Meet the teaching team: Instructor

Prof. Alexander Fisher

Meet the teaching team: TAs

• Christine Shen

• Zoey Liu

• Michael Sarkis

• Blossom Mojekwu

• Sara Mehta

• Mariana Izon

What is data science?

• Data science is using data in a principled way to gain understanding, insight, and knowledge.

• We're going to learn to do this in a 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 - 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. However this course is not your typical high school statistics course.

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

• 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

Examples of data science in practice

• Apple Mobility Trends

• What redisctricting looks like in every state

• Tracking their life via Fitbit

• US Spread of COVID-19 maps and analytics

Examples continued

• $26\times 25\times 24\times ...\times 2\times 1=4\times {10}^{26}$ possible combinations

• Check 100M per second

• 120,000,000,000 years of computational time

Course Toolkit

Course Website: sta199-f21-001.netlify.app

• Central hub for the course!
• Quick tour of the website

Sakai sakai.duke.edu

• Gradebook, quizzes

Slack: https://sta199-001-f21.slack.com/

• Questions and general discussion

GitHub: https://github.com/sta199-f21

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

Activities: Prepare, Practice, Perform

• Prepare: introduce new content and prepare for lectures
  • Videos & readings before lecture
  • Low-stakes prep quizzes on basic definitions & concepts from videos
  • See Prepare column on course schedule

• Practice: practice applying statistical concepts and computing
  • Application Exercises (AE) during lecture
  • Graded for completion

Activities: Prepare, Practice, Perform

• Perform: Put together what you've learned to analyze real-world data
  • Weekly team or individual lab assignments
  • Periodic individual homework assignments
  • Two take-home exams
  • Final team project presented during the final exam period.

Where to find help

• Attend Office hours to meet with a member of the teaching team.

  • 10a-12p this Friday via zoom
  • Full office hours begin the week of August 30

• Use slack for general questions about course content and/or assignments, since other students may benefit from the response.

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...

Course community & Learning environment

• 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.

• Respect, honor, and celebrate our diverse community

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

Attending lectures and labs

• You are expected to attend and participate lectures and labs

• 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 AEs and labs to complete remotely
  • Online office hours and slack are available to ask questions
  • Email Professor Fisher if further arrangements needed

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/t4Z6jadPmyZhHDvq6 and enter your 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).

Let's look at some data!

• Let's take a look at the UN Votes analysis.

For Thursday

• We'll start talking about the computing toolkit

• Watch videos for Prepare: Week 01

• Reserve an STA198-199 RStudio container.

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

• Submit your GitHub username

• Bring laptop/tablet/Chromebook to class

• Join slack