CS 194-16 Introduction to Data Science Fall 2015

Organizations use their data for decision support and to build data-intensive products and services. The collection of skills required by organizations to support these functions has been grouped under the term Data Science. This course will attempt to articulate the expected output of Data Scientists and then equip the students with the ability to deliver against these expectations. The assignments will involve programming, statistics, and the ability to manipulate data sets with code.

Notes on CS294-16: The graduate version of the class CS294-16 is a "mezannine" class which is part of the Master of Engineering curriculum. Its not normally available to other graduate students except under special circumstances.

New: Final Projects

Logistics

• Course Number: CS 194-16, CS 294-16 Fall 2015, UC Berkeley
• Instructor: John Canny
• Time: MW 5pm - 6:30pm
• Location: 155 Donner Lab through 9/15/2015, then 310 Jacobs Hall
• Teaching Assistants: Haoyu Chen
• Discussion: Join Piazza Links to an external site. for announcements and to ask questions about the course
• Office hours:
  • John Canny - M 3-4, W 2-3 at 637 Soda
  • GSI - Tue 3-4, Fri 3-4 at 283H Soda

Pre-requisites

Pre-requisites for this course include 61A and 61B and basic programming skills. Knowledge of Python will be useful for the assignments, and several will also use the Scala Links to an external site. language. Students will also be expected to run VirtualBox Links to an external site. on their laptops for the assignments.

Please take the class survey here.

Please set up your machine according to these instructions. There are many issues with Windows 10 at this time. Dont upgrade if you can avoid it.

Make sure you set up and test your VM before the first lab on 9/2.

Texts

There is no single textbook, and readings will be posted lecture by lecture. However, there are a couple of books that are particularly useful and we will reference them repeatedly:

• Data Science from Scratch Links to an external site. by Joel Grus
• Python for Data Analysis Links to an external site. by Wes McKinney

Grading

• Class Participation and in-class labs: 20%
• Midterm: 20%  (statistics of midterm score)  midterm solutions
• Final Project (in groups): 30%
• Homeworks : 25%
• “Bunnies” : 5%

Midterm Info

The midterm is next Monday 11/23 from 5-6:30pm. Its closed-book but you can bring a single 8.5x11 sheet with notes on both sides.

Solutions

Homework Solutions: Homework Solutions

Lab Solutions: Lab Solutions

Midterm Fall 2014 and Solutions

Nano-Quizzes for each lecture

Schedule