Lecture 1: Introduction to Data Science
Welcome to PSTAT5A!
Instructor
- Narjes Mathlouthi (
nmathlouthi@ucsb.edu)
Office Hours (Zoom):
- Thursdays 11 AM–12 PM
Teaching Assistants
Summer Lee (
sle@ucsb.edu)Mingzhu He (
mingzhuhe@ucsb.edu)
Course Resources
- Canvas: Grades & Announcements
- Gradescope: Quizzes & Labs
- Entry code:
WJ4XR7
- Entry code:
- Course Website: bit.ly/3Ga8CSK
- All lecture slides, labs, and code will be posted here
Course Resources
Labs: Interactive, hands‐on Python computing sessions hosted on JupyterHub.
Access the lab environment at: https://pstat5a.lsit.ucsb.edu
All relevant course material will be posted on the website. Quizzes are the only exception and will be administered via Gradescope. Please read the syllabus fully and carefully!
Communication & Email
- Priority: Bring non‐urgent questions to office hours or after lecture rather than emailing.
- Email Subject: Always include
[PSTAT 5A]to help us sort and reply efficiently.
- Response Time: Please allow 24–48 hours for replies; avoid sending emails over weekends.
What is Data Science?
- No single agreed-upon definition
- A cross-disciplinary field:
- Statistics: theory of modeling & randomness
- Computer Science: computation & data handling
- Statistics: theory of modeling & randomness
Why Theory Matters
- Data today is huge computation alone isn’t enough
- Theory guides how and why we apply tools
- Employers need analysts who understand and apply
Path Forward: Course Outline
- Descriptive Statistics: Summarize & visualize data
- Probability: Random variables & distributions
- Inferential Statistics: Confidence intervals & hypothesis tests
- Regression: Modeling relationships
- Data Collection: Sampling & study design
Why Should I Care?
- Data is everywhere, any field dealing with data needs these skills
- Companies seek insightful analysts, not just code runners
- This course equips you with both theory and practice
Let’s Get Started!
Ready to dive into Descriptive Statistics?