Office: 22c Sheilds Building
email: fcw5014 [at] psu [dot] edu
Office Hours Zoom Friday 3:30-4:30pm
By appointment
Section 1:
Time/Day: TR 1:35PM - 2:50PM
Location: zoom
As part of this course experience, we will experiment with recording the class meetings using Zoom (https://psu.zoom.us). Please NOTE: This is experimental, and there is a risk the technology will not work correctly each class session. Please do not depend on this technology for course content, your attendence is manditory. If you miss class and this technology fails you will still be responsable for covered course content as well as any assignment given in class. The recordings are only available to you from within our Canvas course site in Canvas Media Gallery; videos will not be viewable by anyone outside of the class. If for some reason you do not want your voice recorded, please let me know. The only live microphone will be attached to me and I will cover the microphone when students ask questions to minimize the potential of recording voices other than my own. If you have any concerns regarding the use of this technology in class please let me know in person or by email.
The teaching staff will make every effort to make class recordings available shortly after the class session. The recordings will also be used in conjunction with an artificial intelligence application that is designed to provide instructors with information about their course, such as the percentage of time they spend across a semester focusing on specific course topics. Please share your experience and impressions with using the Zoom recordings!
Laptops: Bring a laptop to class each day if you have one. Please let me know if you do not have a personal laptop.
An Introduction to Statistical Learning
- Base R - Cheatsheet
- datatables(cheatsheet)(cheatsheet2
- ggplot2 - Cheatsheet
- stringr - Cheatsheet
- lubridate - Cheatsheet
Follow the below links download and install the appropriate version of R, R Studio and Atom for your operating system
We will be using Kaggle for class-related discussion and questions, to help you benefit from each other’s questions and the collective knowledge of your classmates and professor. Questions can be posted to the entire class (for content-related questions). I encourage you to ask questions if you are struggling to understand a concept, and to answer your classmates’ questions when you can.
Do Not use Kaggle for issues related to your grade or other private matters; email those questions or comments to the instructor directly or discuss them in person.
Most issues about classroom activities can be posted to Kaggle, but you should use email (or a conversation in person) for all personal or private matters.
Learning outcomes will be assessed based on performance in each of the following categories accompanied by their impact on the overall grade:
- 30% Weekly Activities (in-class and HW)
- 20% Performance on Weekly Activities
- 10% Reading Quizzes
- 20% Midterm Project Results
- 20% Final Project Results
Weekly in-class assignments will include an activity assigned and completed in part or whole during class. The format and length of in-class assignments will vary as warranted by the subject matter each week, although each assignments will be given the same weight toward the overall grade. There are no make-up assignments.
Reading quizzes will be due before class in order to assess comprehension of the reading assignment that will be discussed each week. This allows students to see new content and concepts for the first time at their own pace in order to more effectively use class time to emphasize main points, clear up confusion, etc. The goal of the reading quiz is to hold students accountable for completing the reading each week before class.
The official course description is available in Penn State’s University Bulletin linked here, but a recent version is reproduced below for your convenience.
A case study-based course in the use of computing and statistcal reasoning to answer data-intensive questions. STAT 380 Data Science Through Statistical Reasoning and Computation (3) This course addresses the fact that real data are often messy by taking a holistic view of statistical analysis to answer questions of interest. Various case studies will lead students from the computationally intensive process of obtaining and cleaning data, through exploratory techniques, and finally to rudimentary inferential statistics. This process will exploit students exposure to introductory statistics as well as the R programming language, hence the required prerequisites, yet novel computing and analytical techniques will also be introduced throughout the course. For the collection of data, students will learn scripting and database querying skills; for their exploration, they will employ R capabilities for graphical and summary statistics; and for their analysis, they will build upon the basic concepts obtained in their introductory statistics course. The varied case studies will elucidate additional statistical topics such as identifying sources of bias and searching for high-dimensional outliers.
All quizes and tests must be done individualy without the aid of other students. In-class activities may be discussed in groups but must be completed individualy. Submissions will require the submission of code, the first line of code must always contain a commented out author line. Making a submission without code that is able to produce the exact submission will invalidate the submission. Turning in code with an author line that does not reflect the origins of the work is a violation of academic integrity.
The Eberly College of Science Code of Mutual Respect and Cooperation embodies the values that we hope our faculty, staff, and students possess and will endorse to make the Eberly College of Science a place where every individual feels respected and valued, as well as challenged and rewarded.
Academic dishonesty is not limited to simply cheating on an exam or assignment. The following is quoted directly from the “PSU Faculty Senate Policies for Students” regarding academic integrity and academic dishonesty:
Academic integrity is the pursuit of scholarly activity free from fraud and deception and is an educational objective of this institution. Academic dishonesty includes, but is not limited to, cheating, plagiarizing, fabricating of information or citations, facilitating acts of academic dishonesty by others, having unauthorized possession of examinations, submitting work of another person or work previously used without informing the instructor, or tampering with the academic work of other students. All University and Eberly College of Science policies regarding academic integrity/academic dishonesty apply to this course and the students enrolled in this course. Refer to the following URL for further details on the academic integrity policies of the Eberly College of Science: http://www.science.psu.edu/academic/Integrity/index.html. Each student in this course is expected to work entirely on her/his own while taking any exam, to complete assignments on her/his own effort without the assistance of others unless directed otherwise by the instructor, and to abide by University and Eberly College of Science policies about academic integrity and academic dishonesty. Academic dishonesty can result in assignment of “F” by the course instructors or “XF” by Judicial Affairs as the final grade for the student.
Penn State welcomes students with disabilities into the University’s educational programs. If you have a disability-related need for reasonable academic adjustments in this course, contact Student Disability Resources (SDR; formerly ODS) at 814-863-1807, 116 Boucke, http://equity.psu.edu/student-disability-resources. In order to receive consideration for course accommodations, you must contact ODS and provide documentation (see the guidelines at http://equity.psu.edu/student-disability-resources/guidelines).