IST256 Syllabus Fall 2021

Course Information

IST256: Applications Programming for information Systems


This course is for students who are new to programming yet desire to learn how it applies to our everyday lives.

Catalog Description

Structured program design, development testing, implementation, and documentation of common information system applications using structured programming languages. Lectures and laboratory.


Due to the prevalence of technology in our lives, learning to program has become the critical skill of the 21st century. Students will learn practical applications of computer programming such as how to automate tasks, manipulate data and solve problems applicable to almost any academic discipline.

Learning Outcomes

At the end of the course, students will be able to:

  1. Analyze complex problems by thinking computationally and systematically.
  2. Solve practical, real-world problems using a modern computer programming language..
  3. Demonstrate the ability to read, write, discuss and code confidently.
  4. Understand how to code in teams, collaborate with others and manage source code.
  5. Acquire new programming knowledge independently.

Large Group and Small Group Sections

Every student in IST256 is assigned to the main section M001, then one of the recitation sections. You are required to attend both sections every week. Your recitation instructor is responsible for your grades.

SU Section Class # Type Instructor Instructor Email Meeting Day/Time Location / Instruction Mode
M001 17302 Large Group Michael Fudge [email protected] Mondays 3:45pm - 5:05pm Grant Auditorium
M002 17321 Small Group Michael Fudge [email protected] Wednesdays 2:15pm - 3:35pm Hinds Hall 010
M004 17323 Small Group Subhasree Sengupta [email protected] Wednesdays 3:45pm - 5:05pm Hinds Hall 018
M005 17324 Small Group Deborah Nosky [email protected] Wednesdays 2:15pm - 3:35pm Hinds Hall 018
M007 17352 Small Group Deborah Nosky [email protected] Wednesdays 12:45pm - 2:05pm Hinds Hall 011
M008 17471 Small Group Paige Brnger [email protected] Wednesdays 3:45pm - 5:05pm Hinds Hall 010

Office Hours

Office hours are for asking questions, clearing up doubts and misunderstandings in the the coursework and getting advice / guidance on labs and homework. Please to not expect to be tutored during office hours, and please do not work on your homework during our office hours. Each of your instructor’s Office Hours will be posted in Blackboard. If you require tutoring, please see the getting help section below.

Understanding Approach Used in this Course

Learning to program a computer does not come easy for most people. Decades of teaching programming to students like yourself has taught me it requires time, patience, practice and a well-established routine. This is not unlike the same routine required to learn a foreign language or musical instrument. There are times to practice and then times to demonstrate what you have learned.

Forced Practice

For better or worse, this course grading is designed to force you to practice. There are various activities due each week: readings, labs, and in-class / out-of-class homework activities. These are designed to expose you to programming little each day rather than binging the content once a week. Consuming the material this way gives you multiple points of exposure and most importantly time to process. Practice activities are effort graded. This means being correct carries the same weight as explaining your struggles when you know you are not correct and seeking help when you need it.

Building Habit Through Routine

Another thing we do to help you to be successful is to force a routine upon you. The course material is conceptually difficult so we try to remove some of the logistics from learning by providing the same structure week to week:

  1. Before LARGE GROUP lecture you are exposed to the topic through reading. The focus is on understanding the concepts.
  2. During LARGE GROUP lecture you observe and ask questions. Students do not write code at this point. The focus is on reading code, understanding the concepts as applied through code, and learning how the code executes.
  3. The LAB assignments force you to practice writing code for the first time. This is done in a guided fashion. You are given short, specific problems to solve with code. Guidance is provided to those who require it. The completed lab is submitted as evidence that you practiced and include what you have learned.
  4. During the SMALL GROUP lecture, we focus on problem solving and writing solutions with code. The problem at this phase are similar complexity as the homework. At this point every student should be writing code, learning to get comfortable writing code and troubleshooting problems. Code written in small group must be turned in for a participation grade.
  5. Finally, the HOMEWORK assignments are your opportunity to demonstrate you can code a solution to a problem on your own. Guidance is provided as to how to approach the problem. Homework assignments are a form of practice so it is expected students will explain what they learned or are still struggling to conceptualize.


Assessments are no longer about practice. They are about demonstrating what you have learned. There are two types of assessments in this course EXAMS and a PROJECT. There are exams at quarterly milestones throughout the course and in the sprit of mastery, the lowest exam score is dropped. The project demonstrates you can learn to code independently, then explain your solution to another.

Large Session Expectations

  • The Monday’s Large group session, section M001 in a large lecture hall. Because it can be intimidating to ask questions in this forum, you are encouraged to use the class chat offered for this course. The first slide at the beginning of large group will explain how to access the class chat. You are welcome, and encouraged to ask questions or for clarification of concepts during the lecture.

  • You will be expected to engage in class by participating in class-wide polls, quizzes and surveys. Your responses are not graded but they are recorded as a means to measure your participation and engagement in class.

  • Do NOT try to code as I code. Instead, watch and ask questions about what you see and experience. It’s far too early for most learners to try and code as I code at this point, and there will be opportunities to do that in small group. Instead you should take nodes and ask questions. Except for the homework solution, the code I write will be made available to you after class.

Small Group Expectations

  • Small group sessions meet with a fraction of the class. Here you will practice programming and algorithmic problem solving.

  • You are expected to bring your fully-charged laptop to class! You will need to use it to complete in-class exercises. If you do not have your laptop, then you are not prepared for class.

  • You will be expected to participate in class. This includes sharing your thoughts, ideas, and computer code when you are asked. Some of you might be hesitant to do this, but you need to get over it for your own benefit. Nobody starts out programming as an expert. It takes practice, experimentation, and repeated failure to get it.

  • Please be respectful of your instructor and classmates. You are not competing against each other, you are a community. Not everyone learns at the same pace and we should be kind and respectful to our classmates who struggle.

Course Resources

Course Website

Our course website it located at or The course website contains the syllabus, list of due dates, and links to readings, content, videos and tools used in the course.


Our programming environment is a private-cloud web application called Jupyter Hub. This is the de-facto programming environment for data scientists. All students have an account; use your SU NetID and password to login. After you login you will see a library folder inside that folder is an ist256 folder. All of the course content (lecuture slides, code samples, labs, homework) is available in this folder.


The following texts are required:

  • Programming in Python 3 an Interactive Textbook Must be purchased online or through the SU bookstore. Student cost is $58. Your use of this book will be tracked and counts towards your grade.
  • Python for Everybody: Exploring Data In Python 3 by Charles Severance. Free.

Here are some additional free textbook recommendations. Consider these supplemental resources:

Activating Your Zybook

  • Go to:
  • Create Your Account. Make sure to sign up with your email address. NOTE: If you do not use your SU Email, our bot cannot track your book usage.
  • When prompted for the Zybook code, enter SYRIST256Fall2021
  • You can purchase the Zybook online with a credit card, or purchase through the bookstore and receive an access code.

NetID, Google and Microsoft Accounts

This course will require you to use your Syracuse University provided Google and Microsoft Accounts. Both accounts are based on your NetId. Your Google account is [email protected] and your Microsoft Account is [email protected]. Learn more:

3rd Party Services Used in This Course

This course uses a variety of 3rd party services to supplement and enhance your learning experience. Here’s a list of with links to the resources we will use in this course. It is expected you will know how to access each of these resources.

Tool Purpose Link Notes
Blackboard Announcements, Grades Sign in with your NetId
JupyterHub Python Programming, Code Samples, Slides, Labs, HW. Sign in with your NetId
Microsoft Teams Chat with classmates / Instructors. Virtual office hours. Sign in with your SU Microsoft Account
Polly In-Class polling for large group sessions. Sign in with SU Microsoft Account
Severance Textbook The popular “Python for Everyone” book. Available in a variety of different formats.
Zoom Videoconferencing tool we use for office hours and student support. Access through Blackboard
Zybooks Textbook Our hands-on interactive textbook. Purchase online or via the SU bookstore. Your Zybooks account

Please consult the Course Links section for details.

Bring Your Own Device

This course uses the BYOD (Bring Your Own Device) model.

  • For Large Group you need a device to ask and answer questions, basically to participate in group chat and polls (Polly). This can be a smartphone, tablet or laptop (Mac, Windows, or Chromebook). Do not try to code along in large group unless you are experienced. It is best to take notes and ask questions.
  • For Small Group you need a device for which you can write code in the browser using Jupyter Hub. This should be a laptop computer (Mac, Windows or Chromebook).


The University offers free tutoring for this course through the Center for Learning and Student Success (CLASS).

Group sessions are offered in the lower level of Bird library:

  • Tuesdays 5:00pm - 6:00pm Table 4
  • Wednesdays 7:00pm - 8:00pm Table 4
  • Thursdays 6:30pm - 7:30pm Table 4

Sign up for tutoring at:

For more information on individual and group tutoring sessions, please visit:

Methods of Evaluation


This course uses a well thought out mix of individual, group, in-class and out-of-class instruments to assess your knowledge acquisition. A variety of techniques are used to cater to students of different learning styles and assess the course learning outcomes.

Type Learning
Quantity Points
Pct Of
Total Grade
How Do I Turn it in?
Exams E1 - E4 Assessment 1,2,3 4 (3 best count) 30 90 36% Exams Section on Blackboard
Project P1 - P4 Assessment 1,2,3,5 1 (Split in 4 Phases) 49 49 20% Project folder on Jupyterhub
Zybook Participation Z01 - Z10 Practice 1,2,3 10 2 20 9% Just Read the assigned chapter on the Zybooks Website.
Class Coding Labs L01 - L13 Practice 1,2,3,4 13 3 39 16% Run the submission script at end of the Lab in Jupyterhub
Homework H01 - H13 Practice 1,2,3,4 13 3 39 16% Run the submission script at end of the Homework in Jupyterhub
Small Group Code S01 - S13 Practice 1,2,3 13 1 13 5% Turn in code your code after your small group session.

Exams (E1 - E4)

  • Exams are high-stakes assessments. They measure the individual’s ability to recall, understand, and apply the course material. They are one of two instruments in this course which measures your mastery of the learning outcomes.
  • There will be four exams in the course. Your best three scores count towards your final grade, offering you an opportunity to achieve mastery over the material, should you perform poorly on one.
  • Each exam focuses on specific lessons, but due to the nature of the course material, all exams are cumulative.
  • Exams are issued online via blackboard outside of class.
  • You may take the exam anytime and from anywhere on the exam date.
  • Once you start the exam you must complete it in a single session.
  • You get one exam attempt. Do not start until you are ready to take the exam.
  • Exams are timed at 15 minutes and will auto-submit when time is up.
  • Questions consist of multiple choice, and fill-in-the blank.
  • Questions are delivered at random from a pool of questions, one question at a time. This is to mitigate cheating on the exam and copying of code into Python.
  • Since everyone has a 24 hour window to complete the exam, there are no re-issues or make-ups.
  • To prevent building of test banks, answers to the exams will not be released. You may ask in office hours to see your exam.

Project (P1 - P4)

  • The project is the other high-stakes assessment. The goal of the project is to demonstrate your ability as an individual to program something novel, useful and innovative in Python. It should represent an accurate culmination of what you have learned in the course.
  • You will work on the project individually, be expected to produce working code, and be able to explain it at both a high and detailed level.
  • The project is divided into 4 phases; due dates are posted on the course schedule.
  • You will receive feedback and advice after the first two deliverables; a project grade after the final deliverable.
  • Each project phase must be submitted on Jupyterhub using the provided submission notebook.
  • Late submissions are accepted but will impact your project grade severely.

Project Phases

Phase Name Deliverables
P1 Ideation Outline the specific goals and objectives of your project; include evidence of its feasibility by including citations of resources you will use to complete the code.
P2 Beta Version Create a first working draft of the completed program i.e. the “Beta” version; Provide live demo of running program to your small group instructor for feedback.
P3 Final Version Final version of working code; Instructor feedback taken into consideration; Improvements to achieve the desired grade.
P4 Demo and Reflection Pitch / Demo video of running project program. Video reflection;

Criteria for Project Grade

  • Complete all project deliverables on time, and to satisfaction as per the requirements.
  • Clearly demonstrate through code 3 things you learned beyond what was taught in class. This can be a new API, module, aspect of or aspect of the Python language itself. This goes beyond just use, you must identify and explain.
  • Take instructor feedback into consideration.
  • Journal as your work on your project, recording time and tasks.
  • In addition, there is a grade limit based on the number of lines of student-written code that is used in the project. Note: copies of code from class or elsewhere do not count. This must be code you wrote yourself that directly impacts the project’s behavior.
Lines of student-written code in the project Maximum Possible grade
200 or more A+
100 to 200 B+
Under 100 C+

Grading Scale For Project

Finally, you are assigned a letter grade for your project. This letter grade is translated to a number of points based on this table.

Project Grade Assigned Points
A+ 49
A 47
A- 45
B+ 42
B 40
B- 37
C+ 35
C 32
C- 30
D+ 27
D 25
D- 20
F 0

Specifics on the project as well as details of each deliverable can be found under your project folder in Jupyterhub.

Zybook Participation (Z01 - Z10)

  • The Zybook interactive textbook is an excellent resource for learning Python. As such, your use of this resource will be graded. This is a low-stakes assessment which is mostly participatory.
  • For most lessons, relevant Zybook chapters are assigned on the course schedule.
  • As you complete the exercises and readings, your activity is recorded. This is how your grade is determined, which is then posted to Blackboard.
  • Only the readings and participation activities are graded. The challenge activities and labs are optional. You do not need to complete them and they are not part of your Zybook participation grade.
  • Be sure to complete the activities prior to the due date.

Rubric for Zybook Participation

Amount of the chapter you’ve completed before due date Assigned Grade
100% 2
75% or more 1
Less than 75% 0

Class Coding Labs (L01 - L13)

  • Each week there will be an out-of-class hands-on lab programming activity.
  • The purpose of the lab is to provide guided, hands-on programming practice. Labs are your first opportunity to get your hands on a keyboard and start programming. This is a low-stakes assessment, mostly participatory.
  • The What’s Due section of the syllabus identifies the lab you should complete.
  • You can find the lab activity on JupyterHub.
  • This activity must be completed and turned submitted by the due date.
  • You may work alone or with a partner as you complete the lab. If you work with another, you should both complete the lab individually, and you should make a note of who your lab partner was when you completed your work. As to not draw attention to a potential academic integrity violation.
  • If you are having difficulty completing the lab, you are welcome to review the walk-through video which guides you through the more difficult parts of the lab. You are encouraged to only consult the walk-through when you are stuck.

Rubric for Class Coding Labs

Lab Criteria Definitions:

  1. Code Correct means all You Code sections of the lab are correct.
  2. Code Complete means all You Code sections have an honest attempt to code the problem at hand. Please note this does not imply the code is correct. If the code is not correct, there is an adequate reflection with student questions.
  3. Cells Executed means all code cells in the lab display evidence they were executed in your lab submission.
  4. Metacognition Complete means the student made an honest effort to answer the open-ended questions in the lab adequately conveying what you have learned and what still confuses you. This should be evident in the work you have done to complete the lab.
Lab Criteria Assigned Grade
All 4 criteria met 3
3 criteria met 2
2 criteria met 1
Less than 2 criteria met 0

Homework (H01 - H13)

  • Practice makes perfect. Each week you will be assigned homework to complete outside of class.
  • The goal of the homework is practice problem solving with code independently. Throughout the process you should take inventory of your abilities with respect to the material. While it is admirable to get the code correct, that is not the evaluation criteria nor is it the purpose of the homework. You should use the homework as a personal gauge for how well you are grasping the material.
  • You can find the the homework assignments on JupyterHub. The What’s Due section of the syllabus identifies the homework assignment you should complete.
  • Homework are individual assignments. You can collaborate on strategy but you must must work alone on the assignment. You must be able to explain the code you write, or it will be considered an academic integrity violation. It’s not about getting it right, but it is about making an honest self-assessment!
  • For each homework there is an advice video which provides hints and tips for how you can approach the homework assignment. You are encouraged to only consult the video when you are having difficulty with the homework.
  • If you get assistance from somewhere else, such as online, or someone else such as a tutor, you must divulge that in your submission or it will be considered an academic integrity violation.

Rubric for Homework

Homework Criteria Definitions:

  1. Code Correct means an honest attempt was made at a solution and the solution is correct. For incorrect solutions, the code runs, might not be correct, but there is some explanation in the reflection.
  2. Code Well Written means your code is easy to understand, modular in nature, has aptly named variables, was programmed in the style we learned in class, and demonstrates what you learned that week.
  3. Problem Analysis Complete problem analysis was complete, identifying the problem’s inputs, outputs, and algorithm. An outline of the step-by-step process for how the program should behave.
  4. Questions Complete means the student made an honest effort to answer the open-ended questions in the lab.
  5. Reflection Complete means the student completed their code reflection, discussing their experiences with completing the assignment. This should provide insight as to how the work was done touching upon struggles, how you got it done, what was learned in the process.
Homework Criteria Assigned Grade
All 5 criteria met 3
4 criteria met 2
3 criteria met 1
Less than 3 criteria met 0

Small Group Code (S01 - S13)

Your small group instructor will measure attendance and participation each session through you turning in your small group code. This is the code we worked on in class together. The specific criteria is entirely at the discretion of your small group instructor, but mainly consists participating in small group through coding effort.

Grading Scale For Final Grade

We use the following grading scale for translating your total points earned into a letter grade to be submitted to the University registrar.

Student Achievement Total Points Earned Registrar Grade Grade Points
Mastery 238 - 250 A 4.000
225 - 237 A- 3.666
Satisfactory 213 - 224 B+ 3.333
200 - 212 B 3.000
188 - 199 B- 2.666
Low Passing 175 - 187 C+ 2.333
163 - 174 C 2.000
151 - 162 C- 1.666
Unsatisfactory 125 - 150 D 1.000
0 - 124 F 0.000

Course Specific Policies

Due Dates

  • Due dates are posted on the Syllabus in the course schedule section, specifically What’s Due?. Due dates are also posted in Blackboard.
  • In order to provide timely and relevant feedback, no late work is accepted. Exceptions will only be made under extreme circumstances with supporting University documentation of illness or personal reasons.

Extra Credit

No extra credit is offered in this course.

Course Honor Code

The course honor code represents our commitment to Academic Integrity in a programming course. I drafted the class honor code to avoid academic negligence - situations where students are unaware that their actions are actually a form of cheating. Our honor code remedies this problem by clearly stating the expectations of Academic Integrity for this course. It states:

  1. All work is my own. Answers on all student work, assignments (labs, homework, problem sets, projects, papers, etc…) and assessments (quizzes, exams, tests, etc…) are my own individual work (except where collaboration is explicitly permitted). In the case where collaboration is permitted I will only collaborate within my team. Your own work means it manifests your own thoughts and ideas, not someone else!
  2. I will not share answers. I will not make answers (either my own or the professor’s) to work, assignments (labs, problem sets, projects, papers, homework, etc…) and assessments (quizzes, exams, tests, etc…) available to anyone else in or out of class. This includes posting them on the web or sharing them in test banks.
  3. I will not misrepresent my ability. I will not engage in any activity which misrepresents or falsifies my knowledge of the subject matter and therefore improves my grade dishonestly. This includes unsanctioned test aids, copying homework, and assistance from unapproved sources outside of class.
  4. I will give credit. I will always pay attribution to my sources, and not misrepresent the works of others as my own. If you get code from the internet, you must cite it like you would any source in an academic paper.
  5. I accept the honor code and its consequences. I understand and accept that that all work I submit is subject to the honor code, and if I violate this honor code I my instructor is obligated to report me to the University’s office of Academic Integrity.

When in doubt, ask. When unsure, disclose openly. Most students who get into trouble are trying to hide their academic dishonesty. Don’t do that. We will catch you easily.

Sanctions for Violations of Academic Integrity

  • All suspected academic integrity violations will be reported to the university’s office of academic integrity.
  • Proposed sanction for violations of a low-stakes assessment such as a homework assignment or lab, is a grade of zero.
  • Proposed sanction for violations on high-stakes assessment such as a quiz, exam or the final project is a grade of F in the course.

Syracuse University Policies

Syracuse University has a variety of other policies designed to guarantee that students live and study in a community respectful of their needs and those of fellow students. Some of the most important of these concern:

Diversity and Disability (ensuring that students are aware of their rights and responsibilities in a diverse, inclusive, accessible, bias-free campus community) can be found here, at:

Religious Observances Notification and Policy (steps to follow to request accommodations for the observance of religious holidays) can be found here, at:

Orange SUccess (tools to access a variety of SU resources, including ways to communicate with advisors and faculty members) can be found here, at:

Syracuse University values diversity and inclusion; we are committed to a climate of mutual respect and full participation. There may be aspects of the instruction or design of this course that result in barriers to your inclusion and full participation in this course. I invite any student to meet with me to discuss strategies and/or accommodations (academic adjustments) that may be essential to your success and to collaborate with the Center for Disability Resources (CDR) in this process.

If you would like to discuss disability-accommodations or register with CDR, please visit Center for Disability Resources. Please call (315) 443-4498 or email [email protected] for more detailed information.

CDR is responsible for coordinating disability-related academic accommodations and will work with the student to develop an access plan. Since academic accommodations may require early planning and generally are not provided retroactively, please contact CDR as soon as possible to begin this process.

University Attendance Policy

Attendance in classes is expected in all courses at Syracuse University. Students are expected to arrive on campus in time to attend the first meeting of all classes for which they are registered. Students who do not attend classes starting with the first scheduled meeting may be academically withdrawn as not making progress toward degree by failure to attend. Instructors set course-specific policies for absences from scheduled class meetings in their syllabi.

It is a federal requirement that students who do not attend or cease to attend a class to be reported at the time of determination by the faculty. Faculty should use “ESPR” and “MSPR” in Orange Success to alert the Office of the Registrar and the Office of Financial Aid. A grade of NA is posted to any student for whom the Never Attended flag is raised in Orange SUccess. More information regarding Orange SUccess can be found here, at: Students should also review the University’s religious observance policy and make the required arrangements at the beginning of each semester

Academic Integrity Policy

Syracuse University’s Academic Integrity Policy reflects the high value that we, as a university community, place on honesty in academic work. The policy defines our expectations for academic honesty and holds students accountable for the integrity of all work they submit. Students should understand that it is their responsibility to learn about course-specific expectations, as well as about university-wide academic integrity expectations. The policy governs appropriate citation and use of sources, the integrity of work submitted in exams and assignments, and the veracity of signatures on attendance sheets and other verification of participation in class activities. The policy also prohibits students from submitting the same work in more than one class without receiving written authorization in advance from both instructors. Under the policy, students found in violation are subject to grade sanctions determined by the course instructor and nongrade sanctions determined by the School or College where the course is offered as described in the Violation and Sanction Classification Rubric. SU students are required to read an online summary of the University’s academic integrity expectations and provide an electronic signature agreeing to abide by them twice a year during pre-term check-in on MySlice.

Use of Blackboard

This course involves the use of Syracuse University’s Blackboard system as an online tool. The environment is composed of a number of elements that will help you be successful in both your current coursework and your lifelong learning opportunities. To access Blackboard, use your Syracuse University NetID & Password. This specific course will appear in your course list.

To search for answers to your Blackboard questions, visit the Answers self-help knowledge If you have problems logging in or need assistance with Blackboard, contact the ITS Service Center at: [email protected] or 315.443.2677. The Syracuse University Blackboard support team will assist you.

Course Schedule

Dates Topic (Click Link for Content)
8/30 - 9/5 Lesson 01: Introduction to Python Programming
9/6 - 9/12 Lesson 02: Input, Output, Variables and Types
9/13 - 9/19 Lesson 03: Conditionals
9/20 - 9/26 Lesson 04: Iterations
9/27 - 10/3 Lesson 05: User-defined Functions, Modules
10/4 - 10/10 Lesson 06: Strings and Text Processing
10/11 - 10/17 Lesson 07: File I/O and Persistence
10/18 - 10/24 Lesson 08: Lists
10/25 - 10/31 Lesson 09: Dictionaries and JSON
11/1 - 11/7 Lesson 10: HTTP Protocol and Network Programming
11/8 - 11/14 Lesson 11: Web API’s
11/15 - 11/21 Lesson 12: Data Analysis with Pandas
11/22 - 11/28 No Classes - Thanksgiving Break
11/29 - 12/5 Lesson 13: Data Visualization
12/6 - 12/12 Project Week (No Large Group Meeting, Meet Small Group Online for P2)

What’s Due?

Use this table to track the due dates of the out-of-class deliverables in this course. Dates and times are Eastern Time Zone.

Date Due Time Due Gradebook Points Tool What is Due?
9/4/2021 11:59 PM Z01 2 Zybooks Chapter 1
9/4/2021 11:59 PM L01 3 Jupyterhub 01-Intro/LAB-Intro.ipynb
9/4/2021 11:59 PM S01 1 Jupyterhub 01-Intro/SmallGroup-Intro.ipynb
9/4/2021 11:59 PM H01 3 Jupyterhub 01-Intro/HW-Intro.ipynb
9/7/2021 3:00 PM Z02 2 Zybooks Chapter 2
9/7/2021 11:59 PM L02 3 Jupyterhub 02-Variables/LAB-Variables.ipynb
9/8/2021 11:59 PM S02 1 Jupyterhub 02-Variables/SmallGroup-Variables.ipynb
9/11/2021 11:59 PM H02 3 Jupyterhub 02-Variables/HW-Variables.ipynb
9/13/2021 3:00 PM Z03 2 Zybooks Chapter 3
9/14/2021 11:59 PM L03 3 Jupyterhub 03-Conditionals/LAB-Conditionals.ipynb
9/15/2021 11:59 PM S03 1 Jupyterhub 03-Conditionals/SmallGroup-Conditionals.ipynb
9/18/2021 11:59 PM H03 3 Jupyterhub 03-Conditionals/HW-Conditionals.ipynb
9/20/2021 3:00 PM Z04 2 Zybooks Chapter 4
9/21/2021 11:59 PM L04 3 Jupyterhub 04-Iterations/LAB-Iterations.ipynb
9/22/2021 11:59 PM S04 1 Jupyterhub 04-Iterations/SmallGroup-Iterations.ipynb
9/24/2021 11:59 PM E1 30 Blackboard Exam 1 (Focus on Lessons 01-03)
9/25/2021 11:59 PM H04 3 Jupyterhub 04-Iterations/HW-Iterations.ipynb
9/27/2021 3:00 PM Z05 2 Zybooks Chapter 5
9/28/2021 11:59 PM L05 3 Jupyterhub 05-Functions/LAB-Functions.ipynb
9/29/2021 11:59 PM S05 1 Jupyterhub 05-Functions/SmallGroup-Functions.ipynb
10/2/2021 11:59 PM H05 3 Jupyterhub 05-Functions/HW-Functions.ipynb
10/4/2021 3:00 PM Z06 2 Zybooks Chapter 6
10/5/2021 11:59 PM L06 3 Jupyterhub 06-Strings/LAB-Strings.ipynb
10/6/2021 11:59 PM S06 1 Jupyterhub 06-Strings/SmallGroup-Strings.ipynb
10/9/2021 11:59 PM H06 3 Jupyterhub 06-Strings/HW-Strings.ipynb
10/11/2021 3:00 PM Z07 2 Zybooks Chapter 7
10/12/2021 11:59 PM L07 3 Jupyterhub 07-Files/LAB-Files.ipynb
10/13/2021 11:59 PM S07 1 Jupyterhub 07-Files/SmallGroup-Files.ipynb
10/16/2021 11:59 PM H07 3 Jupyterhub 07-Files/HW-Files.ipynb
10/18/2021 3:00 PM Z08 2 Zybooks Chapter 8
10/19/2021 11:59 PM L08 3 Jupyterhub 08-Lists/LAB-Lists.ipynb
10/20/2021 11:59 PM S08 1 Jupyterhub 08-Lists/SmallGroup-Lists.ipynb
10/22/2021 11:59 PM E2 30 Blackboard Exam 2 (Focus on Lessons 04-07)
10/23/2021 11:59 PM H08 3 Jupyterhub 08-Lists/HW-Lists.ipynb
10/25/2021 3:00 PM Z09 2 Zybooks Chapter 9
10/26/2021 11:59 PM L09 3 Jupyterhub 09-Dictionaries/LAB-Dictionaries.ipynb
10/27/2021 11:59 PM S09 1 Jupyterhub 09-Dictionaries/SmallGroup-Dictionaries.ipynb
10/30/2021 11:59 PM H09 3 Jupyterhub 09-Dictionaries/HW-Dictionaries.ipynb
11/2/2021 11:59 PM L10 3 Jupyterhub 10-HTTP/LAB-HTTP.ipynb
11/3/2021 11:59 PM S10 1 Jupyterhub 10-HTTP/SmallGroup-HTTP.ipynb
11/6/2021 11:59 PM H10 3 Jupyterhub 10-HTTP/HW-HTTP.ipynb
11/9/2021 11:59 PM L11 3 Jupyterhub 11-WebAPIs/LAB-WebAPIs.ipynb
11/10/2021 11:59 PM S11 1 Jupyterhub 11-WebAPIs/SmallGroup-WebAPIs.ipynb
11/12/2021 11:59 PM E3 30 Blackboard Exam 3 (Focus on Lessons 08-10)
11/13/2021 11:59 PM H11 3 Jupyterhub 11-WebAPIs/HW-WebAPIs.ipynb
11/16/2021 11:59 PM L12 3 Jupyterhub 12-Pandas/LAB-Pandas.ipynb
11/17/2021 11:59 PM S12 1 Jupyterhub 12-Pandas/SmallGroup-Pandas.ipynb
11/20/2021 11:59 PM H12 3 Jupyterhub 12-Pandas/HW-Pandas.ipynb
11/20/2021 11:59 PM P1 0 Jupyterhub project/P1.ipynb
11/29/2021 3:00 PM Z10 2 Zybooks Chapter 10
11/30/2021 11:59 PM L13 3 Jupyterhub 13-Visualization/LAB-Visualization.ipynb
12/1/2021 11:59 PM S13 1 Jupyterhub 13-Visualization/SmallGroup-Visualization.ipynb
12/4/2021 11:59 PM H13 3 Jupyterhub 13-Visualization/HW-Visualization.ipynb
12/7/2021 11:59 PM P2 0 Jupyterhub project/P2.ipynb
12/10/2021 11:59 PM E4 30 Blackboard Exam 4 (Focus on Lessons 11-13)
12/15/2021 11:59 PM P3 0 Jupyterhub project/P3.ipynb
12/15/2021 11:59 PM P4 49 Jupyterhub project/P4.ipynb