Logistics

This page constiutes the official syllabus for this class.

Course Information

Introduction to Data Science
CMSC320 Spring 2020

• Lecture Meeting Times
  Monday and Wednesday, 5:00pm-6:15pm, IRB 0324

• Instructor:
  Héctor Corrada Bravo
  Center for Bioinformatics and Computational Biology
  Department of Computer Science
  hcorrada@umiacs.umd.edu
  Office: 3226 Iribe Center for Computer Science and Engineering Phone Number: 301-405-2481
  Office Hours: Friday 1:00pm-2:00pm and by appointment

• Communication:

  • For course related questions, use Piazza as indicated below.
    
  • For any other communication (e.g., absences accomodations etc.) send message through ELMS.

We will use the class Piazza site https://piazza.com/class/jrgmq242247377 for questions, dicussion and announcements. Assignments and grades for the class will be posted to the class ELMS site: https://umd.instructure.com/courses/1257097 . In case of an extended emergency closure, announcements on policy and procedures will be posted to Piazza.

TAs and Office Hours Schedule

TBD

Textbook and Resources:

There is no required textbook, the lecture notes will serve as the primary material. However, we will be drawing heavily from these sources:

• G. Grolemund and H. Wickham. R for Data Science.
  
• G. James, D. Witten, T. Hastie and R. Tibshirani. Introduction to Statistical Learning with Applications in R. Springer 2013.. A free PDF of this book can be downloaded at their site.
• P. Bruce and A. Bruce. Practical Statistics for Data Scientists
  
• N. Zumel and J. Mount. Practical Data Science with R. Manning Publications Co. 2014.
  

Additional readings will be posted in ELMS https://umd.instructure.com/courses/1257097 .

Additional class resources are listed here

Course Description, Goals and Expectations

Data science encapsulates the interdisciplinary activities required to create data-centric products and applications that address specific scientific, socio-political or business questions. It has drawn tremendous attention from both academia and industry and is making deep inroads in industry, government, health and journalism.

This course focuses on (i) data management systems, (i) exploratory and statistical data analysis, (ii) data and information visualization, and (iv) the presentation and communication of analysis results. It will be centered around case studies and projects drawing extensively from applications.

Consult the class home page for an up-to-date course topic schedule.

Expected outcomes

1) Students will be able to create specific requirements for a data-centric application used to address a specific problem or question
2) Students will be able to identify and select appropriate tools: language, libraries and data resources, to meet specific requirements for a data-centric application
3) Students will be able to build and disseminate a data-centric application from a set of specific requirements using existing tools, libraries, data resources and publishing mechanisms.

Expectations for Students

• There will be reading assignments. Students are expected to have read the material before class.
  
• Students are expected to attend lectures. Active participation is expected. There will be graded work done in class.
  
• Assignments are to be handed-in electronically or in class as instructed on their due date. Late assignments will not be accepted.
  
• There will be graded work to be done in class. Students not in class that day, except for an excused absence, will not be able to complete that work outside class.
  
• Students may discuss homeworks and projects in groups. However, each student must write and/or program solutions independently.
  
• Posting project solutions in a public online location without express consent and permission from the instructor is a violation of academic integrity policy.
  
• Cell phone usage is prohibited during lecture, laptop use will be allowed to the extent that students demonstrably use it to follow along an in-class analysis or demonstration.
  
• You can earn full credit for class participation in three ways:
  (1) lecture participation, asking questions and answering your peers questions, (this will be impossible to keep track of, so please the two below :-) )
  (2) piazza participation, asking and answering questions on piazza,
  (3) regular attendance to office hours (there will be sign-in sheets during office hours).
  To earn full credit you should aim to ask or answer a question at least once every two weeks on lecture or on piazza; or attend office hours at least once a month (this can include just going to my office hours to chat about computer science, data, science, software engineering, etc.).

Grading Procedures

• Projects (40%)
• Written homework (25%)
• Midterm exams (20%)
• Final Project (10%)
• Class participation (5%)

University Policies and Resources

Policies relevant to Undergraduate Courses are found here: http://ugst.umd.edu/courserelatedpolicies.html. Topics that are addressed in these various policies include academic integrity, student and instructor conduct, accessibility and accommodations, attendance and excused absences, grades and appeals, copyright and intellectual property.

Academic Integrity

Academic integrity is an essential part of your educational program. Please find more information about academic integrity policies in the Computer Science Department here: http://www.cs.umd.edu/class/resources/academicIntegrity.html

Course evaluations

Course evaluations are important and the department and faculty take student feedback seriously. Students can go to http://www.courseevalum.umd.edu to complete their evaluations.