IST256 Syllabus Fall 2024

Course Information

IST256: Introduction to Python for the Information Profession

Audience: IST256 or IST356?

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

  • IST256 is for students with little to no programming experience. The course content is 75% python fundamentals and 25% python for data analytics.
  • IST356 is for students with prior programming experience. The course content is 25% python fundamentals and 75% python for data analytics.

Catalog Description

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

Description

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 Recitation 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 Professor Professor Email Meeting Day/Time Location / Instruction Mode
M001 16857 Main Section Michael Fudge [email protected] Mondays 3:45pm - 5:05pm Lyman Hall 132
M002 16865 Recitation Preeti Jagadev [email protected] Wednesdays 2:15pm - 3:35pm Lyman Hall 115B
M003 17007 Recitation Preeti Jagadev [email protected] Wednesdays 3:45pm - 5:05pm Lyman 115B
M004 16866 Recitation Nina Schneider [email protected] Wednesdays 3:45pm - 5:05pm Hinds Hall 011
M006 17008 Recitation Nina Schneider [email protected] Wednesdays 2:15pm - 3:35pm 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.

Spaced and Repetitive 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 formative assessments. 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 impose 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 the main section lecture you are exposed to the topic through reading and video assignments. The focus is on understanding the concepts.
  2. During the main section lecture you observe and ask questions. You do not need write code at this point. The focus is on reading code, understanding the concepts as applied through code, learning how the code executes, and asking questions.
  3. The lab assignments help 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. For each lab, a guidance video 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 your recitation, 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 Recitation 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. Expressing your thoughts on the learning experience is an import part of learning itself.

Summative Assessments

Summative assessments are no longer about practice. They are about measuring what you have learned. There are two types of summative assessments in this course exams and a project. There are exams at milestones throughout the course. The project demonstrates you can learn to code independently, then explain your solution to another.

Main Session Expectations (Mondays)

  • 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. Students who do not participate are noted and will be flagged for poor participation in orange success.

  • You are encouraged to not try to code as I code, especially if you are a beginner. Instead, watch and ask questions about what you see and experience. It’s far too early for most learners to try and 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.

Recitation Expectations (Wednesdays)

  • Recitation 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 https://ist256.com or https://ist256.github.io. The course website contains the syllabus, list of due dates, and links to readings, content, videos and tools used in the course.

Jupyterhub

Our programming environment is a private-cloud web application called Jupyter Hub. https://v2hub.ischool.syr.edu. This is the de-facto programming environment of the scientific community. All students have an account; use your SU Microsoft Account (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 (lecture slides, code samples, labs, homework) is available in this folder.

Textbooks

The following text is required:

  • Python for Everybody: Exploring Data In Python 3 by Charles Severance. https://www.py4e.com/book. The book is free and available in several formats from the URL provided.
  • There are assigned readings which must be completed prior to each large group lecture. The reading can be found in the content section of the website and the course syllabus.
  • In addition to the required reading the last 4 units have custom readings authored by your instructor.

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

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 https://blackboard.syr.edu Sign in with your NetId
JupyterHub Python Programming, Code Samples, Slides, Labs, HW. https://v2hub.ischool.syr.edu Sign in with your NetId
Microsoft Teams Chat with classmates / Instructors. Virtual office hours. https://teams.ist256.com Sign in with your SU Microsoft Account
Polley In-Class polling for large group sessions. https://poll.ist256.com Sign in with SU Microsoft Account
Severance Textbook The popular “Python for Everyone” book. https://www.py4e.com/book Available in a variety of different formats.
Zoom Videoconferencing tool we use for office hours and student support. https://zoom.us Access through Blackboard

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 (Polley). 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 Recitation 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). You will be expected to bring this device to recitation and use it to write code as part of participating in the recitation.
  • Since you will code in a web browser using the provided JupyterHub platform, the hardware requirements are minimal. Any laptop, or chromebook should suffice.

Tutoring

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

Sign up for tutoring at: http://myt.syr.edu.

For more information on individual and group tutoring sessions, please visit: https://class.syr.edu/academic-support/

Methods of Evaluation

Grading

This course uses a well thought out mix of formative, summative, 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.

Assessment
Name		 Blackboard
Gradebook		 Type Learning
Outcomes		 Quantity Points
Each		 Points
Total		 Pct Of
Total Grade		 How Do I Turn it in?
Exams E1 - E4 Assessment 1,2,3 4 22/23 90 35% See course schedule for dates
Project P1 - P4 Assessment 1,2,3,5 1 49 49 20% Project folder on Jupyterhub
Class Coding Labs L01 - L13 Practice 1,2,3,4 13 3 39 15% Run the submission script at end of the Lab in Jupyterhub
Homework H01 - H13 Practice 1,2,3,4 13 3 39 15% Run the submission script at end of the Homework in Jupyterhub
RecitationCode S01 - S13 Practice 1,2,3 13 3 39 15% Run the submission script at the end of recitation
TOTAL 256

Exams (E1 - E4)

  • Exams are high-stakes, summative 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 4 exams in the course.
  • Each exam focuses on specific lessons, but due to the nature of the course material, all exams are cumulative.
  • Exams are issued in class.
  • Exams are closed book. No used of Notes, Jupyterhub, or any outside resources.
  • You will have 25 minutes to complete 22 or 23 exam questions.
  • Students can bring a one page cheat sheet with typed or hand written notes and/or code samples.
  • Questions consist of multiple choice, fill-in-the blank, and short-answer.
  • If you require a special testing accommodation, you should schedule to take the exam in the CDS testing center as it will be difficult to accommodate exam time extensions in class.
  • There are no make-up exams. Exam dates are posted on the syllabus, please plan accordingly.

Project (P1 - P4)

  • The project is the other high-stakes summative assessment. The goal of the project is to demonstrate your ability as an individual to perform a data analysis 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 not accepted. We need time to grade.
  • The What’s Due section of the syllabus outlines the two exam dates.

Project Phases

Phase Name Deliverables
P1 Data Set Selection Chose a primary and backup dataset of interest for which you will perform your data analysis. You will recieve feedback only.
P2 Exploratory Data Analysis Write python code to explore your dataset as to understand it and to extract information as to create your data story.
P3 Data Product Write python code to tell a data story with your dataset. This should allow uses to interact with the data and see visualizations.
P4 Demo and Reflection Demo video of how your data product works and a video reflection of what you learned.

Criteria for Project Grade

  • Complete all project deliverables on time, and to satisfaction as per the requirements. While you recieve a grade for P4, work from all other phases are factored into your grade.
  • Your code clearly demonstrates what you learned; the code you write is in the style learned throughout the course.
  • Take instructor feedback was taken into consideration.
  • Journal as your work on your project, recording time and tasks.
  • Effective data analysis, coherent data story, evidence of manipulations to source data as to create improvements to the data story.
  • Polished final data product that uses interact for inputs.
  • 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
100 or more A+
50 to 100 B+
Under 50 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.

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, formative assessment.
  • 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 lab walk-through video which guide 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. We value reflection. It is important to the learning process.
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, or the an AI assistant 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 Metacognition section.
  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/plan.
  4. Metacognition Complete means the student made an honest effort to answer the open-ended questions in the assignment.
Homework Criteria Assigned Grade
All 4 criteria met 3
3 criteria met 2
2 criteria met 1
Less than 2 criteria met 0

Recitation Code Assignment (S01 - S13)

Your recitation professor will measure attendance and participation each session through you turning in your recitation code activity. This is the code you work on in class with your classmates. If you are not in class, you will not recieve credit. The specific criteria is entirely at the discretion of your instructor, but most likely consists participating in the programming assignment completed in class.

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 243 - 256 A 4.000
230 - 242 A- 3.666
Satisfactory 217 - 229 B+ 3.333
204 - 216 B 3.000
192 - 203 B- 2.666
Low Passing 179 - 191 C+ 2.333
166 - 178 C 2.000
153 - 165 C- 1.666
Unsatisfactory 128 - 152 D 1.000
0 - 127 F 0.000

Course Specific Policies

Due Dates

  • Late work is not accepted. Due to the formative nature of work in the course (practice with timely feedback), it does not make sense to accept late work. Please plan to keep pace with the course by completing your practice exercises on time.
  • Due dates are posted on the Syllabus in the course schedule section, specifically What’s Due?. Due dates are also posted in Blackboard.
  • Once again: 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

Earning Research Credit through the CITRA Portal

This course participates in the Communication, Information, and Technology Research Alliance (CITRA) Research Pool. This is a shared resource for students interested in participating in scientific research being conducted by Newhouse or iSchool faculty and students, and you can earn credit for this course in exchange for volunteering for those studies. At any time during the semester, you can visit https://ischool.syr.edu/citra to read more about the study participation opportunities. Note that there may not always be studies available and you might not be eligible for all studies, but that the list of studies is updated frequently so you should check for new studies throughout the semester. For each study listed there are specific instructions for how to sign up and participate—if you have questions, please email the researcher listed directly.

When you sign up for a CITRA Pool study, you will earn credits equal to roughly 1 credit for each 30 minutes of study-participation time (although some studies could be worth more or less, depending on what you are asked to do). Each CITRA Pool credit can be assigned only to one course, and study participation must be completed by the last class day of the semester.

Finally, it is important to understand that it is not mandatory that you participate in research to earn course credit. If you would still like to earn course credit but are not interested in volunteering for any of the CITRA Pool studies, you may contact the coordinators of the CITRA Pool for more information about alternative credit activities. For iSchool courses, your contact is the CITRA coordinator Dr. Jaime Banks ([email protected]). You may also email those contacts for any other questions, comments, or concerns you have about the CITRA Pool system.

For our particular class, each CITRA credit is worth 1 point, and you are limited to earning 9 credits for the semester, or the equivalent of 3 practice assignments (lab, small group, or homework).

Course Withdraw

  • Only the main section instructor can sign a withdraw petition.
  • We will only sign a withdraw from this course based on poor academic performance (F grade).
  • You can always re-take the course and replace your failing grade with a passing grade.
  • If you are putting in the time and effort it is very difficult to fail this course in the first place.
  • This reserves course withdraws for their original intended purpose - extreme circumstances with supporting University documentation of illness or personal reasons.

Use of AI Assistants

Based on the assignments in this course and our specified learning outcomes, the full use of artificial intelligence as a tool, with disclosure and citation, is permitted in this course. Students do not need to ask permission to use these tools before starting an assignment or exam, but they must explicitly and fully indicate which tools were used and describe how they were used.

Course Honor Code - Academic Integrity

What Does Academic Integrity Mean In This Course?

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 allowed and disclosed). 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’s. AI is included in the “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 / chegg / course hero, 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. This includes passing off AI output as your own work.
  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, a tutor, or an AI assistant, you must cite it like you would any source in an academic paper. If you used AI, disclose that.
  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.

This can be easily summarized as: If code did not originate from you, you must clearly disclose where it came from.

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

Oops. I violated academic integrity. What now?

  • All suspected academic integrity violation 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 any violation on a summative assessment such as an 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. These policies apply to this course and can be found at this URL:

https://academicaffairs.syracuse.edu/important-syllabus-reminders/

Course Schedule

Assigned readings and help videos can be found in the corresponding lesson folder on Jupyterhub.

Dates Topic
8/26 - 9/1 Lesson 01: Introduction to Python Programming
9/2 - 9/8 Lesson 02: Input, Output, Variables and Types
No Large group on 9/2 due to Labor Day
9/9 - 9/15 Lesson 03: Conditionals
9/16 - 9/22 Lesson 04: Iterations
E1 9/18 on in Small Group
9/23 - 9/29 Lesson 05: User-defined Functions, Modules
9/30 - 10/6 Lesson 06: Strings and Text Processing
10/7 - 10/13 Lesson 07: File I/O and Persistence
10/14- 10/20 Fall Break 10/14 and 10/15
E2 on 10/16 in Small Group
10/21 - 10/27 Lesson 08: Lists
10/28 - 11/3 Lesson 09: Dictionaries and JSON
11/4 - 11/10 Lesson 10: Pandas I
E3 on 11/6 in Small Group
11/11 - 11/17 Lesson 11: Pandas II
P1 project deliverable due on 11/15
11/18 - 11/24 Lesson 12: Data Visualization
11/25 - 12/1 Thanksgiving Break - No Classes
12/2 - 12/8 Lesson 13: Web API’s
P2 project deliverable due on 12/6
12/9 - 12/15 E4 on 12/9 in Large Group
P3 and P4 Project delerables due 12/16

What’s Due?

There are deliberables due consistently each week. Use this table to track the due dates of all graded work in this course. Dates and times are Eastern Time Zone. Labs are due Tuesdays, Small group assignments are completed in your small group, and Homework are due Fridays.

Date Due Time Due Gradebook Points Where? What is Due?
8/27/2024 11:59 PM L01 3 Jupyterhub 01-Intro/LAB-Intro.ipynb
8/28/2024 11:59 PM S01 3 Jupyterhub 01-Intro/SmallGroup-Intro.ipynb
8/30/2024 11:59 PM H01 3 Jupyterhub 01-Intro/HW-Intro.ipynb
9/3/2024 11:59 PM L02 3 Jupyterhub 02-Variables/LAB-Variables.ipynb
9/4/2024 11:59 PM S02 3 Jupyterhub 02-Variables/SmallGroup-Variables.ipynb
9/6/2024 11:59 PM H02 3 Jupyterhub 02-Variables/HW-Variables.ipynb
9/10/2024 11:59 PM L03 3 Jupyterhub 03-Conditionals/LAB-Conditionals.ipynb
9/11/2024 11:59 PM S03 3 Jupyterhub 03-Conditionals/SmallGroup-Conditionals.ipynb
9/13/2024 11:59 PM H03 3 Jupyterhub 03-Conditionals/HW-Conditionals.ipynb
9/17/2024 11:59 PM L04 3 Jupyterhub 04-Iterations/LAB-Iterations.ipynb
9/18/2024 11:59 PM S04 3 Jupyterhub 04-Iterations/SmallGroup-Iterations.ipynb
9/18/2024 11:59 PM E1 23 Small Group E1: Exam 1 (Focus on Lessons 1-3)
9/20/2024 11:59 PM H04 3 Jupyterhub 04-Iterations/HW-Iterations.ipynb
9/24/2024 11:59 PM L05 3 Jupyterhub 05-Functions/LAB-Functions.ipynb
9/25/2024 11:59 PM S05 3 Jupyterhub 05-Functions/SmallGroup-Functions.ipynb
9/27/2024 11:59 PM H05 3 Jupyterhub 05-Functions/HW-Functions.ipynb
10/1/2024 11:59 PM L06 3 Jupyterhub 06-Strings/LAB-Strings.ipynb
10/2/2024 11:59 PM S06 3 Jupyterhub 06-Strings/SmallGroup-Strings.ipynb
10/4/2024 11:59 PM H06 3 Jupyterhub 06-Strings/HW-Strings.ipynb
10/8/2024 11:59 PM L07 3 Jupyterhub 07-Files/LAB-Files.ipynb
10/9/2024 11:59 PM S07 3 Jupyterhub 07-Files/SmallGroup-Files.ipynb
10/11/2024 11:59 PM H07 3 Jupyterhub 07-Files/HW-Files.ipynb
10/16/2024 11:59 PM E2 23 Small Group E2: Exam 2 (Focus on Lessons 4-6)
10/22/2024 11:59 PM L08 3 Jupyterhub 08-Lists/LAB-Lists.ipynb
10/23/2024 11:59 PM S08 3 Jupyterhub 08-Lists/SmallGroup-Lists.ipynb
10/25/2024 11:59 PM H08 3 Jupyterhub 08-Lists/HW-Lists.ipynb
10/29/2024 11:59 PM L09 3 Jupyterhub 09-Dictionaries/LAB-Dictionaries.ipynb
10/30/2024 11:59 PM S09 3 Jupyterhub 09-Dictionaries/SmallGroup-Dictionaries.ipynb
11/1/2024 11:59 PM H09 3 Jupyterhub 09-Dictionaries/HW-Dictionaries.ipynb
11/5/2024 11:59 PM L10 3 Jupyterhub 10-Pandas-I/LAB-PandasI.ipynb
11/6/2024 11:59 PM S10 3 Jupyterhub 10-Pandas-I/SmallGroup-PandasI.ipynb
11/6/2024 11:59 PM E3 22 Small Group E3: Exam 3 (Focus on Lessons 7-9)
11/8/2024 11:59 PM H10 3 Jupyterhub 10-Pandas-I/HW-PandasI.ipynb
11/12/2024 11:59 PM L11 3 Jupyterhub 11-Pandas-II/LAB-PandasII.ipynb
11/13/2024 11:59 PM S11 3 Jupyterhub 11-Pandas-II/SmallGroup-PandasII.ipynb
11/15/2024 11:59 PM H11 3 Jupyterhub 11-Pandas-II/HW-PandasII.ipynb
11/15/2024 11:59 PM P1 0 Jupyterhub project/P1.ipynb
11/19/2024 11:59 PM L12 3 Jupyterhub 12-Visualization/LAB-Visualization.ipynb
11/20/2024 11:59 PM S12 3 Jupyterhub 12-Visualization/SmallGroup-Visualization.ipynb
11/22/2024 11:59 PM H12 3 Jupyterhub 12-Visualization/HW-Visualization.ipynb
12/3/2024 11:59 PM L13 3 Jupyterhub 13-WebAPIs/LAB-Webapis.ipynb
12/4/2024 11:59 PM S13 3 Jupyterhub 13-WebAPIs/SmallGroup-Webapis.ipynb
12/6/2024 11:59 PM H13 3 Jupyterhub 13-WebAPIs/HW-Webapis.ipynb
12/6/2024 11:59 PM P2 0 Jupyterhub project/P2.ipynb
12/9/2024 11:59 PM E4 22 Small Group E4: Exam 4 (Focus on Lessons 10-12)
12/16/2024 11:59 PM P3 0 Jupyterhub project/P3.ipynb
12/16/2024 11:59 PM P4 49 Jupyterhub project/P4.ipynb

Page last revised on: 2024-08-14