• In catalog: Introduction to Statistical Sciences
• New: Introduction to Statistical and Data Sciences

• Example domains: biology, economics, physics, sociology, etc.
• So why the title switch?

Have students engage in the data/science research pipeline in as faithful a manner as possible while maintaining a level suitable for novices.

• Cobb: Minimizing prerequisites to research
• Not necessarily publishing in top journals, but answering scientific questions with data.
• Difficult to do research without understanding stats, however

We will, as best we can, perform all this:

And not just this, as in many previous intro stats courses:

Foster a conceptual understanding of statistical topics and methods using simulation/resampling and real data whenever possible, rather than mathematical formulae.

• Whenever we can, use real data
• "Classroom data are like teddy bears and real data are like a grizzly bear with salmon blood dripping out its mouth."
• Example data set: nycflights
• There are two "engines" that can make statistics "work"
  • Mathematics: formulas, approximations, etc
  • Computers: simulations, random number generation

In this course, computers and not math will be the "engine". What does this mean?

• Less of this:
  
• But more of this:
  

• Previous programming/coding experience is not a prerequisite to this course
• This course is NOT a course on programming, coding, nor computer science
• You will however be exposed to basic computational and algorithmic thinking

Blur the traditional lecture/lab dichotomy of introductory statistics courses by incorporating more computational and algorithmic thinking into the syllabus.

• Completely separate lecture and labs is a legacy of a time before
  

• Not all laptops are created equal: operating system, processing power, age
• RStudio Server: cloud-based version of RStudio where all processing is done on Middlebury servers
• go/rstudio (on campus or via VPN)

Undirected time to

• Set aside to help people get up to speed with computing
• It is like learning a language.
• Learn from each other.
• Collaborate on problem sets (due on Fridays)

Develop statistical literacy by, among other ways, tying in the curriculum to current events, demonstrating the importance statistics plays in society.

• H.G. Wells (paraphrased): "Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write."
• Me: "Sure, it's easy to lie with statistics. But it's also hard to tell the truth without them."

• EDA = Exploratory data analysis
• "Data visualization is a gateway drug to statistics."
• Prez from Season 4 of "The Wire":
• 

• Final exam
• Capstone experience to align this topics and principles of this course with how research and learning is done in practice.
• Work on interpersonal and collaborative skills. No textbook on that!