Statistics: Stat Computing&Data Anlysis

Fundamental data processing, code development, graphing and analysis using statistical software packages, including SAS and R. Analysis of data and interpretation of results. Project-based. Credit not awarded for both STAT 3010 and STAT 5010. Prerequisite: STAT 1410, STAT 2430, or STAT 3210; or STAT 1110 with Instructor permission.

Hybrid Course; Prereqs enforced by the system: STAT 1410, STAT 2430 or STAT 3210; or STAT 1110 with instructor permission; Open to Degree and PACE students

Stat3010 is a Tue/Thur HYBRID course. This means that half of our class time is spent in person (doing applied group activities) and half of our class time is spent online (learning material through recorded lectures). Learning statistical programming takes LOTS of practice and students often learn at different paces. This is why a hybrid approach works so well for this class – you can learn the statistical programming at your own pace (pause, rewind and replay the lecture as many times as you’d like!) and then practice applying what you’ve learned with in person, real-world group activities. Our in-class group, applied activity days will be on Tuesdays. The goal of STAT3010 is to teach students how to conduct data analyses using the statistical programs SAS and R. We discuss practical issues of using real data with an emphasis on real-world data analysis, including cleaning, management, manipulation, visualization, basic statistical procedures, and interpretation of findings. This is a “STAT” class and we will review some basic statistical methods learned, however, the class is more about using applying methods using statistical programs and not a course in statistical theory. It is important for students to have a foundational statistical knowledge before taking STAT3010. STAT3010 is a project based course.

This course has 9 learning modules. Each module is designed to take one week. Learning Module Components include: 1) Online Lecture: Consists of recorded video lectures and writing statistical programs. These lectures become available on Thursdays. You can access the video lectures for each week at your own time and your own pace (they are asynchronous). This allows you the flexibility to work through each lecture’s material in a way that best supports your learning style. This is great for flexibility, but really requires that you manage your time effectively and take responsibility for your learning. I strongly recommend setting aside 75 minutes at a regular time each week to complete the lecture materials. You are expected to have worked through the lecture material before the start of each group activity (Tuesday). In order to fully engage in the group activity, and be a contributing member of your group, it is essential that you have thoroughly absorbed the lecture material. 2) Quizzes (graded): Consists of a 10 question assessment in Brightspace. The quizzes are released on Thursday and are due 8 days later, the following Friday 11:59pm. You can view the quiz questions as often as you’d like, but you only get one chance to answer each question….so be sure you are comfortable with your answer before you hit submit, and be sure you don't "accidentally" hit submit before you are ready. 3) Small Group Activity (graded): Consists of an applied programming project that is designed to be worked through as a group. All students are randomly assigned to a small group of about 5 students each. You will meet with your assigned small group in person every Tuesday and work through the activity together. Activities are submitted for a group grade and are due 3 days later, Friday 11:59pm. Students are expected to make a plan with the group for how best to use Thursday class time. This course has 1 final project. The project is designed to take four weeks. After the learning modules are complete, we will begin to work on a group project. Each small working group will be provided with a dataset. The task will be to work together to develop and answer research questions, create a presentation to communicate your findings, and document your data analyses following reproducible research guidelines (you’ll have to apply the project management skills your group has developed over the previous weeks!). There will be an oral presentation of your final project during the last week of the semester and students will conduct a peer review of their classmates work. All students will receive an individual and group grade for the project work.

Course grades will be determined by: • Quizzes (9) – 30% • Group Activities (9) – 35% • Project (1) – 35%