M1U: Course Syllabus

The syllabus for this course is available as a PDF through Canvas. However, it is also reproduced here for reference and discussion.

Overview

Course

LIS/ICT 661: Introduction to Data Science

Instructor Information

  • Name: Heng Zheng, PhD
  • Office location: Room 317, Lucille Little Library
  • UKY email address: hengzheng@uky.edu
  • Microsoft Teams: hengzheng@uky.edu - preferred method of contact
  • Office hours: Thursdays from 11:00 AM to 1:00 PM.
    • Meetings are on Microsoft Teams by default https://teams.microsoft.com/meet/233062564011475.
    • If the scheduled office hours do not work for you, please send me a Teams message or email so we can schedule a time that works with your schedule. Please remember to email me at least 24 hours in advance, so I can set up the virtual meeting and invite you to join it. More information for using Microsoft Teams can be found here.
    • If you would like to meet in person, my office is Room 317, Lucille Little Library. Please contact me at least one day in advance if you prefer an in-person meeting. In-person meetings may not be available without advance notice.
  • Timeframe for responses: I typically respond to student messages and emails within 24 hours on weekdays. Messages received on weekends or university holidays will be answered by the next business day.

Meeting Schedule

This course takes place asynchronously on Canvas

Required Materials

This course uses a free custom online textbook based on Creative Commons-licensed works such as Data Feminism, OpenStax’s Introductory Statistics, and the “ModernDive” Statistical Inference Via Data Science textbook.

This course textbook is adapted from materials developed by Dr. Spencer Greenhalgh. Some sections retain first-person examples and experiences from the original text; unless otherwise indicated, those references describe the original author. Additional source materials are identified within individual chapters.

Course Information

Course Description

This course will provide a foundation in the area of data science based on data curation and statistical analysis. The primary goal of this course is for students to learn data analysis concepts and techniques that facilitate making decisions from a rich data set. Students will investigate data concepts, metadata creation and interpretation, the general linear model, and basics of information visualization. At the beginning, this course will introduce fundamentals about data and data standards and methods for organizing, curating, and preserving data for reuse. Then, we will focus on the inferential statistics: drawing conclusions and making decisions from data. This course will help students understand how to use data analysis tools, and especially, provide an opportunity to utilize an open source data analysis tool, R, for data manipulation, analysis, and visualization. Finally, in this course we will discuss diverse issues around data including technologies, behaviors, organizations, policies, and society.

Course Objectives—“I Can Statements”

The following “I can” statements will guide all of the learning and assessment activities throughout this course. Although these objectives have some overlap, activities within each module will clearly and specifically relate to a single objective, and larger assessments will implicitly ask you to demonstrate all of them. As we proceed throughout the semester, you should feel increasingly comfortable making these statements about yourself:

  • I can express my understanding of philosophical, ethical, statistical, research, and other concepts underpinning data science.
  • I can apply that understanding—in conjunction with R programming—to completing practical projects.
  • I can connect conceptual and practical elements of data science to disciplinary and contextual knowledge.

Course Assessment

Letter Grade Scale:

  • 90.0 points – 100.0 points = A
  • 80.0 points – 89.9 points = B
  • 70.0 points – 79.9 points = C
  • 0.0 points – 69.9 points = E

These 100 points come from the following assessment activities, which should all be completed honestly and individually on Canvas:

Projects

Throughout the semester, you will complete four projects worth a total of 55 points:

  • Project #1: Finding and Evaluating Data (10 points)
  • Project #2: Exploring and Describing Data (10 points)
  • Project #3: Building and Evaluating Models (10 points)
  • Final Project: Reporting Data Analysis (25 points)

Detailed instructions for these projects can be found on Canvas.

Participation

Throughout the semester, you will earn 45 points from a series of participation activities. During each of the fifteen modules of the semester, you will complete three reading or participation activities (each worth one point) that will help you extend or apply your understanding of course content; while these activities vary from module to module, a plurality of modules involve reading and discussing a selection from the textbook, completing a programming walkthrough with provided data, and then adapting (some of) the code from the walkthrough to work with your own data.

Mechanics of Submissions

All assignments, discussions, and quizzes must be submitted through Canvas by the deadlines listed on Canvas. Students are responsible for ensuring that submissions are successfully uploaded and viewable. Late or incomplete submissions will be handled in accordance with the course’s late work policy. Additional submission details, including file formats and technical requirements, will be provided on the individual assignment pages.

Late Work Policy

Assignments should be submitted online via Canvas by the posted deadlines. Late submissions will be penalized as follows: - Work submitted within three (3) days after the deadline will receive a 20% deduction from the earned grade. - Work submitted more than three (3) days late will receive a zero (0) grade.

Students who wish to make up work more than three (3) days late must contact me via Microsoft Teams or email and provide a justification with supporting documentation. If approved, make-up work may receive no more than 70% of the available points. Requests must be made by the Final Project deadline, and all approved make-up work must be submitted by the Final Submission Deadline.

Excused absences recognized under University policy. will be handled in accordance with University policy.

Course Policies

All of the policies listed on this page are in effect for this course.

Academic Offenses

Please refer to the University’s official policy on academic offenses: academic offenses.

Plagiarism [source]

“All academic work, written or otherwise, submitted by students to their instructors or other academic supervisors, is expected to be the result of their own thought, research or self-expression. In cases where students feel unsure about a question of plagiarism involving their work, they are obliged to consult their instructors on the matter before submission. When students submit work purporting to be their own, but which in any way borrows ideas, organization, wording or content from another source without appropriate acknowledgment of the fact, the students are guilty of plagiarism.

“Plagiarism includes reproducing someone else’s work (including, but not limited to a published article, a book, a website, computer code or a paper from a friend) without clear attribution. Plagiarism also includes the practice of employing or allowing another person to alter or revise the work which a student submits as their own, whoever that other person may be, except under specific circumstances (e.g. Writing Center review, peer review) allowed by the Instructor of Record or that person’s designee. Plagiarism may also include double submission, self-plagiarism or unauthorized resubmission of one’s own work, as defined by the instructor.

“Students may discuss assignments among themselves or with an instructor or tutor, except where prohibited by the Instructor of Record (e.g. individual take-home exams). However, the actual work must be done by the student, and the student alone, unless collaboration is allowed by the Instructor of Record (e.g. group projects). When a student’s assignment involves research in outside sources or information, the student must carefully acknowledge exactly what, where and how they have employed them. If the words of someone else are used, the student must put quotation marks around the passage in question and add an appropriate indication of its origin. Making simple changes while leaving the organization, content and phraseology intact is plagiaristic. However, nothing in this AR shall apply to those ideas which are so generally and freely circulated as to be a part of the public domain.”

Cheating [source]

“Cheating is defined by its general usage. It includes, but is not limited to, the wrongfully giving, taking or presenting any information or material by a student with the intent of aiding themself or another on any academic work which is considered in any way in the determination of the final grade.

“The fact that a student could not have benefited from an action is not by itself proof that the action does not constitute cheating.”

Code, Plagiarism, and AI Policies

It is common practice in data science and programming communities to borrow code from other, more knowledgeable programmers. Indeed, many of the weekly activities in this class will explicitly involve copying or adapting code from our textbook. While I prefer that you draw from the textbook when borrowing code from other sources, you might also find online or other sources helpful for figuring out how to complete a specific task for your class projects. When done properly, this is not plagiarism or cheating—in fact, it is good practice in data science.

Nonetheless, you are ultimately responsible for completing assessments, and plagiarism and cheating remain a serious concern for this course. If you consult other sources, please ensure that they support (rather than replace) your personal work, effort, initiative, and understanding. It is your responsibility to ensure that you understand what plagiarism is and how to avoid it; when in doubt, reach out to me with your questions.

Generative AI tools may be used as limited support for learning in this course. You should write your own code and make a reasonable independent effort to resolve problems before using AI. AI may be used to explain error messages, review code that you have written, help with troubleshooting, and provide feedback. It may not be used to generate a complete solution, analysis workflow, or written response, reflection, or interpretation for submission.

If you use generative AI, you must disclose the tool used and how it supported your work. You are responsible for verifying the accuracy of any AI-generated information, code, or suggestions, and for being able to explain and reproduce everything you submit.

Do not present AI-generated output as your own work. Do not use AI to fabricate information, data, sources, or references. Do not enter private, sensitive, restricted, proprietary, or otherwise unauthorized data into external AI tools.

Documentation for Excused Absences

Documentation for excused absences must meet the requirements of the University Excused Absence Policy. Additional documentation or information may be requested when permitted by University policy.

Attendance Policy

This is an asynchronous online course, which requires participation via Canvas to facilitate a sense of community. I will also use Canvas to make course announcements, distribute reading materials, create assignments, and post grades. Please check Canvas. regularly and check the course homepage for announcements and assignment deadlines.

Religious Observance and Excused Absences

In accordance with University of Kentucky policy, absences due to major religious observances may be considered excused. Students are responsible for requesting an excused absence in advance of the absence, as outlined in the University’s official policy on religious observance.

Please refer to the University of Kentucky Ombud’s guidance on absences for religious observance: https://ombud.uky.edu/students/academic-policies/absence-religious-observance

Course Schedule

Module 1: Course Introduction (Week 1)

  • read and discuss the course syllabus
  • complete “Install R and RStudio” walkthrough
  • introduce yourself to the class

Module 2: Data Science (Week 2)

  • read and discuss “The New(?) and Shiny(?) Science of Data”
  • complete “Getting Started with Data in R” walkthrough
  • complete “Set up GitHub” walkthrough

Module 3: Reproducibility and Paradigms (Week 3)

  • read and discuss “Research Paradigms and Reproducibility”
  • complete “Using Projects and Scripts in R” walkthrough
  • complete “Writing in R Markdown” walkthrough

Module 4: Data Sharing (Week 4)

  • read and discuss “The Value of Open Data”
  • complete “Find a Dataset Relevant to You” walkthrough
  • read and discuss “Show Your Work”
  • submit Project 1: Finding and Evaluating Data

Module 5: Theory and Ethics (Week 5)

  • read and discuss “Numbers Don’t Speak for Themselves”
  • read and discuss “Are Ethics Enough in Data Science”
  • reflect on theoretical and philosophical constraints in context

Module 6: Data Cleaning (Week 6)

  • read and discuss “Unicorns, Janitors, and Rock Stars”
  • complete “Wrangling and Tidying Data” walkthrough
  • practice wrangling and tidying your own data

Module 7: Data Visualization (Week 7)

  • read and discuss “Subjectivity in Data Visualization”
  • complete “Data Visualization” walkthrough
  • practice visualizing your own data

Module 8: Descriptive Statistics (Week 8)

  • read and discuss “Statistics and Scientific Racism”
  • complete “Descriptive Statistics” walkthrough
  • calculate descriptive statistics for your own data
  • submit Project 2: Exploring and Describing Data

Module 9: Linear Regression (Week 9)

  • read and discuss “Linear Regression”
  • complete “Basic Regression” walkthrough
  • perform a basic regression with your own data

Module 10: Multiple Regression (Week 10)

  • read and discuss “Consequences of Failed Predictions”
  • complete “Multiple Regression” walkthrough
  • perform a multiple regression with your own data

Module 11: Statistical Sampling (Week 11)

  • read and discuss “Samples and Populations”
  • complete “Sampling” walkthrough
  • explore sampling with your own data

Module 12: Confidence Intervals (Week 12)

  • read and discuss “Confident About What?”
  • complete “Confidence Intervals” walkthrough
  • explore confidence intervals with your own data

Module 13: Hypothesis Testing (Week 13)

  • read and discuss “The Dangers of False Positives”
  • complete “Hypothesis Testing” walkthrough
  • explore hypothesis testing with your own data

Module 14: Inferential Regression (Week 14)

  • read and discuss “Small Stories vs. Big Data”
  • complete “Inferential Regression” walkthrough
  • perform an inferential regression with your own data
  • submit Project 3: Building and Evaluating Models

Module 15: Course Reflection (Week 15)

  • reflect on your understanding of data science
  • reflect on your application of data science
  • reflect on your connection with data science

Module 16: Final Project (Week 16)

  • submit Final Project: Reporting Data Analysis

Other Information

University Policies and Student Support Resources

Any student who has difficulty affording or accessing food to eat every day or who lacks a safe and stable place to live and believes this may affect their performance in the course is urged to contact the Center for Support and Intervention. Furthermore, if you are comfortable doing so, you can also notify me.

Classroom Emergency Preparedness and Response Information

Your safety is our top priority. The UK Police Department works to enhance campus security, but we all share responsibility for keeping our community safe. In an emergency during class, follow your instructor’s directions based on university, building and department plans.

If there is an emergency, DIAL 911. To report suspicious activity or non-emergency situations, call the UK Police Department at 859-257-8573 or #UKPD from any mobile phone.

UK Alert

The university provides emergency notifications through UK Alert, which sends messages via email, text message, phone calls, building alarm systems, digital signage, social media, outdoor sirens and via push notifications through the SafeZone App. If you receive a UK Alert message during class, notify your instructor and classmates immediately. For more information, visit the UK Alert page.

Emergency Actions:

Evacuation

Leave the building immediately using the nearest safe exit. Do not use elevators. Follow posted evacuation routes and go to the designated assembly area if available. Stay clear of the building and wait for further instructions from emergency personnel.

Severe Weather Sheltering

Report to the nearest marked shelter location. If a marked shelter location is unavailable, move to an interior room or hallway on the lowest floor. Avoid doors and windows. Take cover under a sturdy table and protect your head and neck.

Shelter-in-Place

Stay where you are unless your building is affected. If it is, evacuate only if the fire alarm sounds or law enforcement instructs you. Choose an interior room with few or no windows and no exterior doors if possible. Close all windows and doors.

Active Aggressor

  • Run – Escape if you can.
  • Hide – If escape isn’t possible, hide in a secure place. Silence your phone and dim the screen.
  • Fight – As a last resort, defend yourself by any means necessary.

Distance Learning Courses

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