STA 235H - Introduction

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.title[
# STA 235H - Introduction
]
.subtitle[
## Fall 2023
]
.author[
### McCombs School of Business, UT Austin
]

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.center[
.big[
.box-5LA[Welcome to STA 235H 
Data Science for Business Applications]
]]
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.big[
Introductions
]
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# About the instruction team

.pull-left[
.box-2transl[Prof: Magdalena Bennett, Ph.D.]

- Assistant Professor in the Stats Group (IROM department)
  
  - Ph.D. in Economics of Education, Columbia University
  
  - Research: Causal Inference (+ ML) applied to social policies (e.g. education).

]
--
.pull-right[
.box-4transl[T.A.: Pedro Lima (Ph.D. student)]

.box-6transl[T.A.: Emma Costa (3rd-year Honors)]

.box-7transl[T.A.: Diego Robbins (3rd-year Honors)]
]

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.box-2LA.medium.sp-after-half[Introduce yourself!]

.box-5l.medium.sp-after-half[Interesting (or uninteresting) fact about yourself]

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# Interesting fact about me?

... I have a credit on IMDB, the movie database.

.center[
![:scale 70%](https://github.com/maibennett/sta235/raw/main/exampleSite/content/Classes/Week1/1_Intro/images/imdbv2.png)
]
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.box-2LA.medium.sp-after-half[Introduce yourself!]

.box-5l.medium.sp-after-half[Interesting (or uninteresting) fact about yourself]

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.big[
Let's review the syllabus
]
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.box-7Trans[Please, read the syllabus!]

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# About this course

- Objective:

"[G]ain the tools you need to tackle real-world problems from a quantitative perspective."

.box-5trans[You don't need to be a data scientist for this class to be useful!]

---
# How, when, and where?

- **.darkorange[In-person (Fall 2023)]:** 2 hrs/week in this classroom.

- **.darkorange[Drop-in Office Hours]**:

.pull-left[ 
.box-5[Prof. Bennett: 
Wed 4:30 - 5:30 PM 
Thu 4:00 - 5:30 PM]]
.pull-right[.box-5[T.A.s: 
Weekly + HW weeks (TBD)]
]
 
 
 - .small[Other times available upon request]
 
 
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# How, when, and where? (Cont.)

https://sta235.com

![:scale 75%](https://github.com/maibennett/sta235/raw/main/exampleSite/content/Classes/Week1/1_Intro/images/sta235_website.png)

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# Classroom Norms

.pull-left[
- Please, **.darkorange[be on time]**.

- **.darkorange[Participate and ask questions!]** (cold-calling can be used to loosen the atmosphere)

- **.darkorange[Bring your laptop]**: We will be doing in-class coding (let me know if you have any issues with this point).
]
.pull-right[
![](https://media.giphy.com/media/iB4PoTVka0Xnul7UaC/giphy.gif)
]

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# What will you need?

.pull-left-little_l[
- A **.darkorange[laptop]** to bring to class.

- **.darkorange[R & R Studio]**

- **.darkorange[Required Books]**:

- Angrist, J. & J. Pischke. (2015). "Mastering Metrics". Princeton University Press. (*Buy used or new*)
  
  - James, G et. al. (2021). "An Introduction to Statistical Learning with Applications in R". Springer. (*Available online*)
]
.pull-right-little_l[
![](https://github.com/maibennett/sta235/raw/main/exampleSite/content/Classes/Week1/1_Intro/images/mm.png)
]

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# How to succeed in this course?

- **.darkorange[Attend class]**.
  
- Slides are uploaded before class (*not self-contained*). Take notes but focus on **.darkorange[understanding]**.

- **.darkorange[Ask questions]** during class!

- Complete all **.darkorange[readings]** and **.darkorange[assignments]** by the assigned date.
  
- Get an **.darkorange[early]** start on assignments and **.darkorange[follow the submission guidelines]**.

---
# Assignments, Exams, and Grading

- **.darkorange[Just in Time Teaching (JITT) assignments (10%):]**

- Short online quizzes about readings and/or material.
  - Submit by 11:59 PM on Saturday (for Mon class) or Monday (for Wed class) before that week's class.
  - Graded for completion (new material) and correctness (for material already seen). You can re-take it as many times as you want!

- **.darkorange[6 homework assignments (35%):]**

- All in Canvas.
  - Assignments include both written questions and code (submit R script).
  - You can drop one assignment (only 5 will count -- can't drop the last HW).
  - You need to be responsible for your own work!
  
.box-4[Read submission guidelines]

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# Assignments, Exams, and Grading (Cont.)

- **.darkorange[Midterm (25%) and Final Exam (25%):]**

- In-class exams. 
  - Open book (offline resources), but no online resources.
  - Final exam is cumulative.
  
- **.darkorange[Attendance/Participation (5%):]**

- Attendance will be taken on 5-7 random classes. You can be absent in one of them without penalty.
  
  - If you miss more than one (1) of those classes, you can make up with participation (see Syllabus for details). 
  
---
# Assignments, Exams, and Grading (Cont.)

- Assignments and exams are **.darkorange[usually curved]**

- Final grade **.darkorange[will not be curved]**.

- Cutoffs for final letter grade:

|Grade|A|A-|B+|B|B-|C+|C|C-|D|F|
|-----|-|--|--|-|--|--|-|--|-|-|
|Cutoff|94%|90%|87%|84%|80%|77%|70%|65%|60%|<60%|

- Cutoff scores are strict (no rounding)

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# Communicating with the instruction team

**.darkorange[Email address: m.bennett@austin.utexas.edu]**

- Use the subject **.darkorange[STA 235H - Your subject]**.
- Email me directly for questions related to course administration.
- Usually respond in 1 business day.
- General questions should be posted on Canvas (Discussion Board)
- **.darkorange[Please, do not send messages through Canvas.]**

---
# Collaborations and Academic Integrity

- **.darkorange[You are encouraged to form study groups!]**

- Studying or discussing class material with others does **.darkorange[not]** mean you can copy other's work.
  
  - Students are responsible for their own work. All of it.
  
- **.darkorange[Do not share your files with other students]**

- If we find any evidence of copying or plagiarism, all students involved will be subject to disciplinary measures.
  
- **.darkorange[Remember to give credit where credit is due!]**

- Use citations and references when you use someone else's work.

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.big[
.darkorange[What questions do you have?]
]

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.big[
A brief motivation
]
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# What is Data Science?

![:scale 50%](https://media.giphy.com/media/A06UFEx8jxEwU/source.gif)

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# Data Science tasks

By **.darkorange[Hernán, Hsu, and Healy:]**
--
.box-2Trans[Description]

.box-4Trans[Prediction]

.box-6Trans[Causal Inference]

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# Data Science tasks

.box-2tL.medium.sp-after-half[Can we classify our customers into different segments?]
--
.box-4tL.medium.sp-after-half[What is the probability of a shopper coming back to our website?]
--
.box-6tL.medium.sp-after-half[What is the effect of increasing our advertising budget on our total revenue?]

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.big[
.darkorange[We'll review all of these in this class!]
]

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# After this course...

.box-3Trans[1) Bridge the gap between the "what" and the "how"]

.box-7Trans[2) Be critical consumers of "Data Science"]

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# Some notes before the break

- **.darkorange[Bootcamp]** session on Tuesday 22nd (MEZ 1.306): Focus on R.

- "Disability & Access (D&A) is seeking the assistance of students to serve as **.darkorange[volunteer notetakers]**."

- Volunteers will be eligible to receive volunteer hours in appreciation for their time. 
  - If you are a good notetaker and interested in helping other students, please contact me after class.