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Lecture 1

Introduction

DATA 8

Spring 2017

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Welcome to Data 8

  • John DeNero — 781 Soda Hall
  • Office hours: 9am to 11am Thursdays (starting next week)
  • Appointments: Email denero@berkeley.edu
  • A course developed collaboratively by faculty and students across campus, starting in Spring 2015

http://data8.org/sp17/staff.html

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Announcements

http://data8.org/sp17

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Data Science

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What is Data Science?

Drawing useful conclusions from data using computation

  • Exploration
    • Identifying patterns in information
    • Uses visualizations
  • Inference
    • Quantifying whether those patterns are reliable
    • Uses randomization
  • Prediction
    • Making informed guesses
    • Uses machine learning

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Connector Courses

  • Data science is driven by applications
  • Every data-driven subject brings new challenges
  • Connectors are small, independent courses taught by Berkeley faculty who are excited to share their expertise

http://data8.org/connector

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Course Structure

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Parts of Data 8

  • Lecture has a screencast, but it's better to attend
  • Weekly lab is required and part of your final grade
  • Weekly homework assignments & a few projects
  • (U)GSI office hours Monday-Thursday 1-6 (next week)
  • Midterm exam in class (Fri 3/10) & final exam (Tu 5/9)

Participation points can be earned in two ways:

  • Attend lecture in weeks 3 through 14 (weeks 1 & 2 optional)
  • Or complete a final independent project with a partner
  • Students with prior coursework in CS & Stats who are taking Data 8 for a letter grade must choose the project

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Course Policies

Learning

Community

Course Staff

http://data8.org/sp17/policies.html

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Collaboration

Asking questions is highly encouraged

  • Discuss all questions with each other (except exams)
  • Submit lab assignments individually (graded on completeness)
  • Submit homework individually, but feel free to discuss
  • Submit projects in pairs; find a partner in your lab

The Limits of collaboration

  • Don't share solutions with each other (except project partners)
  • Copying solutions will result in failing the course

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Why Data Science?