Course Materials for the Fall 2013 Edition of Stat 157: Reproducible and Collaborative Data Science
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README.md

Statistics 157: Reproducible and Collaborative Data Science

This repository contains the course materials for the Fall 2013 Edition of Stat 157, a Seminar on Topics in Probability and Statistics.

TuTh 9:30-11AM 3 Evans Hall UC Berkeley, Fall 2013

Description

The course will cover philosophy, software tools, processes and best practices for reproducible computational research. The software tools will include git, IPython, SQL, LaTeX, laptop-based Virtualization, and Amazon Web Services. There will be a collaborative term project.

Instructors

Aaron Culich
Electrical Engineering and Computer Sciences
<aculich@eecs.berkeley.edu>
Office Hours: TBD

Philip Stark
Department of Statistics
<stark@stat.berkeley.edu>

Graduate Student Instructors

Kristina Kangas
Integrative Biology
<k_kangas@berkeley.edu>
Office Hours: By Appointment

Christopher Shaver
Computer Sciences
<shaver@eecs.berkeley.edu>

Class Logistics

Units/Credits: 3

Final Exam Date & Time: Tuesday, December 17, 2013 3-6PM

Format

This course will focus heavily on in-class participation in addition to assigned readings from scholarly journals, presentations from guest speakers, several "feedback" assignments, and weekly blog articles in addition to regular practice with the software tools listed in the description.

Your persistent cooperation in group work and contributions to the course will culminate into a collaborative term project. The format will be interactive and will involve your questions, opinions, and participation.

Topics Covered Will Include:

  • Reproducibility and Collaboration
  • Computational Architecture
  • Security and Data Integrity
  • Data structures and formats
  • Content v. Presentation; Web standards
  • Simulation
  • Numerics
  • Numerical linear algebra
  • Optimization
  • Data Visualization
  • Code efficiency

You'll find more details in the topic sketch.

Specific Objectives

This is not a traditional course, and you will not be able to cram and succeed in this course. The instructors and GSIs want you to succeed. Ask for help when you need it, and before you become desperate.

Grading

  • Lecture Attendance/Participation
  • Homework Assignments
  • Weekly Reflections
  • Collaborative Term Project

We will discuss each assignment in greater detail in lecture.

Additional Notes

Guest Speakers: Participation is mandatory. When a guest lecturer is scheduled to speak you cannot miss the class without contacting the instructor or GSI and scheduling a meeting to justify your absence.

Absences at other times: Communication is key. The success of the group depends on the cooperation of its members. You will be held responsible for communicating absences to your group members in addition to the instructor and the GSI.

Electronic Gadgets: Silenced or Off.