Most people would sooner die than think, and most of them do.
This course pulls many of the diverse capabilities developed throughout the other courses in the data science degree together to give students a complete skill set in and a better understanding of data programming. If you have signed up for this class, you are most likely driven by curiosity and interested in how data decisions are made (sometimes called data intuition). Possibly, you have a more empathetic approach to how the world works and how problems can be solved. Finally, you have an eye for visualization and how data is communicated to make impactful decisions.1
Upon completion of this course, you will be able to use data-driven programming in R for the handling, formatting, and visualization of messy and complex data. You will implement data wrangling techniques and the grammar of graphics process in visualizing complex data. Specifically, as a successful learner, you will be able to
The course follows these principles of teaching Data Science2
This is a data coding class. The prerequisites for this course include introductory statistics and introductory programming in Python or R. As such we assume that you do know what a console does and how to execute scripts. We will not focus on traditional statistical hypothesis testing or complex statistical modeling, but we will leverage the concepts of how to visualize uncertainty and variability. A firm understanding of standard deviation and variance is needed. Here is a text review as well.
We will meet for 1.5 hours on two days a week. and use the following weekly rhythm. Material for each class is found on the class meeting page.
In my experience, getting lectured training outside of college is even more expensive than it is in college. A week’s worth of training can cost more than a semester of school here at BYUI.3 Due to this expense, learning how to digest material that is online and get up to speed on a topic before going to the expert for questions is a valuable skill to develop. I expect that you have completed the assigned reading material before class begins. You will also have coding tasks to complete after the reading and before class starts.
As described in the weekly format, we will have shorter assignments due at the beginning of each period and weekly case studies. We will use class time to enforce the programming and visualization concepts needed for the weekly topic covered in the case study.
Each of you will be responsible to create a data driven question, find the data to answer this question, and build a visual analysis that answers your question with data. A few notes on this project;
You can see the semester project task here.
Grading is a nasty side effect of mass learning and academia. We are in a class at a university and will have to manage this side effect. However, we don’t have to let it control our learning, thinking, or this class. Learning and thinking should motivate each activity.
As we team, teacher and student, we have a challenge to become more in three months! We have worked hard to identify the specifications needed for a data visualization specialist (as an undergraduate). My goal is to align your grade with the skill specification you have mastered. In other words, the grade you want will determine how much work you will do. Individual tasks in the class will not be traditionally graded. If your work meets the specified criteria you will get full credit and only then (there is no partial credit on tasks).
In a specifications-grading system all tasks are evaluated on a high-standards pass/fail basis using detailed checklists of task requirements and expectations4. Letter grades are earned by passing marks on a set of tasks. This system provides for a variety of choice and is closer to how learning, and work, is done in the real world. It will be easy for us to tell if work is complete, done in good faith, and consistent with the requirements.
We have five concepts that guide our learning objectives. Click on each to see an example task that we could complete this semester.
|Grade||Class Tasks||Case Studies||On Time||Semester Project|
|1 See the tasklist for a description of the ‘On Time’ column values|
In all grade levels above a C-, case study 13 must be completed.
We will have three in-class coding challenges (Task 16, Task 24, & last day of class) that you will need to complete. The coding challenge on the last day of the course is the one that can affect the grade earned above.
I may drop your grade up to three steps (e.g., from an A- to a B-) from your requested grade depending on your performance. I will report the grade quality of your first two challenges in I-learn. Generally, students sought grades are in line with the caliber of their coding challenge performance, and no change is made to their final grade.
After reviewing the material above, please make a list of learning goals you have based on this class. We would enjoy talking with you about those goals in the first few weeks of the semester. We look forward to working with you this semester.
Our class will uphold the following as well.
Additionally, textbooks often are not available when needed or too expensive to get your employer to purchase every time you want one.↩
Making the right checklists can be difficult. Bad checklists could fall in the following categories – vague and imprecise; too long; hard to use; impractical; too pedantic. Good checklists are precise, efficient, easy to use and understand. This is the first time this course has been offered so we will have to work together to make sure requirements are good.↩