Intro to Data Science

class: center, middle, inverse, title-slide

.title[
# Intro to Data Science
]
.subtitle[
## What <code>R</code> we doing?
]
.author[
### Prof. Weldzius
]
.institute[
### Villanova University
]

---

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# Why are you here?

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# Is this all just a fad?

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# So what IS data science?

- Split into two camps

1. .blue[Research] camp

- Focused on **answering a research question**
  
  - Follows the "scientific method"
  
  - Goal: contribute to knowledge
  
  - Domain: academia
  
--
  
2. .red[Prediction] camp

- Focused on **making a prediction**
  
  - Typically unconcerned with theory or *why* a model works
  
  - Goal: inform a decision / policy
  
  - Domain: private sector

---

# The Two Camps

---

# The Two Camps

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# .blue[Research] Camp

- The scientific method

1. .red[Observation] &rarr; .blue[Question]

2. .blue[Theory] &rarr; .blue[Hypothesis]

3. .red[Data Collection / Wrangling] &rarr; .red[Analysis]

4. .red[Results] &rarr; .blue[Conclusion]
  
--

---

# .blue[Research] Camp

- The scientific method

1. .red[Observation] &rarr; .blue[Question]

2. .blue[Theory] &rarr; .blue[Hypothesis]

3. .red[Data Collection / Wrangling] &rarr; .red[Analysis]

4. .red[Results] &rarr; .blue[Conclusion]
  
<center><img src="https://github.com/rweldzius/PSC4175/raw/main/static/images/camps5.png" width="60%"></center>

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# Course Objectives

- This course is the menu, not the food

- Look over many different fields, methods, and tools

- You pick those you like, and take more advanced classes to dig into them

- But we are very **hands on**

- You must download `R` and `RStudio` prior to next lecture (Problem Set 0)
  
--

- You must work through first HW using an `.Rmd` file
  
---

# Learning goals

1. Understand the uses of quantitative data for analyzing sophisticated research questions

2. Describe data used to investigate research questions, including univariate and multivariate visualizations and summary statistics.

3. Apply the appropriate methods to answer research questions and evaluate the hypotheses.

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# ChatGPT in the classroom

- Are we at the precipice of a new era in human-computer relations?

- ChatGPT can help you in achieving these learning goals
  
--

- But it needs to be used wisely...it is still a tool
  
--

- It can make coding (the hardest part of this class) easier

- But it can also prevent you from learning

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# AI in the labor market

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# AI in the labor market

- Will this be like automation and globalization for US manufacturing?
  
--

- What skills will be valuable in 5 years? 10 years?
  
--

- My answer: prepare you for both possibilities

- If AI is a "fad", make sure you can do this work unassisted
  
  - If AI is the new normal, make sure you can work with it productively
  
--

- The one thing you **shouldn't** do

- Take shortcuts / cheat
  
--

- You will still have an interview in which you are asked something like the following (actual interview question to a previous student):

- "In the next 30 minutes, recreate the following plot using R"

- **You** need to know this answer

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# Grades

- 14 homework assignments, 1-3 points each (35 points). 
  
--

- 5 problem sets (35 points) 
  
  - 1 extra credit point available each PSet
  
--

- Final project (20 points)

- Participation & Attendance (10 points)
  
--

- 100 points (+5-10 extra credit points)

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# Grades: Homework

- Think of these as your readings/practice for each class.

- One homework for every lecture

- Due by midnight on Tuesdays (no late homework accepted!)

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# Grades: PSets

- 5 in total (Pset 0 doesn't count)

- Posted to **course webpage** on Mondays at noon; due **Friday by midnight**

- Submit/Upload to **Blackboard**

- Each day late is -1 pt; after 3 days, scored zero
  
--

- Restrictions:

- Open book / open note / open Campuswire

- Can collaborate but submissions must be your own
  
--

- **Must submit a record of AI work with the problem set**

--
  
  - Print the output as PDF
  
---

# Honor Code

- Students are assumed to have read and agreed with the [Villanova University Academic Integrity policy](https://www.vanderbilt.edu/student_handbook/the-honor-system/)

- Violations of this policy may result in:

- An F for the semester (at minimum)
  
  - Suspension for a semester
  
  - Expulsion
  
--

- However, except where **explicitly noted**, this course is collaborative

- Open book, open note, open internet
  
  - Can rely on Campuswire for help
  
  - Can work together on problem sets (but must submit own work)

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# Resources

- Campuswire (place for **questions**)

- Post questions on the class feed
  
--

- Blackboard (place for **submissions**)

- Submit homeworks and problem sets
  
--

- Course Webpage (place for **materials**)

- Find all class materials
  
--

- Office hours (place for **hands-on help**)
  
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# Teaching Philosophy

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# Teaching Philosophy

- This course is **inherently** hard

- Learning `R` is challenging
  
--

- But the goal is to **encourage** you to pursue data science

- As such, the **nature** of the material is at odds with the **goal** of the class

- My solution: grade leniently

- i.e., lots of extra credit

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# Conclusion

- Let's have a great semester!

- Homework:

1. Review course webpage
  
  2. Create a CampusWire account (Link: https://campuswire.com/p/G9062A201. PIN: 7429)
  
  3. Create a Copilot account (https://copilot.microsoft.com/) or ChatGPT account
  
  4. Work through psc4175_hw_1.Rmd (due by Friday)
  
  5. Complete/submit problem set 0 (due by Friday)