DSA-201 Intro to Data Science and Analytics, Fall 2021

Meetings

M/W 4:00 PM - 5:15 PM

You may think of this course as being in a book club: We will have weekly readings to read or videos to watch (all posted on Canvas) and afterwards we get together to engage in active discussion of those readings/videos. Thus it is very important that every participant comes to the meetings already having read/watched the assigned readings/videos for that week.

Instructor

Carlos Paniagua, carlos.paniagua@salve.edu, O'hare 212, 401-847-6650. You can also reach me on Teams. Please use email for administrative issues (reporting missing class before it happens) and Microsoft Teams for all other quick consultations regarding the course. For more in-depth consultations about course content see Piazza and the following.

Open hours

These are times we can meet one-on-one. You can see me after class or by appointment.

Description

This course is a no-prerequisite introduction to the fundamental concepts, techniques, tools, and methods of data science and analytics. We will use the very popular R programming language for statistical analysis. From the course catalog: Topics include data collection and sampling in real-world problems, unstructured data, brief review of descriptive statistics and statistical plots, data transformations and missing data, visualization of multivariate data, clustering, univariate and multivariate regression, confirmatory data analysis.

Objectives

Upon completion of the course participants should be able to:

Understand fundamental concepts and characteristics of data.
Design, write, and debug basic and useful programs in R and R-Studio for data management, analysis, and visualization.
Understand principles and practices in data filtering, cleaning, and linking.
Communicate results of data analyses to decision makers or stakeholders
Adequately frame a data science problem: question formulation, data identification.
Perform adequate transformations to data, including linking, aggregation, and summarization.
Organize and manage data at various stages of project lifecycle

Student Profile

Although there are no formal prerequisites to participate in this course, the ideal student should have

A legitimate interest/curiosity in data science
Some curiosity about business, science, education, health, or another substantive area
Basic computer skills, particularly around spreadsheets
Familiarity with basic arithmetic, geometry, and trigonometry
Basic understanding of simple descriptive statistics
Motivation to learn and achieve a high degree of professional preparation

Materials

The main resource will be the textbook Introduction to Data Science (2017), by Saltz & Stanton (Required)

Other useful resources:

R for Data Science (2017) by Garrett Grolemund and Hadley Wickham, O’Reilly. Web version at http://r4ds.had.co.nz/
Modern Data Science with R (2021) by Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton, CRC Press. Web version at https://mdsr-book.github.io/mdsr2e/
Introduction to Data Science Data Analysis and Prediction Algorithms with R by Rafael A. Irizarry. Web version at https://rafalab.github.io/dsbook/
Introduction to Modern Statistics is a more classical approach to statistics using today's technology.
Hands-On Programming with R by Garrett Grolemund https://rstudio-education.github.io/hopr/ (for the basics of programming in R)
Advanced R by Hadley Wickham available at https://adv-r.hadley.nz/index.html
Sometimes we want to do something very specific but we are kind of stuck. Chances are somebody else has been in a similar situation already. In these cases you could try a search on the Q&A site StackOverflow: https://stackoverflow.com/questions/tagged/R. See also Cross Validated.

Evaluation

Course assessment will consist of homework, quizzes, participation, and projects. Standard letter-grade scale will apply. The breakdown is as follows.

Homework (40%, about 1 every week) for practicing carrying out data processing, analysis, management, and communication.
Projects (20 %, at least one). Problem solving/interesting applications based on the concepts and techniques covered. These are to be worked on your own development environment (more on this below) and turned in to Canvas.
Quizzes (30%, about 3) to assess mastery of course objectives.
Participation (10%, continuously throughout the semester). More on this below.

Expectations

Academic Integrity: All students are expected to accept and to abide by the values of honesty, integrity, and truthfulness in their academic pursuits. Sanctions for violations of academic honesty, such as plagiarism or cheating range from failure for the work involved to failure in the course. A record of violations and sanctions is maintained in the student's file. Any violation may result in dismissal from the University (Undergraduate Catalog). Academic honesty is taken seriously in this class. Students committing academic dishonesty will be subject to the consequences described in the Catalog as determined by the professor. In short, everything you turn in must be your own work!

Examples of what is not OK to do:
```
 -   To let someone else see/copy your own work

 -   To fail to acknowledge in writing help given/received either from a person or otherwise

 -   To share files with other students
```
Examples of what is OK (encouraged) to do:
```
 -   Discuss (talk about) general ideas with other students and with your instructor
```
Before live meetings: That participants will come to live meetings having thoroughly read the assigned readings/watched assigned videos.
During live meetings: I ask that participants will have their cameras on at all times and be actively engaged (for example, asking a lot of questions!) during discussions.

After live meetings: To be engaged in asynchronous discussions on Piazza, a Q&A platform within Canvas. I expect participants to make at least one contribution (question, answer to a question, something interesting or useful you discovered, etc.) every two weeks in order to be in good participation standing.

Tentative schedule

Week	Readings	Focus for the week
Week 1	Ch 1: About Data Ch 2: Data Problems Ch 3: Getting Started with R	Data Science Introduction
Week 2	Ch 4: Follow the Data Ch 5: Rows and Columns Ch 6: Data Munging	Working with Dataframes
Week 3	Ch 7: Onward with R-Studio Ch 8: What’s My Function Ch 9: Beer, Farms, & Peas	Descriptive Statistics and Functions
Week 4	Ch 10: Sample in a Jar	Sampling and Inferential Statistics
Week 5	Ch 11: Storage Wars	Connecting with External Data Sources (such as JSON)
Week 6	Ch 12: Pictures vs Numbers	Introduction to Visualization
Week 7	Ch 13: Map Mash-Up	Working with Map Data
Week 8	Ch 16: Line Up, Please	Linear Modeling
Week 9	Ch 17: Hi Ho, Hi Ho – Data Mining We Go	Association Rules Mining
Week 10	Ch 18: What’s Your Vector, Victor?	Support Vector Machines
Week 11	Ch 14: Word Perfect Ch 15: Happy Words?	Text Mining
Week 12	Ch 19: Shiny Apps	Interactive/Web R Applications
Week 13	Ch 20: Big Data? Big Deal!	Big Data

Disability Accommodations

Salve Regina University is committed to providing equal access for students with disabilities to all of its programs and services in accordance with the Americans with Disabilities Act (ADA) of 1990, and Section 504 of the Rehabilitation Act. If you have a disability that entitles you to instructional or other accommodations, you must register with the Office of Disability Services at the Academic Center for Excellence, and arrange to provide them with documentation of your disability. The Office is open Monday – Friday 8:00am – 4:00pm EST, and can be reached by phone (401-341-3150) or via email (laura.kcira@salve.edu). The Office of Disability Services will provide you with letters of accommodation for your professors as appropriate. You should arrange to speak with the professor as soon as possible (ideally within the first week of class) to discuss arrangements for implementing your accommodations.

Other Expectations (COVID)

In compliance with the University’s continuing covid plan, students must comply with the most recent masking requirements. Students are not permitted to consume any food or beverage during in person instruction. Students are required to sit in their assigned seats during class. Students should not reconfigure classroom furniture and will have the option to clean their seating areas upon arriving and departing the classroom; supplies will be provided in all classrooms. Faculty members will ask students to leave the classroom if they are unable to comply with these regulations.

This course has been designed to support students in their efforts to meet stated course outcomes. Course delivery may be altered by unexpected events or extenuating circumstances beyond the control of the professor; however, instruction will continue, and the learning goals will remain. If change in delivery or venue is required, faculty will provide protocols to students.

If a student learns that they are required to isolate/quarantine (due to a positive covid test or a close contact), they must immediately email their instructors to notify them of the duration of the isolation/quarantine and to request appropriate academic support so that the student can continue in the course. Instructors will reply with an outline of the supports to be provided.

If a student experiences significant disruption to their learning due to multiple periods of required isolation/quarantine, they should work with the instructor and the dean of undergraduate studies to come to a resolution.

Use of Salve Email

All official email communication at Salve Regina University involving faculty, students, and staff is to be conducted using Salve email (addresses ending in @salve.edu). Students must regularly check their Salve email for important notifications from their faculty, the Registrar, and others.

Academic Support

The Academic Center for Excellence (ACE) provides peer tutoring appointments that help students at all skill levels achieve academic success. Subject tutors review content and provide specific study strategies for courses in many disciplines. Writing tutors help any undergraduate student with every phase of the writing process from brainstorming to revision. Peer academic coaches help students reach academic goals by supporting students with time-management, note-taking, studying strategies and more. For more information or to schedule an appointment, visit https://salve.edu/academic-center-excellence (Links to an external site.) or call (401) 341-2226.

Credit Hour

Salve Regina University awards academic credit hours for the successful completion of this course, and the course requires a significant commitment of time and effort from the student. Accreditation regulation requires that students complete (1) one hour of classroom or direct faculty instruction and a minimum of two hours of out of class work each week for approximately fifteen weeks for one semester hour of credit, or the equivalent amount of work over a different amount of time; or (2) At least an equivalent amount of work for other learning activities such as laboratory work, internships, practica or studio work. The learning outcomes, assignments and workload for this course reflect this expectation.

Disclaimer

Changes to this syllabus may be made as the instructor deems necessary. The most up to date version will be available on the syllabus page on Canvas.