Teaching Team

Instructor, Bora Jin

• Office hours
  • Monday, Wednesday, Thursday at 11am - 12:15pm
  • Old chem 003

Teaching Team

TA, Camilla Yu

What is Data Science?

• Data science is using data to understand the world.

• We're going to learn to do this in a principled and tidy way -- more on that later!

• This is a course on introduction to data science, with an emphasis on statistical thinking.

Course FAQ

Q - What data science background does this course assume? A - None.

Q - Is this an intro stat course? A - Not a traditional one. While statistics $\ne$ data science, they are very closely related and have tremendous of overlap. Hence, this course is a great way to get started with statistics.

Q - Will we be doing computing? A - Yes. We will use the computing language R.

Course Learning 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.

• Develop your own question(s) about data and use statistical techniques to answer the question(s).

• Practice effective oral and written communication of results.

Some of What You Will Learn

Examples of Data Science in Practice

• Russian Invasion of Ukraine

• What Redisctricting Looks Like in Every State

• Traking Your Health via Fitbit

• Infectious Disease Tracker

Time for an Analysis!

• Let's look at the UN vote analysis.

Class Meetings

Summer courses require a high-level of commitment: "a minimum of 25 hours per week" based on Credit Hour Policy

Lecture

• Monday to Friday at 9:30am - 10:45am, Old Chem 003
• Focus on concepts behind data analysis
• Important: Videos & readings before lecture (Prepare column on course schedule)
• Interactive lecture including examples and hands-on exercises
• Bring fully-charged laptop to every lecture -- Please let me know as soon as possible if you do not have access to a laptop.

Class Meetings

Summer courses require a high-level of commitment: "a minimum of 25 hours per week" based on Credit Hour Policy

Lab

• Tuesday & Friday at 11am - 12:15pm, Old Chem 003
• Focus on computing using R tidyverse syntax.
• Apply concepts from lecture to case study scenarios.
• Bring fully-charged laptop to every lab.

Course Toolkit

Course Website: sta199-summer22.netlify.app

• Central hub for the course
• Check office hours!
  • Full office hours start next week.

GitHub: github.com/sta199-summer22

• Distribute & work on assignments -- more on this later!

Sakai: sakai.duke.edu

• Announcement, Gradebook

Course Toolkit

Gradescope: gradescope.com

• Submit assignments

Slack: sta199-summer22.slack.com

• Questions and general discussion

Me: bora.jin@duke.edu

• Specific questions about grades or personal matters that may not be appropriate for the public course forum
• Please include "STA199" in the subject line.

Grading Components

• Homework (25%): Individual homework assignments combining conceptual and computational skills. The lowest homework grade will be dropped at the end of the semester

• Labs (15%): Individual assignments focusing on computational skills. The lowest lab grade will be dropped at the end of the semester

• Exams (35%): Two take-home open-note exams

• Final Project (20%)

  • Team project
  • Presentation during class on June 23
  • You must complete the final project and be in class to present it in order to pass this course. Please plan accordingly!

Grading Components

• Application Exercises (AE) (2.5%)

  • Practice applying statistical concepts and computing, Due next class meeting
  • Graded for completion

• Participation (2.5%)

  • You are expected to attend and participate in lectures and labs.
  • Frequent absences or tardiness may impact your final grade.

• Regrading requests

  • Must be submitted on Gradescope within a week of when an assignment is returned.
  • No grades will be changed after the final project presentations.

Late Work

• Under extenuating circumstances, please let me know as soon as possible before the deadline to waive the late penalty.

• For homework and lab assignments, there will be 10% deduction for each 24-hour period the assignment is late.

• Late work will not be accepted for exams, AEs, or the final project.

• Excused absences with legitimate reasons and forms do not excuse you from assignments and their deadlines.

Course Policies

• Uphold the Duke Community Standard:

  • I will not lie, cheat, or steal in my academic endeavors;
  • I will conduct myself honorably in all my endeavors; and
  • I will act if the Standard is compromised.

• Reusing code:
  • Cite properly if code from an outside source is directly used.
  • On homework or lab assignments, you may not directly share (or copy) code or write up with other students.
  • On the final project, you may not directly share (or copy) code or write up with another team.
• Any violations will automatically result in a grade of 0 on the assignment and will be reported to Office of Student Conduct for further action.

Learning Environment

• Respect, honor, and celebrate our diverse community

• Learning environment that is welcoming, inclusive, and accessible to everyone

• Please wear a mask to help protect your peers and others around you 😷

• Please do not come to class if you have symptoms related to COVID-19, have had a known exposure to COVID-19, or have tested positive for COVID-19.

  • You will still have access to slides, AEs, and lab materials remotely.
  • Online office hours and Slack are available to ask questions.
  • Email me if further arrangements needed.

Academic Resource Center

The Academic Resource Center (ARC) offers free services to all students during their undergraduate careers at Duke.

Services include

• Learning Consultations
• Peer Tutoring and Study Groups
• ADHD/LD Coaching, Outreach Workshops
• and more.

Contact the ARC at ARC@duke.edu or call 919-684-5917 to schedule an appointment.

CAPS

Duke Counseling & Psychological Services (CAPS) helps Duke Students enhance strengths and develop abilities to successfully live, grow and learn in their personal and academic lives.

Services include

• brief individual and group counseling
• couples counseling
• outreach to student groups
• and more.

Create a GitHub Account

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

After you create your account, go to https://forms.gle/WgBRjAoCJPb5eNBX7 and enter your name, Duke email address (NETID@duke.edu), and GitHub username.

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.
• Make it timeless.
• Avoid words with special meaning in programming (e.g. NA).

Bulletin

• Watch videos for Prepare: May 12

• Make sure you have access to Sakai and Gradescope

• Submit your GitHub username

• Join Slack

• Bring a laptop to class

Bulletin

• Reserve an STA198-199 RStudio container.

  • Go to https://vm-manage.oit.duke.edu/containers
  • Click Reserve Container for the STA198-199 container

• (Optional) If you wish to work "locally" on your own computer, you need to download

  • (1) the R programming language
  • (2) the RStudio integrated development environment (IDE) and
  • (3) Git.
  • Some resource: install R, RStudio instructions sheet for help