Course Descripঞon:

Basic principles and techniques in data analytics; methods for the collection of, storing, accessing,    and manipulating standard-size and large datasets; and various techniques of data visualization. The concepts will be implemented with R.

Course Objecঞves:

Students who finish this course at a minimum should be able to:

Read and write R code efficiently.

Write reports in R Markdown.

Apply data gathering and data manipulation methods in R.

Analyze data sets using exploratory data analysis (EDA) methods in R.

Identify when to use specific statistical and visualization methods.

Implement a wide variety of Statistical Learning techniques in R.

  Effectively communicate basic results of analyses with others.

  Participate in the Data Science process.

Assessment:

Complete descriptions and instructions for completing assessments will be provided when                 assignment is made. Dates for assignment of assessment and due dates for assessment completion will be indicated on the course schedule (https://canvas.vt.edu/courses/152169/pages/course-             schedule) .

Assessment

Final Grades:

All assignments receive a numeric grade. Your final grade will be converted to a letter grade as follows:

Grades

Texts:

There is no required book for this course, but there is a free book that you can read for a deeper look at some of the topics.  Note: This book is a too advanced for this course, but can provide some         interesting summaries of some of the techniques that we will study throughout this course.

  Free online pdf: http://www-bcf.usc.edu/~gareth/ISL/     (http://www-bcf.usc.edu/~gareth/ISL/))

“An Introduction to Statistical Learning with Applications in R” by Gareth James, Daniela Witten,

Trevor Hastie and Robert Tibshirani

Materials:

All reports must be submitted in a .pdf format to canvas.  You will use R Markdown to write these reports.  This can easily be done using RStudio (see below).

Technology: (h‚ps:/canvas.vt.edu/courses/152169/pages/technology- needed)

R is a programming language (like Python and MATLAB) and software environment for statistical computing and graphics.  RStudio is a free and open source integrated development environment (IDE) for R.

R can be run independently of RStudio, but RStudio needs R installed to run. We recommend installing and using RStudio as it is much easier to use than base R.

R can be downloaded here:  https://cran.r-project.org/    (https://cran.r-project.org/) RStudio can be downloaded here (scroll down to the bottom):

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Additionally it is recommended that you download and install LaTeX.

https://www.latex-project.org/get/    (https://www.latex-project.org/get/)

Course Structure:

This course will be taught using asynchronous online lectures. The material is completely accessed via Canvas. The material will be presented using a combination of lecture slides and highly             commented documents containing R code. You are expected to follow these slides and documents by reading and practicing within RStudio at your own pace. Additionally, I will have regular Zoom    "class" sessions during the week (days and hours are tentative and subject to minor revisions and  will be announced on the Canvas page).

There will be homework and quiz activities based upon the reading. The due dates will be shown at the top of the assignments and in the course schedule on canvas. Since this is a summer course, I have broken the schedule into 6 weeks. With the exception of week 1 and week 6, each week has  exactly two homework assignments. Some weeks may have additional material due, such as the    material related to the course project.

The time commitment for this course is generally fairly high. Each week in the summer is equivalent to 2.5 weeks of the regular semester. While you are approaching the material using a self-paced     manner, the practice of R can be time consuming if you are still somewhat new to programming.      Spending 20-30 hours per week for this summer course is not out of the question.

The course project is designed to assess your ability to apply your working knowledge of the course concepts and techniques on “real” datasets while effectively communicating the results of your        analysis with others. The timeline regarding the project spans most of the course. You will have       regular due dates for certain items/actions related to the course project.

There will be peer review assignments from time to time for the course project materials and final   project report. I will make the assignments in canvas using their system.  This course will be           conducted as a learning community. Please be open, honest, timely, constructive, and respectful in your responses to your fellow students.

More detailed information on the Assignments and Course Project can be found below.

Assignments:

Students are permitted to discuss homework assignments with currently enrolled students unless      otherwise specified by the professor. You are encouraged to form groups on canvas and make use of the Q&A message boards for this purpose. However, the student’s submitted work should represent  his/her own ideas. Students are not allowed to copy computer codes or answers from each other,      and must write their own codes and solutions or results. You are required to identify all collaborators  on your assignments. Identification still does not grant permission to copy. The Virginia Tech honor    code and the policies in the CMDA Statement on Academic Integrity will be strictly enforced.

Every homework assignment requires a formal write-up using R Markdown. If you do not use R Markdown, you will receive a zero.

Completed assignments should be submitted by the specified deadline to Canvas, clearly marked    with student’s name and assignment number, eg. Lucero_Christian_HW1.pdf and the corresponding computer code should be clearly labeled, annotated, and included in a .zip file when appropriate.

Homework will be assigned regularly (depending on the length of the assignment) and due by      11.59pm. A one-day late submission will carry a 20 point penalty. All submissions beyond that will receive a zero.

https://canvas.vt.edu/courses/152169/pages/course-syllabus?module_item_id=1932840                                                                                                          4/6

We will not accept excuses that you “finished the assignment, but forgot to turn in the assignment, here is my screenshot to prove it” and similar statements. Students have doctored such images in the past and reported for honor code violations.

Each HW will be worth 100 points. Solutions to homework assignments will not be provided.

The Course Project:

A course project will carry 50% of the grade. This project is intended to apply the skills you have        learned in the course on larger more open-ended topics. The project will take considerably more time than a regular homework assignment and students should not procrastinate.

In addition to students submitting a final project report and associated computer code, we will ask    you to submit status reports and other proofs of effort (together we’ll refer to these as milestones) so that we can check your progress before the final due date. These milestones will have specific         deadlines.

Projects will be evaluated by your instructor as well as your peers. You will be asked to read and      provide a peer review feedback for several other students on some of their milestones and their final project report. Please do a thorough job, while being respectful, as this activity also is being graded  (I’ll review your peer reviewed comments).

Other class policies:

  If you need course adaptations or accommodations because of disability or medical emergencies

notify the instructor.

Barring illness or other unforeseen emergencies, missed homework assignments and exams

cannot be made up and will receive a grade of zero. The only other exceptions to this policy are if you are involved in an official University activity (e.g., out-of-town competition) or event directly    related to your program (e.g., conference) that cannot be scheduled at another time. In those       instances, you must notify the instructor at least three weeks prior to the date that the missed       homework will be assigned or missed exam is scheduled, so that alternative arrangements can    be made.

  Any questions about homework grades should be referred first to the teaching assistant or grader

using the comment box on Canvas. If you still have questions about a grade, or cannot resolve an issue with the teaching assistant or grader, then you should email the instructor.

  The tenets of the Virginia Tech Honor Code will be strictly enforced in this course and all

assignments shall be subject to the stipulations of the Honor Code.

Honor Code: