include data collection, integration, management, modeling, analysis, visualization, prediction and informed decision making, as well as data security and data privacy. This introductory course is integrative across the core disciplines of Data Science, including databases, data warehousing, statistics, data mining, data visualization, high performance computing, cloud computing, and business intelligence. Professional skills, such as communication, presentation, and storytelling with data, will be fostered. Students will acquire a working knowledge of data science through hands-on projects and case studies in a variety of business, engineering, social sciences, or life sciences domains. Issues of ethics, leadership, and teamwork are highlighted.

(Prerequisite: a basic background in python programming and statistics, either at the undergraduate or graduate level. )

Other Issues

Student Disabilities: If you need course adaptations or accommodations because of a disability, or if you have medical information to share with me, please make an appointment with me as soon as possible. If you have not already done so, students with disabilities who believe that they may need accommodations in this class are encouraged to contact the Office of Disability Services (ODS) as soon as possible to ensure that such accommodations are implemented in a timely fashion. This office is located in the West St. House (157 West St) and their phone number is 508.831.4908.

Course Progress

Time

1/3

Exam

35%

Homework

80%

Projects

60%

Grading Policy

Case Studies and Presentation (72% = 6 X 12%): We will have six case studies (i.e., team projects) in the course. Students are required to work in small teams on each case study. Case studies will involve additional case-specific background reading. A report for each case study should be submitted. Powerpoint presentation needs to be submitted on the day of the presentation.
Peer Review (12% = 12 X 1%)
Exams (16% = 4 X 4%): 4 Quizzes. For the dates of the quizzes, please check the course calendar in Canvas system.

Late Homework Submission: Late assignments will not be graded. If an emergency arises or you know in advance about a conflict, please use your budget of the 2 uncounted homeworks.
Makeup Quizzes: No makeup quiz will be available. If an emergency arises or you know in advance about a conflict, please use your budget of the 2 uncounted quizzes.
Collaboration and Academic Honesty Policy: Collaboration is prohibited on the quizzes and homeworks. All violations of the collaboration policy will be handled in accordance with the Academic Honesty Policy.

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