lect01

Next Lecture

lect01, Tue 03/31

Introductions and Course Overview

Lecture 1

Starts about 10 min into the video

Course Info

• Instructors
  • Alex Franks (Prof. Franks)
  • Yekaterina Kharitonova (Prof. K)
• TAs:
  • Franky (Meng) - STAT
  • Brian (Lim) - CS
• Tutors:
  • Anya
  • Arthur
  • Natalie
  • Noemi
  • Sergio

Course websites

• Piazza for all course-related announcements and questions
• Course website: https://ucsb-ds.github.io/s20
• Jupyterhub (for labs and homework): ds100.lsit.ucsb.edu
• (+ Gradescope for submissions)

Textbook / references

• Reference text: Python Data Science Handbook
• Lecture notes

Grades

No exams.

• 50% - approximately 5 homeworks
• 25% - Labs on Wednesdays (not graded on attendance, submitted online, shorter than HW)
• 20% - Final project (due Wed, June 10)
  • like a “double homework”
  • more info later in the quarter
• 5% - Participation

Homework

• usually 1-2 weeks
• will be accepted up to 2 days late
  • less than 24 hours late: 80% credit
  • 24-48 hours late: 60% credit
  • after 48 hours: no credit (0%)
• contact the instructors in case of an emergency

Labs

• Labs are required; need to be turned in at the end of the lab as a record of your attendance.
• Section attendance: try to stay with the time you are assigned on GOLD.
• Submit it by Friday at 5pm

Participation

• Lecture attendance is strongly encouraged but not the only way to participate.
• join the lecture and ask questions (raise your hand on Zoom to be unmuted to ask your question)
• join the lab and ask questions
• participate on Piazza (asking/answering questions)
• office hours

In-class group work

Will be split into groups of about 3 to introduce yourself and discuss the question that was posed.

Timestamp in the video 32:36

Programming Languages for Data Science

• R (PSTAT 10), created specifically for statistical computing and graphics
• Python (CS 8), “general purpose” programming language + important packages

Who is this class for?

• advanced beginners
• not for those who took advanced stats or cs courses

Potential List of Topics

*

Taking computational / conceptional approach instead of the rigorous mathematical treatment

What is a data scientist?

T vs $\pi$-shaped

What is data science?

Domain expertise helps to know which tools to use and when.

Pokemon-card-style collection of data science explanation visualizations

Learning from Data

• The way to learn about the world is to take a claim and prove that it’s false.

• Induction. Can we generalize from specific instances?

• How is knowledge created from a sociological perspective?

  • science is a communal activity
  • e.g., threshold for p-values

Falsification

$H_0$ (null hypothesis): “All swans are white.”

vs.

$H_A$: “Not all swans are white.”

$H_0$ (null hypothesis): “The ivory-billed woodpecker is extinct.”

Can I be sure? Maybe I have been missing it.

Induction and Evidence

• data provides evidence for the truth of a conclusion
• argue about what is probable rather than what is impossible
• often about inferring general principles from specific observation
• Bayes’ Theorem for describing probabilities based on evidence

Inference: “Black swans are rare.” Fraction of black swans in the world?

The role of models

• Make assumption about how the data is generated.
• Models can still be used to develop statistical tests
• Can also be used to make predictions / forecasts and describe sources of variability
• Can (and should) be continuously refined

Statistical Inference

• probability (sampling distribution) ==> data ==> population

Given facts about the world, what might I see?

Learn the facts about the world

“Inverse probability”

DS 100 philosophy

4 steps:

1. Raw data is not information (pandas)
2. Information is not knowledge (EDA)
   • …
   • visualization (Altair)
3. Knowledge is not understanding (domain expertise)
4. Understanding is not wisdom (Ethical data science, consequences, data privacy)

Wisdom and Data Science

Questions to ask yourself

Mark Twain’s quote about statistics.

Discussion

UC Berkeley Gender Bias case (1973)

• graduate school admissions

What possible truths are consistent with this information? 1:10:11?

“Simpson’s paradox”