Welcome to STA 199

Lecture 1

Dr. Elijah Meyer

Duke University
STA 199 - Spring 2023

January 13, 2023

Welcome

Goals for Day 1

Get organized

  • Get to know the professor
  • Get to know each other
  • Course overview
  • Register with GitHub & Slack

Who Am I?

 

Who Are You?

Please share with your neighbors:

  • Major
  • Year
  • Why you are taking this course
  • Anything else

What is data science?

What is data science?

“Data science is a concept to unify statistics, data analysis, machine learning and their related methods in order to understand and analyze actual phenomena with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, information science, and computer science.”

-Donoho, 2017

Examples of data science

  • Identification and prediction of disease
  • Targeted advertising
  • Supply chain optimization
  • Sports recruitment + strategist
  • The list goes on and on…..

Jobs

Jobs

Jobs

Data literacy

Our Course

Our Classroom

  • Community
  • Communication
  • Respect

Course objectives

  • Learn to explore, visualize, and analyze data in a reproducible and shareable manner

  • Gain experience in data wrangling, exploratory data analysis, predictive modeling, and data visualization

  • Work on problems and case studies inspired by and based on real-world questions and data

  • Learn to effectively communicate results through written assignments and final project presentation

Some of what you will learn

– Fundamentals of R

– Data visualization

– Web scraping

– Version control with GitHub

– Reproducible reports with Quarto

– Regression

– Statistical inference

R - figures

Figure 1: Example R Figures

{fig.align = “center”}

R

Note

This is a new language

Workflow & Website

sta199-s23-2.github.io/

Before Class

  • Watch lecture content videos

During Class

  • Warm up question

  • Mix of lecture and interaction

Please bring your laptops if able

Activities and assessments

  • Homework: Individual assignments combining conceptual and computational skills.

  • Labs: Individual or team assignments focusing on computational skills.

  • Exams: Two take-home exams.

  • Final Project: Team project presented during the final exam period.

  • Application Exercises: Exercises worked on during the live lecture session.

  • Statistics Experiences: Engage with statistics outside of the classroom and reflect on your experience.

Lab

  • Focus on computing using R tidyverse syntax

  • Apply concepts from lecture to case study scenarios

  • Work on labs individually or in teams of 3 - 4

Textbooks and readings

  • R for Data Science by Grolemund & Wickham (2nd ed. O’Reilly)

  • Introduction to Modern Statistics by Cetinkaya-Rundel & Hardin (1st ed. OpenIntro)

Create a GitHub account (Why?)

GitHub, Inc., is an Internet hosting service for software development and version control.

Create a GitHub account

Please do this before the Getting to know you survey

Go to https://github.com/, and create an account (unless you already have one).

Some tips from Happy Git with R.

– Incorporate your actual name!

– Reuse your username from other contexts if you can, e. g., Twitter or Slack.

– Pick a username you will be comfortable revealing to your future boss.

– Be as unique as possible in as few characters as possible. Shorter is better than longer.

– Avoid words with special meaning in programming (e.g. NA).

GitHub account

Figure 2: Invite Example

Slack

https://slack.com/get-started#/createnew

R-Studio

– Reserve a STA198-1991 RStudio container

– Go to https://cmgr.oit.duke.edu/containers

– Click Reserve Container for the STA198-199 container

Getting to know you survey

For Wednesday

– We’ll start talking about the computing toolkit

– Watch videos for Wednesday

– Complete Getting to Know You Survey (by Monday)

Please bring laptop to class if able for next time!

🔗 sta199-s23-2.github.io