Syllabus
Click here to download a PDF copy of the syllabus.
Course info
| Day | Time | Location | |
|---|---|---|---|
| Section 20 | MWF | 9:00 am - 9:50 am | Lutkin Hall |
Prerequisite: High School Algebra
Please note that the specifics of this course syllabus are subject to change in the case of unforeseen circumstances. Instructors will notify students of any changes as soon as possible. Students will be responsible for abiding by the changes.
Learning objectives
By the end of the quarter, you will be able to…
- Use statistical software to manage and process data.
- Use statistical software to perform exploratory data analyses. That is, explore data numerically and visually to gain understanding through data and generate hypotheses and inferences to later test.
- Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference.
- Build a conceptual understanding of the unified nature of statistical inference.
- Apply estimation and testing methods to analyze single variables or the relationship between two variables in order to understand natural phenomena and make data-based decisions.
- Model numerical response variables using a single or multiple explanatory variables.
- Interpret results in context without relying on statistical jargon.
- Critique and evaluate data-based claims and decisions.
Course Structure
A part of the class time will be dedicated to discussion/lecture, while another part will be dedicated to working on activities. A lot of what you do in this course will involve writing code, and coding is a skill that is best learned by doing. Throughout the class we will discuss and review the work on the activities. In many cases we may come together to work on parts of an activity as a class.
Textbooks
We will be using Introduction to Statistics and Data Science which is a free online book that we have been developing for this course.
Software
We will be using/introducing the free statistical software Posit Cloud.
Hardware
Students will need a laptop or Chromebook to be able to follow lectures and to work with Posit Cloud to complete activities. If access to a laptop is an issue, then please contact the course instructor and we will work to find an accommodation.
Assessment
Assessment for the course is comprised of five components: reading checks, activities, 3 exams, and a final project.
Reading Checks
Reading checks will be completed using “Tutorial Software” on Posit Cloud and uploaded to the course Canvas page. Each reading check will be scaled to be worth 10 points.
The lowest three grades will be dropped at the end of the quarter.
Activities and small assignments
Daily activities will be worth 10 points and graded for completion (10 points fully complete; 5 points mostly complete; 0 incomplete). This doesn’t mean that your solutions are correct, so make sure that you check your answers against the solutions. Activities will be accepted up to 3 days after the due date with a 10% late penalty.
The lowest three activity grades will be dropped at the end of the quarter.
Exams
There will be 3 in-class exams. Roughly half of it will focus on conceptual knowledge and roughly half will focus on practical applications. Students will be allowed one 8.5 x 11 inch hand-written cheat sheet (front & back) on each in-class exams. The exams are not cumulative.
Project
The final project will be completed in groups of 3-6 people and allow you to explore a dataset of your choice. More information will be provided later in the quarter.
Exam Improvement Policy
We have worked to develop a policy geared towards a growth mindset. That is, we want a policy where students clearly demonstrate that they have used the exam as a diagnostic tool to learn from and improve their understanding of statistics. There is NO final cumulative exam during the designated final exam time, instead you may choose to retake 1 exam during the exam time. This exam will replace your old score — only in cases where it is an improvement.
Missed Exam Policy
There are no make-up exams. If you miss an exam due to illness, travel, etc., you will need to take the exam during the final exam period as your re-take exam.
Grading
The final course grade will be calculated as follows:
| Category | Percentage |
|---|---|
| Reading Checks | 15% |
| Activities/Small Assignments | 10% |
| Exam 1 | 20% |
| Exam 2 | 20% |
| Exam 3 | 20% |
| Final Project | 15% |
The final letter grade will be rounded to nearest tenth of a percent and determined based on the following thresholds:
| Letter Grade | Final Course Grade |
|---|---|
| A | >= 85 |
| A- | 85 - 80 |
| B+ | 80 - 75 |
| B | 75 - 70 |
| B- | 70 - 65 |
| C+ | 65 - 60 |
| C | 60 - 55 |
| C- | 55 - 50 |
| D | 50 - 45 |
| F | < 45 |
Tips for success
- Dedicate yourself to being an active and engaged learner.
- Prepare for class by reading and working through code before class.
- Work in groups to learn and complete activities.
- Ask questions! Ask them during class, office hours, or on Campuswire.
- Contribute to a welcoming and inclusive learning environment.
- Don’t be afraid to make mistakes, you learn from mistakes.
Asking Questions & Course Communication
This term we will be using Ed (see website for access) as our preferred platform for questions about activities, reading checks, and general course questions. The system is highly catered to getting you help quickly and efficiently from classmates and the instructional team. Rather than emailing questions to the instructional team, you should post your questions on Ed.
The instructional team will check Ed periodically and answer questions, but we strongly encourage students to answer each other’s questions.
Please do not expect answers during weekends and evenings.
Drop-In Support (No Appointment Needed)
Drop-In Peer Tutoring is set up such that students can drop in to study alone or with others and ask questions of a peer leader who has done well in the class. Tutoring is provided for many of the introductory courses in Biology, Chemistry, Economics, Engineering, Mathematics, Physics, and Statistics. Check their website for a complete list of supported courses.
Contact Valerie Wolf (valerie.wolf@northwestern.edu) with any questions.
Attendance Expectation/Policy
While we do not collect formal attendance, implicit in the course design it is expected that you attend class to benefit from working with others — either by helping others or by helping others learn.
If you are unable to attend class due to a legitimate reason, please make sure you ask your class-mates about any in-class announcements, and keep up with the deadlines on the course website / canvas. You don’t need to tell the instructor that you are not attending class (except in special cases, such as on exam days).
For students athletes travelling on exam days, please make sure I receive an email from your coach mentioning that they will proctor the exam on the exam day, and the NU document mentioning the dates you are travelling. If you are missing class on non-exam days, make sure you are keeping up with homework as per the course schedule on the website / canvas.
For ANU students with extra time, your exam will start at 9 am on the exam day, and will end as per the time limit recommended by ANU. Exam location will be confirmed some time before the exam.
Syllabus statements, generative AI, and academic integrity}
This course follows the Northwestern University Syllabus Standards. Students are responsible for familiarizing themselves with this information.
In addition to the aforementioned standards, if you copy code, idea, or text from someone, or from some online resource without citing it, you will fail the course. Plagiarism will result in the F grade.
Academic work and classes missed for medical reasons
In partnership with Student Health (Northwestern Medicine), the undergraduate schools have devised a system for students to request an excuse note. Any student who becomes ill must make use of Northwestern Medicine’s process for missing academic work for medical reasons.
Important dates
Click here for the full academic calendar.