Welcome to MATH/STAT 348!

MWF 12:15-1:15 PM

Morken 214

I love this course because it empowers you to tell the stories of data while grappling with difficult questions that arise when analyzing real data.

Learning Goals*

The main learning goal of this course is that students will be able to... (drumroll)

...develop expertise working with, interpreting and communicating the stories of data

more specifically, students will:

Grapple with ethical considerations in the data age.
Dabble with qualitative data analysis
Develop expertise with statistical computing so as to be able to prepare and analyze data
Apply principles of data visualization
Collaborate to integrate statistical analysis techniques and interpret them in a comprehensive analytical report and presentation.

All the assignments and lessons in this class are geared toward these learning goals.*

*More learning outcomes from the mathematics department and PLU's general education are given later.

Stuff you need

No Book purchase required

There is no book purchase required for this course. Readings will be made available via the course website. Here are some of the resources we will use...

A ModernDive into R and the Tidyverse by Chester Ismay and Albert Y. Kim

Introduction to Modern Statistics by Çetinkaya-Rundel and Hardin. I like IMS because it uses the most cutting-edge tools to teach statistics, and it covers relevant topics. (In other words: it is not a crusty outdated old book.)

Google Classroom

The overwhelming majority of my students have reported they like when I used Google Classroom (GC) with my website. So, we're going for it again this semester. You will need to scan your homework and submit as a pdf into GC.

To use your phone to scan homework to a pdf & submit to GC:

Submit with iPhone: https://youtu.be/EIUpRE_xPKc

Submit with Android: https://youtu.be/AbLFIgJq5sc

Why use R? R is...

free
powerful
popular
open-source
great on resumes!

R Statistical Software

This course uses R statistical software with R Studio (both of which are FREE!) Instructions for downloading and getting started are in ModernDive, here.

R can be intimidating (and at times frustrating) at first, but no computing expertise is expected of students entering this course. That means you can reach out to me and your peers for help with ANY questions. We will learn together!

Never used R before? Here are videos to help you get started!

PLU email

To be successful in this course, you will need to activate and use your PLU email (ending with @plu.edu). Your email will contain an invitation to the course website that we will use daily, as well as occasional announcements and notifications about the course.

Although we will use Google Classroom for delivering and submitting assignments and forums, in general we will use this course website for communicating course content.

Prerequisites

To be successful in this course, you should come in being entirely comfortable with the basics of introductory statistics, including (but not limited) to notions of p-value, confidence intervals, study design (observational studies vs. experiments) and the implications for inference (causal claims vs. claims of association).

Some students in this course are computer science majors; others are not. Regardless of your major, no prerequisite computing skills are required.

You also need to have patience and grit; some aspects of the course will require you to persevere, ask questions, and hang in there.

Katyal, Prateek. (2019) "You Got This Lighted Signage." Licensed under Pexels. Retrieved from Pexels.

Course Format & What to Expect

fauxels (2019) Photo Of People Near Wooden Table. Licensed by Pexels: Free to Use. Retrieved from Pexels.

Here is what you can expect from me:

Communication: I will make it clear what the tasks are each day and what upcoming deadlines are.
Purposefulness and Respect for students' time: I have carefully examined what topics we will cover with an eye for what is most important for students to learn. I will not assign readings or lessons unless they are appropriate for the topic of this class and the level of mathematics this course is designed for.
Transparency: Last semester, students were generally really positive about my teaching, but offered suggestions for how to make the course better. I have implemented many of these and will continue to try to make it clear what I'm thinking, why I'm doing it, and asking you how its going.

GENERAL Weekly Schedule

I created this table to help you get a sense of what our week will typically look like:

I am also available to meet by appointment if none of the given student hours work in your schedule!

Coursework

Labs/Assignments: 50%

These include homework assignments, and in-class assignments (which can become homework if unfinished during class). Some may require or allow you to work together in small groups.

Check-up Quizzes / Reflective Reports and More: 5%

You can expect weekly check-up quizzes designed to help you receive quick feedback on whether you are grasping the main ideas of the course so far. There is no time limit.

NO quiz retakes are given, even for good reasons, such as illness. Instead, at the end of the semester students who complete their final course evaluations earn 5 extra credit quiz points (equivalent to one missed quiz).

Also in this category is Reflective Reports, designed to help you reflect on your learning, as well as a CITI Human subjects training.

Notice that this category is only 5% of your grade; it is designed to be low-stakes and low-stress feedback on your understanding of course materials.

Projects 40%

This class has three large projects:

Data Viz project (10%)
Qualitative Analysis Project (15%)
Final (overall) Consulting Project (15%)

More information about these will be given later.

DataFest: 5%

DataFest is an annual event sponsored by the American Statistical Association. Much like a code-a-thon, the event lasts for about 48 hours and gives you a chance to do a deep dive with a real problem using real data with mentors available to help. This year's DataFest is tentatitvely scheduled for April 17-19!*

In addition to uploading your final presentation video to the DataFest competition, you will also submit your group's slides and video as an assignment in this class. Finally, you'll watch peers' videos and write a reflection on what you learned.

*If you are unable to participate during that weekend please see me as soon as possible to work out an alternative

*Maximum score allowable in any category is 100%

Grades

Grades of A, A—, B+, B, B—, C+, C, C—, D, and F are assigned at cutoffs of 93%, 90%, 87%, 83%, 80%, 77%, 73%, 70%, 60%, and 0%, respectively.

Please see your PLU Catalog for general policies on grading, incomplete, P/F, and W grades. Online you can also find the PLU policy in case of an academic emergency (e.g., natural disaster, epidemic, etc.).

Incomplete grades are only assigned to students who have completed at least 50% of course work by the end of the semester, and who were unable to complete the course requirements due to circumstances beyond their control.

Learning Objectives (Formally)

This course is aligned with the following PLU Math Department Learning Objectives:

Communication: Be able to read, interpret, write about, and talk about mathematics (or statistics).
Application: Be able to apply mathematical concepts to concrete situations.
Disciplinary Citizenship: Develop collaborative skills, independence, perseverance, and experience with open-ended inquiry.

This course is aligned with the following Statistics Minor Objectives:

Develop novice-level statistical thinking, particularly with respect to linking appropriate inferences to study design (e.g., correlation does not imply causation).
Demonstrate the ability to appropriately select and use statistical models (e.g., normal distribution, t-distribution, binomial distribution) and statistical methods (e.g., regression, resampling).
Develop facility with one or more professional statistical software programs.

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More Syllabus

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