Course Description: The course provides an introduction to obtaining, restructuring, and  visualizing complex data sets. Students will learn to manipulate many data types, store data in a variety of structures, and construct static and dynamic plots in a variety of contexts.

Overview: The practice of data science requires scientists to be skilled in manipulating and visualizing data. In this course, we will learn how to use advanced techniques for     acquiring, manipulating, and plotting data in R. There will be a focus on interpreting       results, which is essential for making good decisions from data.

Prerequisites: Math 1376 or 4387 or CSCI 1410/11; Math 2830 or 3382.

Optional Textbook: R for Data Science by Hadley Wickham and Garrett Grolemund.  ISBN: 978- 1491910399.  Freely available athttps://r4ds.had.co.nz/.

Computing:

Students will need a computer that can install R and R Studio (PC, Apple, and most Linux machines should work).

Students will need regular internet access to view videos, join office hours, submit homework, etc.

Students need access to a computer with a webcam and microphone to effectively engage in office hours, etc. A phone would also likely work, but a computer will provide a much   better experience.

We will use the freely available R statistical software for data analysis in this class. R is a  free, cross-platform statistical package that is extremely powerful.  It is the standard         statistical computing language in academia and becoming more popular in the public and private sector.  The language is somewhat similar to Matlab.  R may be downloaded at https://cran.r-project.org/ .

RStudio is a free and open-source integrated development environment (IDE) for R           available athttp://www.rstudio.com/.  RStudio is also cross-platform and has many nice features (like the ability to scroll through your plots, syntax highlighting, viewing the        objects in your environment, etc.) that you may find useful.

Course Goals and Learning Objectives:

Overall Learning Objectives

•   Students will learn efficient approaches for acquiring, manipulating, and plotting complex data structures.

•   Students will learn to manipulate a variety of data types.

•   Students will learn to restructure a variety of data types, with an emphasis on data frames.

•   Students will learn to construct static plots for discrete and continuous data.

•   Students will learn to constructed plots for grouped data.

•   Students will learn to draw geographic maps.

•   Students will learn to constructive interactive/dynamic plots.

Learning Outcomes

Several specific learning outcomes will be strengthened in this course.

Assignments will frequently require you to explain and interpret what you are doing or the results you are saying. This will help you improve your ability to, “Communicate mathematical ideas clearly and coherently both verbally and in writing to audiences of varying mathematical sophistication.”

Every assignment will involve programming. This will help ensure that you, “Possess basic programming skills and be able to use mathematical software or other applicable technological tools.

Because this course is asynchronous with no lecture, it will require a lot of hard work by   you on your own. This will help you to, “Learn a mathematical topic without reliance on an instructor.”

Rationale

Students need to be able to efficiently manipulate and display data for effective data             science.   This course will prepare you to manipulate foundational data types and                  structures in data science.  Additionally, graphical techniques are essential to exploring      data and suggesting appropriate modeling techniques.  Students will gain valuable skills in constructing classic plots for various data types, as well as more advanced plotting               techniques for complex data.

Grades: Grades will be determined by a combination of homework assignments, quizzes, and a final project.

Homework:  One or more homework assignments will be assigned for each module of         material.  Unless otherwise stated, assignments must be completed as an R Markdown        document and submitted as a pdf file through Canvas by the appropriate deadline.                Individual problems should be clearly numbered, and problem parts should be clearly         labeled.  Relevant computer code and output should be provided with your answer to each problem.  Some of the assigned problems will be selected for grading (as few as one              problem and as many as all of them).  These problems alone will be used to determine a      student’s grade for that assignment.  Each selected problem will be worth 10 points and     graded using the Homework Rubric provided on Canvas.

Quizzes: Quizzes will be assigned for each section of material. These will be conducted electronically through Canvas.

Projects: All students must complete a final project related to the Auraria Library’s Data 2  Policy program (library.auraria.edu/d2pproject). Students will need to obtain, analyze, and present their analysis in a short video presentation. Students will be graded on the quality  of their analysis, their code, and their presentation. Tentatively, these projects will be           presented on Friday, December 2nd via a prerecorded video a few days prior. There may be a second part of the analysis related to making a web application.

Grade determination

Final grades will be determined according to the following weighting scheme:

Homework

Quizzes

Project

60%

20%

20%

Letter grades will be determined according to the following scale:

Letter grades will be determined by the following scale:

Percentage

92 or higher

90 up to 92

88 up to 90

82 up to 88

80 up to 82

78 up to 80

70 up to 78

68 up to 70

62 up to 68

60 up to 62 Below 60

Letter Grade

A

A-

B+

B

B-

C+

C

D+

D

D-

F

I reserve the right to lower or raise this scale as needed.  This is generally only necessary to ensure borderline grades are appropriately assigned a letter grade, although it may be          necessary to adjust for overly difficult or overly easy exams.

Grade/Assignment Dissemination

Grades will be provided via the course’s Canvas course shell. You can access your scores at any time within the Canvas gradebook.

Homework, quizzes, exams, etc. will be returned electronically via Canvas.

Recommend preparation:

•   Expect to work hard!  Programming requires a unique way of thinking.  Expect to spend several hours studying and working on homework for each class period.

•   Don’t be afraid to ask questions!  It is likely that other students need clarification about the same topic.  Questions are welcomed during office hours, via email, etc.

•   Students are expected to read the provided lecture material and watch the provided videos.

•   Quizzes should be completed prior to the homework assignment. They will test you on important aspects of the material from a more conceptual perspective.

•   Homework should be completed as the homework sets are assigned.  Students will find little benefit in rushing to complete homework assignments.

Class pedagogy:

•   Learning will primarily take place in the form of R Markdown documents and recorded videos discussing the relevant material.

•   Projects will take the abstract and make the tools we’ve learned relevant to the real world.

•   Course announcements will be made frequently through Canvas and/or email.  Emails will be sent to your ucdenver.edu email address in accordance with university policy.  You are responsible for the information contained in any announcements or messages I send you,  regardless ofwhether the information is repeated in class.  It is your responsibility to          frequently check Canvas and to maintain your university email address.

Classroom policies: N/A

Absences, Tardiness, Homework, Quizzes, Exams, etc.: Except for documented health,    disability, emergency reasons, or prior approval, I will not accept excuses for absences or     tardiness.  Unless otherwise specified, missed or late homework, quizzes, projects, or exams will be scored as a zero.

I reserve the right to modify this syllabus as the semester progresses.

Tentative Course Schedule: The following course schedule is tentative.  Homework                    assignment due dates will be determined by how quickly we move through material and will be given throughout the semester.  I reserve the right to modify this schedule as the semester          progresses.

University, college, and department policies

Academic Calendar

For university  deadlines and procedures  (such as the last day to withdraw from a  course), please see the

Academic Calendar.https://www.ucdenver.edu/student/registration-planning/academic-calendars

Academic Support

Instructor office hours or other appointments are the best way to get additional help. I’m happy to help with questions not answered during lecture, additional explanation, or homework assistance.

Other sources of support are

•  The Math and Stat Support office is located in the Learning Commons Building Room 1225 and regularly   offers CU Denver students free drop-in assistance. Hours of operation, zoom links for virtual options, and other forms of support for mathematics and statistics courses are available on the Math and Stat                Support webpage.

https://clas.ucdenver.edu/mathematical-and-statistical-sciences/math-and-stat-support

•  The Learning Resources Center (LRC) provides individual and group tutoring, Supplemental Instruction (SI), study skills workshops, and ESL support.

https://www.ucdenver.edu/learning-resources-center

•  The College of Liberal Arts and Sciences has a summary of campus academic support and school/college ad- vising offices.

https://clas.ucdenver.edu/faculty-staff/content/clas-academic-policies-deadlines

Recording of Class Meetings

Class meetings held on or streamed over a video conferencing platform (such as Zoom, Microsoft Teams, etc) may be recorded and posted for all members of the class.  Student participation and interaction may be            included in the recording. If you have any concerns about this, please contact the instructor.

Diversity Statement

It is my intent that students from all diverse backgrounds and perspectives be well served by this course, that    students’ learning needs be addressed both in and out of class, and that the diversity that students bring to      this class be viewed as a resource, strength and benefit.  It is my intent to present materials and activities that are respectful of diversity: gender, sexuality, disability, age, socioeconomic status, ethnicity, race, and culture, etc.  I would like to create a learning environment for my students that supports a diversity of thoughts,             perspectives and experiences, and honors your identities (including race, gender, class, sexuality, religion,        ability, etc). To help accomplish this:

•  If you have a name and/or set of pronouns that differ from those that appear in your official records, please let me know!

•  If you feel like your performance in the class is being impacted by your experiences outside of class,        please don’t hesitate to come and talk with me.  I want to be a resource for you.  Remember that you can also submit anonymous feedback (which will lead to me making a general announcement to the class, if    necessary to address your concerns). If you prefer to speak with someone outside of the course, the         Office of Diversity, Equity and Inclusion, is an excellent resource.

•  I (like many people) am still in the process of learning about diverse perspectives and identities.  If         something was said in class (by anyone) that made you feel uncomfortable, including by me, please talk