Download this page as a jupyter notebook at ENGR-1330-2022-1-Syllabus The syllabus changes from time-to-time The webpage may become obsolete; the jupyter notebook is always current.

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ENGR 1330 Computational Thinking with Data Science¶

Course Description:¶

Introducion to Python programming, its relevant modules and libraries, and computational thinking for solving problems in Data Science. Data science approaches for importing, manipulating, and analyzing data. Modeling and visualizing real-world data sets in various science and engineering disciplines.

3 credit hours comprising of lectures and hands-on lab sessions.

This course provides a hands-on learning experience in programming and data science using iPython and JupyterLab. iPython is the interactive python kernel implemented in JupyterLab.

Prerequisites:¶

Prior programming background is NOT required. The course is intended for first-year WCOE students (aka engineering foundational)

COVID-19 Important Guidelines:¶

If Texas Tech University campus operations are required to change because of health concerns related to the COVID-19 pandemic, it is possible that this course will move to a fully online delivery format. Should that be necessary, students will be advised of technical and/or equipment requirements, including remote proctoring software.
Policy on absences resulting from illness: We anticipate that some students may have extended absences. To avoid students feeling compelled to attend in-person class periods when having symptoms or feeling unwell, a standard policy is provided that holds students harmless for illness-related absences (see Section A below).

A. Illness-Based Absence Policy (Face-to-Face Classes)¶

If at any time during the semester you are ill, in the interest of your own health and safety as well as the health and safety of your instructors and classmates, you are encouraged not to attend face-to-face class meetings or events. Please review the steps outlined below that you should follow to ensure your absence for illness will be excused. These steps also apply to not participating in synchronous online class meetings if you feel too ill to do so and missing specified assignment due dates in asynchronous online classes because of illness.

If you are ill and think the symptoms might be COVID-19-related:
1. Call Student Health Services at 806.743.2848 or your health care provider. During after-hours and on weekends, contact TTU COVID-19 Helpline at TBD.
2. Self-report as soon as possible using the Dean of Students COVID-19 webpage. This website has specific directions about how to upload documentation from a medical provider and what will happen if your illness renders you unable to participate in classes for more than one week.
3. If your illness is determined to be COVID-19-related, all remaining documentation and communication will be handled through the Office of the Dean of Students, including notification of your instructors of the time you may be absent from and may return to classes.
4. If your illness is determined not to be COVID-19-related, please follow steps 2.a-d below.

If you are ill and can attribute your symptoms to something other than COVID-19:
1. If your illness renders you unable to attend face-to-face classes, participate in synchronous online classes, or miss specified assignment due dates in asynchronous online classes, you are encouraged to contact either Student Health Services at 806.743.2848 or your health care provider. Note that Student Health Services and your own and other health care providers may arrange virtual visits.
2. During the health provider visit, request a “return to school” note.
3. E-mail the instructor a picture of that note.
4. Return to class by the next class period after the date indicated on your note.

Following the steps outlined above helps to keep your instructors informed about your absences and ensures your absence or missing an assignment due date because of illness will be marked excused. You will still be responsible to complete within a week of returning to class any assignments, quizzes, or exams you miss because of illness.

B. Illness-Based Absence Policy (Telepresence/On-Line Classes)¶

Same as above with respect potential to infect others; go to a health care provider if you are ill. Telepresence courses are recorded and will be available on TTU MediaSite and/or YouTube (unlisted). Exercises, Quizzes, and Examinations are all administered by a Learning Management System (Blackboard) and students need to allow enough time to complete and upload their work. Due date adjustments/late submits on case-by-case basis; documentation required as in subsection A above.

Update #1: Provost 13 Aug 2021 Pandemic Guidance

Update #2: Provost 27 Aug 2021 Pandemic Guidance

Course Sections¶

Lesson time, days, and location:

Section 001

Lecture Section 001; CRN 63311; 0800-0850 M,W,F ; Modality: Face-to-Face ; Location: IMSE 116
Laboratory Section D52; CRN 64441; 0900-0950 M,W,F; Modality: Telepresence (on-Line) Location: Internet

Section 013

Lecture Section 013; CRN 64880; 1600-1650 M,W,F ; Modality: Face-to-Face ; Location: EE 217
Laboratory Section D67; CRN 64441; 1700-1750 M,W,F; Modality: Telepresence (on-Line) Location: Internet

Course Instructor:¶

Instructor: Theodore G. Cleveland, Ph.D., P.E., M. ASCE, F. EWRI

Email: theodore.cleveland@ttu.edu (put ENGR 1330 in subject line for email related to this class)

Office location: CE 203F

Office hours: 1300-1400 M-F by Zoom appointment

Teaching Assistant:¶

Teaching Assistant: Josh Archer

Email : josh.archer@ttu.edu (put ENGR 1330 in subject line for email related to this class)

Office location: (Zoom)

Office hours: TBD or by Zoom appointment

Textbook:¶

Ani Adhikari and John DeNero, Computational and Inferential Thinking, The Foundations of Data Science, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). Link: https://www.inferentialthinking.com/chapters/intro

Theodore G. Cleveland, Farhang Forghanparast, Dinesh Sundaravadivelu Devarajan, Turgut Batuhan Baturalp (Batu), Tanja Karp, Long Nguyen, and Mona Rizvi. (2021) Computational Thinking and Data Science: A WebBook to Accompany ENGR 1330 at TTU, Whitacre College of Engineering, DOI (pending)

Course Contents:¶

Computational thinking for problem-solving: Logical problem solving, decomposition, pattern recognition, abstraction, representation, algorithm design, and generalization.
Python Programming:
1. Variables, constants, data types, data structures, strings, math operators
2. boolean operators, expressions, program constructs, functions,
3. looping, I/O files, modules, and database.
Data science fundamentals:
1. Experimental setup:
  1. Importing and formatting data sets,
  2. Displaying data,
  3. Data pre-processing.
2. Introductory statistical analysis with Python:
  1. Elementary statistics, randomness, sampling, probability distributions,
  2. Confidence intervals, hypothesis testing, and A/B testing.
3. Basic data analysis, visualization, and machine learning:
  1. Data pre-processing,
  2. Supervised/unsupervised learning,
  3. Performance evaluation metrics.

Learning Outcomes:¶

On completion of the course, students will have

Created Python programs employing computational thinking concepts to
Employed Python libraries relevant to data science.
Downloaded data files from selected public sources and analyzed content.
Created scripts to perform fundamental data analytics and basic visualization.

ABET Student Outcomes¶

Engineering:
1. An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics.
2. An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.

Computer Science:
1. Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions.
2. Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.

Resources/Tools¶

Platforms for Python Programming (for your own computers)¶

Anaconda platform https://www.anaconda.com/: Anaconda distribution is an open-source Data Science Distribution Development Platform. It includes Python 3 with over 1,500 data science packages making it easy to manage libraries and dependencies. Available in Linux, Windows, and Mac OS X.
Jupyter https://jupyter.org/: JupyterLab is a web-based interactive development environment for Jupyter notebooks, code, and data. JupyterLab is flexible: Configure and arrange the user interface to support a wide range of workflows in data science, scientific computing, and machine learning. note Anaconda for MacOS includes a JupyterLab instance, so a separate install is not required.

Additional Modules for Python Programming¶

Math module https://docs.python.org/3/library/math.html: Gives access to the mathematical functions defined by the C standard e.g. factorial, gcd, exponential, logarithm.
Operator module https://docs.python.org/3/library/operator.html: Helps in exporting a set of efficient functions corresponding to the intrinsic operators of Python. For example, the operator add(x,y) is equivalent to the expression x+y.

Python Modules for Data Science¶

Scipy module https://www.scipy.org/: A Python-based ecosystem of open-source software for mathematics, science, and engineering. Some of the core packages are:
- Numpy: Provides n-dimensional array package
- Scipy: Fundamental for scientific computing (e.g. linear algorithm, optimization)
- Matplotlib: Visualizations/2D plotting
- IPython: Enhanced interactive console <<= this is the kernel used in JupyterLab
- Pandas: Data structures and data analysis
Scikit-learn module https://scikit-learn.org/stable/: A library for machine learning in Python. It is a simple and efficient tool for predictive data analysis. It is built on NumPy, SciPy, and matplotlib modules.

On-Line Options¶

AWS Lightsail Instance (use Windows Server 2000 template; lowest resource provision tier; AWS RDP client, or download and install own RDP client)
1. Then install Anaconda onto the AWS Instance

Hardware Requirements¶

Minimal, in fact this syllabus was created using a JupyterLab notebook (as a markdown processor) on a Raspberry Pi 4B, which technically cannot support a JupyterHub, but does.
Your current laptop should be fine, or if you only have a chromebook, build an AWS instance.

The college of engineering has specific laptop requirements for your other courses that are listed at https://www.depts.ttu.edu/coe/dean/engineeringitservices/buyingtherightcomputer.php

Content Server¶

Blackboard is used as the learning management system (LMS) for this class, and it uses web links to a content server at http://54.243.252.9/engr-1330-webroot/ The Blackboard links will generally go directly to a section in the webroot, as do the internal links in the schedule, but feel free to explore by going in the front door!

Course Schedule¶

Week 1 Introduction¶

(200 cumulative minutes)

date	lesson topics	links	laboratory topics
12Jan22	Introduction - Syllabus - Computational Thinking Principles - JupyterLab Environment	Lesson 0 Lab 0 Exercise Set 0	Setting up a computational environment - Anaconda (PC/Mac) - Tour of Anaconda - Anaconda on AWS - Installing packages (conda/pip)
14Jan22	Data Science and Problem Solving - Data Science Principles - 6-step protocol - Attribution via CCMR	Lesson 1 Lab 1 Exercise Set 1	iPython/JupyterLab fundamentals - Code cells - Markdown cells - Exporting to a pdf

Week 2 Programming Fundamentals¶

(600 cumulative minutes)

date	lesson	links	laboratory
17Jan22	MLK Holiday
19Jan22	Expressions - fundamental operators - arithmetic expressions - simple output: print()	Lesson 2 Lab 2 Exercise Set 2	iPython/JupyterLab arithmetic - Expressions - Simple Output
21Jan22	Data Types and Typecasting - integer, float, string, boolean - String functions and operations - How to Build a Notebook (problem solving)	Lesson 3 Lab 3 Exercise Set 3	iPython/JupyterLab Data Types - int(),str(),float(),bool() - typecasting - string position indices

Week 3 Programming Fundamentals¶

(900 cumulative minutes)

date	lesson	links	laboratory
24Jan22	User interaction - input() - triple quotes - string manipulation - special characters	Lesson 4 Lab 4 Exercise Set 4	iPython/JupyterLab I/O exercises - input() - type casting - output formats - concatenation to build output strings
26Jan22	Data Structures - lists, arrays, tuples, sets, dictionaries - Names, position, index, contents - position keys	Lesson 4.1 Lab 4.1 Exercise Set 4.1	iPython/JupyterLab Lists - Lists - dictionaries - using keys - position indices
28Jan22	Flow Control Structures - Sequence - Selection - Repetition - Loop structures - Flowcharts; Psuedocode	Lesson 5 Lab 5 Exercise Set 5	iPython/JupyterLab Sequence, Selection, and Loops - FOR loops - range() function - WHILE loops - Error trapping try ... except structure

Week 4 Programming Fundamentals¶

(1920 cumulative minutes)

date	lesson	links	laboratory
31Jan22	Functions - Intrinsic (core) - Common external - User written - Saving user written as a module	Lesson 6 Lab 6 Exercise Set 6	iPython/JupyterLab Functions - import packages - aliasing - user defined - save as a file for later import
2Feb22	Data Files - open/read/close - open/write/close - create/append/delete	Lesson 7 Lab 7 Exercise Set 7	iPython/JupyterLab file manipulation - open/close/create/delete - reading into lists - writing output - reading a file from a website (unencrypted http:)
4Feb22	Snow Day

Week 5 Matrices and NUMPY¶

(2400 cumulative minutes)

date	lesson	links	laboratory
7Feb22	Exam 1 - In class 25 multiple choice questions	none	- In lab programming problem(s)
9Feb22	Vectors and Matrices (as lists) - Vector/Matrix Addition/Multipication - Matrix Inversion (Concept of multiplicative inverse) - Systems of Linear Equations - Gaussian Reduction (Optional)	Lesson 8 Lab 8 Exercise Set 8	iPython/JupyterLab file manipulation - open/close/create/delete - reading into lists - writing output
11Feb22	Data Files (continued) - reading from a website	Lesson 7.1 Lab 7.1 Exercise Set 7.1	iPython/JupyterLab file manipulation - open/close/create/delete - reading into lists - writing output - reading a file from a website (unencrypted http:)

Week 6 Databases using PANDAS¶

(2880 cumulative minutes)

date	lesson	links	laboratory
14Feb22	Vectors and Matrices (using NUMPY) - Vector/Matrix Addition/Multipication - Matrix Inversion (Concept of multiplicative inverse) - Solving Linear Systems of Equations	Lesson 9 Lab 9 Exercise Set 9	iPython/JupyterLab file manipulation - open/close/create/delete - reading into lists - writing output
16Feb22	Database Concepts - Records - Fields - Relational model - Unique record identifier/Primary Key	Lesson 11 Lab 11 Exercise Set 11	Flat-File Database - Searching a list - Sorting a list (without sort method)
18Feb22	Databases using PANDAS - Create tables – Examine/extract contents - Functions on elements	Lesson 12 Lab 12 Exercise Set 12	PANDAS Data frames: - Create, index, summarize statistics - fill and drop values - read/write to file

Week 7 Visual Display of Data using MATPLOTLIB¶

(3660 cumulative minutes)

date	lesson	links	laboratory
21Feb22	Plotting - Plot types - Plot uses - Graphing conventions	Lesson 14 Lab 14 Exercise Set 14	Plotting Exercises - Graphing Conventions - Intreperting Graphs
23Feb22	MATPLOTLIB package - line plots - scattergrams - decorators	Lesson 15 Lab 15 Exercise Set 15	MATPLOTLIB Exercises - Graphing Conventions - Data Display for line charts, bar charts, - box plot, scatter plot, and histograms
25Feb22	Exploratory Data Analysis - Data model as a prediction engine - Assessing model quality by plotting - Extrapolation	Lesson 16 Lab 16 Exercise Set 16	Predictions from Data - Lagrange Polynomial Interpolation - Arbitary functions

Week 8 Data Description¶

(3840 cumulative minutes)

date	lesson	links	laboratory
28Feb22	Descriptive Statistics - Mean, median, mode (measures of central tendency) - Variance, Standard deviation (measures of dispersion) - Skew (measures of symmetry) - Higher moments (pointyness ...)	Lesson 17 Lab 17 Exercise Set 17	iPython Descriptive Statistics - Using SCIPY.STATS - Using PANDAS - Visualizing Results
2Mar22	Pandas, Numpy, Plotting review
4Mar22	Exam 2 - In class 25 multiple choice questions	none	- In lab programming problem(s)

Week 9¶

(4320 cumulative minutes)

date	lesson	links	laboratory
7Mar22	Causality, Correlation, Randomness, and Probability -Causailty - Correlation - Probability rules - Conditional probabilities	Lesson 18 Lab 18 Exercise Set 18	Classical Gambling Simulations - Dice - Russian Roulette - Monte Hall Simulation
9Mar22	Simulation - Simulating random processes - Distributions	Lesson 19 Lab 19 (none) Exercise Set 19 (none)
11Mar22	Interval Estimates by Simulation - Bootstrap - Prediction intervals - Parameter intervals	Lesson 20 Lab 20 (none) Exercise Set 20 (none)	Interval Estimates Exercises - Confidence interval concept - Bootstrap simulations

Week 10 Decisions and Hypothesis Tests¶

(4800 cumulative minutes)

date	lesson	links	laboratory
21Mar22	Hypothesis Tests - Comparing two (or more) collections of observations - Histograms as tools	Lesson 21 Lab 21 Exercise Set 21	Functions and Observations on same plots - Visual interpretation of results - Judging the model
23Mar22	Hypothesis Tests - Comparing two (or more) treatments aka A/B Testing - Parametric and Non-Parametric Tests - Type1 & Type2 errors	Lesson 22 Lab 22 Exercise Set 22-HW	Hypothesis testing exercises - Comparing proportions - Type1 & Type2 errors - Attained significance (p-value)
25Mar22	Hypothesis Tests - Attained significance (p-value)	Lesson 23 Lab 23 Exercise Set-23	Hypothesis testing exercises - Comparing proportions - Type1 & Type2 errors - Attained significance (p-value)

Week 11 Data Models¶

(5280 cumulative minutes)

date	lesson	links	laboratory
28Mar22	Ordinary Functions as Data Models-I (Predictor-Response Models) - Line (affine functions) - Polynomials - Periodic	Lesson 24 Lab 24 Exercise Set 24	Functions and Observations on same plots - Visual interpretation of results - Judging the model
30Mar22	Distribution Functions as Data Models-II (Magnitude-Probability Models) – Order statistics (plotting positions) - Normal Distribution Function - Gamma Distribution Function - Extreme Value Distribution Function	Lesson 25 Lab 25 Exercise Set 25	Functions and Observations on same plots - Visual interpretation of results - Judging the model
1Apr22	Exam 3 - In class 25 multiple choice questions	none	- In lab programming problem(s)

Week 12 Simple Linear Regression¶

(5760 cumulative minutes)

date	lesson	links	laboratory
4Apr22	Linear regression - Why regression belongs to both statistics and machine learning - Representation and learning algorithms used to create a linear regression model - How to prepare data for linear regression	Lesson 26 Lab 26 Exercise Set 26	Applications of OLS - linear
6Apr22	Linear regression - Ordinary Least Squares - numpy.linalg.lstsq - stats package	Lesson 27 Lab 27 Exercise Set 27	Applications of OLS - linear
8Apr22	Linear regression - Prediction interval estimates	Lesson 28 Lab 28 Exercise Set 28	Applications of OLS - linear - polynomial

Week 13 Multiple Linear Regression¶

(6240 cumulative minutes)

date	lesson	links	laboratory
11Apr22	Multiple Least Squares - A Design matrix	Lesson 29 Lab 29 Exercise Set 29	- Multiple predictors - Polynomial fits of single predictors
13Apr22	Multiple Least Squares - A Design matrix	Lesson 30 Lab 30 Exercise Set 30	- Logarithmic fits - Power-Law Fits
15Apr22	Multiple Least Squares - A Design matrix	Lesson 31 Lab 31 Exercise Set 31	- Confounding predictors

Week 14 Logistic Regression and Classification¶

(6720 cumulative minutes)

date	lesson	links	laboratory
18Apr22	Easter holiday	none	none
20Apr22	Classification - Classification vs Prediction - Logistic Regression	Lesson 32 Lab 32 Exercise Set 32	Logistic Regression Applications - Odor in a Bayou - Solids in Construction Site Runoff
22Apr22	Classification - Classification vs Prediction - Logistic Regression	Lesson 33 Lab 33 Exercise Set 33	Logistic Regression Applications - Odor in a Bayou - Solids in Construction Site Runoff

Week 15 Nearest Neighbor Classification¶

date	lesson	links	laboratory
25Apr22	K Nearest Neighbor (KNN) Classification - Concept of distance - Training (a model fitting analog)	Lesson 34 Lab 34 Exercise Set 34	- Quality Classification - Bank Note Fraud Detection
27Apr22	Exam 4 - In class 25 multiple choice questions	none	- In lab programming problem(s)
29Apr22	KNN Classification - Confusion matrix, precision, recall, accuracy, F-score - Making decisions	Lesson 35 Lab 35 (none) Exercise Set 35 (none)	semester project workshop

Week 16 Wrap-Up¶

date	lesson	links	laboratory
2May22	Artifical Neural Networks (Demonstration)	Lesson 36 Lab 36 (none) Exercise Set 36 (none)	semester project workshop

Course Assessment and Grading Criteria:¶

There will be three exams and one comprehensive final project for this course.

In addition, lab notebooks, quizzes, and homework assignments also contribute to the final grade.
Late assignments will not be scored.

Grades will be based on the following components; weighting is approximate:

Assessment Instrument	Weight(%)
Exam-1	10
Exam-2	10
Exam-3	10
Exam-4	10
Lab Notebooks & Homework	30
Quizzes	15
Final project	15
Overall total	100

Letter grades will be assigned using the following proportions:

Normalized Score Range	Letter Grade
≥ 90	A
80-89	B
70-79	C
55-69	D
< 55	F

Classroom Policy:¶

The following activities are not allowed in the classroom: Texting or talking on the cellphone or other electronic devices, and reading non-course related materials.

Telepresence (On-line) Laboratory/Courses¶

Obviously electronic devices are vital; disrupting the conference is prohibited, please mute your microphone unless you have a question - consider typing your question into the chat window as well. Be aware of bandwidth issues and remember most lessons and laboratory sessions are recorded and posted on youtube. Recording, editing, and rendering takes awhile, so expect 24-36 hour delay before video is available. Sometimes video capture fails and there will be missing audio and/or missing video.

ADA Statement:¶

Any student who, because of a disability, may require special arrangements in order to meet the course requirements should contact the instructor as soon as possible to make necessary arrangements. Students must present appropriate verification from Student Disability Services during the instructor's office hours. Please note that instructors are not allowed to provide classroom accommodation to a student until appropriate verification from Student Disability Services has been provided. For additional information, please contact Student Disability Services office in 335 West Hall or call 806.742.2405.

Academic Integrity Statement:¶

Academic integrity is taking responsibility for one’s own class and/or course work, being individually accountable, and demonstrating intellectual honesty and ethical behavior. Academic integrity is a personal choice to abide by the standards of intellectual honesty and responsibility. Because education is a shared effort to achieve learning through the exchange of ideas, students, faculty, and staff have the collective responsibility to build mutual trust and respect. Ethical behavior and independent thought are essential for the highest level of academic achievement, which then must be measured. Academic achievement includes scholarship, teaching, and learning, all of which are shared endeavors. Grades are a device used to quantify the successful accumulation of knowledge through learning. Adhering to the standards of academic integrity ensures grades are earned honestly. Academic integrity is the foundation upon which students, faculty, and staff build their educational and professional careers. [Texas Tech University (“University”) Quality Enhancement Plan, Academic Integrity Task Force, 2010].

Religious Holy Day Statement:¶

“Religious holy day” means a holy day observed by a religion whose places of worship are exempt from property taxation under Texas Tax Code §11.20. A student who intends to observe a religious holy day should make that intention known to the instructor prior to the absence. A student who is absent from classes for the observance of a religious holy day shall be allowed to take an examination or complete an assignment scheduled for that day within a reasonable time after the absence. A student who is excused may not be penalized for the absence; however, the instructor may respond appropriately if the student fails to complete the assignment satisfactorily.

DISCRIMINATION, HARASSMENT, AND SEXUAL VIOLENCE STATEMENT: Texas Tech University is committed to providing and strengthening an educational, working, and living environment where students, faculty, staff, and visitors are free from gender and/or sex discrimination of any kind. Sexual assault, discrimination, harassment, and other Title IX violations are not tolerated by the University. Report any incidents to the Office for Student Rights & Resolution, (806)-742-SAFE (7233) or file a report online at titleix.ttu.edu/students. Faculty and staff members at TTU are committed to connecting you to resources on campus. Some of these available resources are: TTU Student Counseling Center, 806- 742-3674, https://www.depts.ttu.edu/scc/(Provides confidential support on campus.) TTU 24-hour Crisis Helpline, 806-742-5555, (Assists students who are experiencing a mental health or interpersonal violence crisis. If you call the helpline, you will speak with a mental health counselor.) Voice of Hope Lubbock Rape Crisis Center, 806-763-7273, voiceofhopelubbock.org (24-hour hotline that provides support for survivors of sexual violence.) The Risk, Intervention, Safety and Education (RISE) Office, 806-742-2110, https://www.depts.ttu.edu/rise/ (Provides a range of resources and support options focused on prevention education and student wellness.) Texas Tech Police Department, 806-742- 3931,http://www.depts.ttu.edu/ttpd/ (To report criminal activity that occurs on or near Texas Tech campus.)

CIVILITY IN THE CLASSROOM STATEMENT: Texas Tech University is a community of faculty, students, and staff that enjoys an expectation of cooperation, professionalism, and civility during the conduct of all forms of university business, including the conduct of student–student and student–faculty interactions in and out of the classroom. Further, the classroom is a setting in which an exchange of ideas and creative thinking should be encouraged and where intellectual growth and development are fostered. Students who disrupt this classroom mission by rude, sarcastic, threatening, abusive or obscene language and/or behavior will be subject to appropriate sanctions according to university policy. Likewise, faculty members are expected to maintain the highest standards of professionalism in all interactions with all constituents of the university. To ensure that you are fully engaged in class discussions and account team meetings during class time, you are expected to do the following:

Maintain the same level of civility and professionalism that would be expected in a face-to-face classroom setting.
Attend all classes regularly.
Log into the video conference on time and remain logged in for the duration of the class period.
Activate your camera so that you are visible to the instructor and other students in the class. If you have concerns about leaving your camera on (such as childcare obligations, privacy issues, or a particular circumstance during a class period), please talk to the instructor.
Refrain from engaging in non-class related activities during class time that create a distraction for other students in the class and/or limit your ability to engage in the course. Failure to meet these expectations may result in the following consequences:

Being counted as absent for the class meeting.
Not receiving credit for class participation for that class period.
Other consequences as stipulated in the syllabus, Texas Tech Code of Student Conduct, or other university policy. Repeated failure to meet expectations (e.g., attendance, participation in class, etc.), in addition to the above consequences, may result in the one or more of the following consequences:
Referral to the appropriate Associate Dean.
Academic penalty, ranging from a warning to failure of the course. (www.depts.ttu.edu/ethics/matadorchallenge/ethicalprinciples.php).

LGBTQIA SUPPORT STATEMENT: I identify as an ally to the lesbian, gay, bisexual, transgender, queer, intersex, and asexual (LGBTQIA) community, and I am available to listen and support you in an affirming manner. I can assist in connecting you with resources on campus to address problems you may face pertaining to sexual orientation and/or gender identity that could interfere with your success at Texas Tech. Please note that additional resources are available through the Office of LGBTQIA within the Center for Campus Life, Student Union Building Room 201, www.lgbtqia.ttu.edu, 806.742.5433.”

Office of LGBTQIA, Student Union Building Room 201, www.lgbtqia.ttu.edu, 806.742.5433 Within the Center for Campus Life, the Office serves the Texas Tech community through facilitation and leadership of programming and advocacy efforts. This work is aimed at strengthening the lesbian, gay, bisexual, transgender, queer, intersex, and asexual (LGBTQIA) community and sustaining an inclusive campus that welcomes people of all sexual orientations, gender identities, and gender expressions.

Ethical Conduct Addendum:¶

Cheating is prohibited, and the representation of the work of another person as your own will be reported to Office of Student Services for further investigation and sanctions as appropriate.