This page and the Course Calendar constitute the official syllabus for this class.

Course Information

Algorithms for Data Science DATA 606 Spring 2020

Office Hours: before and after class or by appointment

Assignments and grades for the class will be posted to the class ELMS site: https://umd.instructure.com/courses/1281291 . In case of an extended emergency closure, announcements on policy and procedures will be announced through ELMS.

Textbook and Resources:

We will refer to the following textbooks

  • Leskovec, et al. (2016). Mining of Massive Datasets. Book Site
  • Boyd and Vandenberghe (2004). Convex Optimization. Book Site

Other readings will be posted in the Course Calendar.

Expected outcomes and topics covered

As large amounts of data are being created it is important to understand how to analyse the data to extract interesting trends and patterns. Since the volume of data is large, it may not be feasible to make more than a single pass over the data. Stream processing methods provide effective ways to extract useful information from large data sets by making very few passes on the data. Surprisingly, a lot of information can be gleaned by making a single pass over the data, or a small number of passes over the data. The first part of the course will cover random sampling and stream processing methods. We will also cover modern optimization methods for large datasets as used in Machine Learning.

We expect familiarity with basic algorithms, there will be programming assignments in Python.

Expectations for Students

Class prep

The Course Calendar will list readings (uploaded to ELMS). You are required to read this material before lecture

Announcements and discussion

We will use ELMS https://umd.instructure.com/courses/1281291 for course discussion.

Other policies

  • 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.
  • Students will be expected to present project progress reports and papers during class.
  • Assignments are to be handed-in electronically or in class as instructed on their due date. Late assignments will not be accepted.
    Students may discuss homeworks and projects in groups. However, each student must write and/or program solutions independently.
  • 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.

Grading Procedures

  • Homeworks and written exercises (70%)
  • Final take home exam (30%)

University Policies and Resources

Policies relevant to 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.

Course evaluations

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