685.662.81 - Data Patterns and Representations

Course Structure

• Weekly Module Activities – provide a continuous opportunity for students to initially explore a vast number of tools to solve a problem through visualization with the opportunity to collaborate with other students and inspire future efforts.
  
  
• Module Review Questions – weekly ‘quizzes’ to ensure that students are practicing retrieval of the key concepts/themes addressed in each module, to ensure that students are developing the right intuition for effective visualization.
  
  
• HW Assignments (4 total)- the HW assignments will provide students the ability to practice each of the principles and tools curated through the course, each assignment will focus on a particular tool and application of data visualization which will tentatively include the following:
  • Python/R for exploratory data analysis, statistical analysis, and model development
  • Python libraries for advanced visualization and user interface and experience (matplotlib, plotly, seaborn, statistical analysis, streamlit)
  • Tableau for data dashboarding and effective storytelling
    
    
• Final Course Project (pairs) – The course project will provide students with the opportunity to work collaboratively with a peer that should incorporate all aspects of the course.
  • Projects will be live, presentation style approximately 15-20 mins in length.
  • Students will select a dataset that is of relevance to their industry or a given interest.
  • The project will follow a structured approach aligned with the course modules:

                   Phase 1: Data Analysis

Students will act as data analysts to explore and understand their dataset. This includes summarizing key characteristics of the data using simple statistics and descriptive analyses to identify trends, anomalies, and relevant features.

Phase 2: Data Visualization and Modeling

In this phase, students will apply advanced visualization techniques to explore their dataset further. They will develop visualizations that effectively convey their findings and apply modeling techniques from the course (e.g., supervised, unsupervised, or multi-modal approaches) to generate insights, predictions, or classifications relevant to their data.

Phase 3: Data Storytelling

Students will synthesize their findings into a compelling narrative. They will design a comprehensive dashboard or presentation that incorporates effective storytelling principles, ethical considerations, and accessibility to ensure their insights are tailored to both technical and non-technical stakeholders.

Course Topics

• Data Storytelling
• Data Visualization
• Human Centered Design
• Comprehensive Storytelling
• Applied Storytelling
• Data Representations
• Images/Video
• Time Series
• Natural Language Processing
• Audio
• Networks
• Explainable
• Multi-Modal
• Ethical Considerations

Course Goals

By blending storytelling with technical expertise, this course equips students to navigate complex datasets, derive insights, and communicate them with clarity and purpose.

Course Learning Outcomes (CLOs)

• Apply principles of data storytelling to develop and communicate narratives that effectively convey insights and engage specific audiences.
• Create clear and compelling visualizations tailored to various data types, using appropriate tools and techniques to enhance understanding and impact.
• Demonstrate the ability to preprocess, transform, and prepare data for analysis across different modalities, ensuring its readiness for modeling.
• Analyze diverse data patterns and representations, including natural language, audio, visual, network, and sequential data, to uncover meaningful insights.
• Synthesize multiple data modalities into multimodal representations, integrating them to address complex, real-world problems.
• Design explainable AI models that prioritize interpretability and transparency, enabling effective communication of model insights.
• Evaluate ethical considerations in data visualization, ensuring transparency, fairness, and trustworthiness in all representations.

Textbooks

Storytelling With Data - ISBN 9781119002253
Introduction to Data Visualization Storytelling: A Guide for The Data Scientist - ISBN 9798772827727

Optional:

Pattern Recognition and Machine Learning by Bishop, Christopher M. - ISBN 9780387310732 - https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf

Required Software

In this course we will be asking you to download and install various software to obtain hands-on experience in a multitude of different data visualization platforms including but not limited to:

Python
Tableau

All software that is not open source, a license will be provided to you during the course.

Student Coursework Requirements

Each module is expected to take about 7–10 hours per week to complete. Here is an approximate breakdown: additional reading as assigned by instructors (1-2 hours per week), attending virtual live session (3 hours per week), and assignments/module discussion (3-5 hours per week).

This course will consist of the following basic student requirements:

Module Activity (15% of Final Grade Calculation)

Every week there will be a module activity that should be completed and submitted to the discussion board prior to the next scheduled lecture. The participation grading criteria is as follows:

Full Credit (100 points):

• Submit your initial post by Day 5.• Respond to at least two other posts by Day 7.

• Half Credit (50 points):

• If your initial post is late but you respond to two other posts.• If your initial post is on time but you fail to respond to at least two other posts.

• No Credit (0 points):

• If both your initial post and responses are late.• If you fail to submit an initial post and do not respond to any others.

Additional Notes:

• Late Initial Posts: Late posts will automatically receive half credit if two responses are completed on time.• Substance Matters: Responses must be thoughtful and constructive. Comments like “Great post!” or “I agree!” without further explanation will not earn credit.• Balance Participation: Aim to engage with threads that have fewer or no responses to ensure a balanced discussion.

Avoid:

• A number of posts within a very short time-frame, especially immediately prior to the posting deadline.
• Posts that complement another post, and then consist of a summary of that

Module Review Questions (15% of Final Grade Calculation)

Every week you will have 7 review questions that are posted in Canvas for you to answer. This is automatically scored and gives you three chances to complete with no penalty for re-taking. You are allotted 60 minutes per attempt. 

Assignments (40% of Final Grade Calculation)

Assignments will include a mix of qualitative assignments (e.g. literature reviews, model summaries), quantitative problem sets, and case study updates. Include a cover sheet with your name and assignment identifier. Also include your name and a page number indicator (i.e., page x of y) on each page of your submissions. Each problem should have the problem statement, assumptions, computations, and conclusions/discussion delineated. All Figures and Tables should be captioned and labeled appropriately. Refer to assignment specific instructions as assignment types will vary.

All assignments are due according to the dates in the Calendar.

Late submissions will be reduced by one letter grade for each week late (no exceptions without prior coordination with the instructors).

If, after submitting a written assignment you are not satisfied with the grade received, you are encouraged to redo the assignment and resubmit it. If the resubmission results in a better grade, that grade will be substituted for the previous grade.

Qualitative assignments are evaluated by the following grading elements:

1. Each part of question is answered (20%)
2. Writing quality and technical accuracy (30%) (Writing is expected to meet or exceed accepted graduate-level English and scholarship standards. That is, all assignments will be graded on grammar and style as well as content.)
3. Rationale for answer is provided (20%)
4. Examples are included to illustrate rationale (15%) (If you do not have direct experience related to a particular question, then you are to provide analogies versus examples.)
5. Outside references are included (15%)

Qualitative assignments are graded as follows:

• 100–90 = A—All parts of question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [rich in content; full of thought, insight, and analysis].
• 89–80 = B—All parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [substantial information; thought, insight, and analysis has taken place].
• 79–70=C—Majority of parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [generally competent; information is thin and commonplace].
• <70=F—Some parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [rudimentary and superficial; no analysis or insight displayed].

Quantitative assignments are evaluated by the following grading elements:

1. Each part of question is answered (20%)
2. Assumptions are clearly stated (20%)
3. Intermediate derivations and calculations are provided (25%)
4. Answer is technically correct and is clearly indicated (25%)
5. Code should run smoothly and visual elements rendered (10%)

Quantitative assignments are graded as follows:

• 100–90 = A—All parts of question are addressed; All assumptions are clearly stated; All intermediate derivations and calculations are provided; Answer is technically correct and is clearly indicated; Answer precision and units are appropriate.
• 89–80 = B—All parts of question are addressed; All assumptions are clearly stated; Some intermediate derivations and calculations are provided; Answer is technically correct and is indicated; Answer precision and units are appropriate.
• 79–70=C—Most parts of question are addressed; Assumptions are partially stated; Few intermediate derivations and calculations are provided; Answer is not technically correct but is indicated; Answer precision and units are indicated but inappropriate.
• <70=F—Some parts of the question are addressed; Assumptions are not stated; Intermediate derivations and calculations are not provided; The answer is incorrect or missing; The answer precision and units are inappropriate or missing.

Course Project and Presentation (30% of Final Grade Calculation)

A course project will be assigned several weeks into the course. The next-to-the-last week will be devoted to the course project.

The course project is evaluated by the following grading elements:

1. Student preparation and participation (as described in Course Project Description) (40%)
2. Student technical understanding of the course project topic (as related to individual role that the student assumes and described in the Course Project Description) (20%)
3. Team preparation and participation (as described in Course Project Description) (20%)
4. Team technical understanding of the course project topic (20%)

Course Project is graded as follows:

• 100–90 = A—Student Preparation and Participation/ Team Preparation and Participation [individual/ team roles and responsibilities well defined and understood; individual/ team well versed in use of applicable software tools; individual/ team work product(s) agreed to, well prepared and available to all team members/ instructors]; Student Understanding/ Team Understanding [rich in content; full of thought, insight, and analysis].
• 89–80 = B—Student Preparation and Participation/ Team Preparation and Participation [individual/ team roles and responsibilities well defined and understood; individual/ team well versed in use of applicable software tools; individual/ team work product(s) agreed to and prepared]; Student Understanding/ Team Understanding [substantial information; thought, insight, and analysis has taken place].
• 79–70 = C—Student Preparation and Participation/ Team Preparation and Participation [individual/ team roles and responsibilities agreed to; individual/ team well versed in use of applicable software tools; individual/ team work product(s) prepared]; Student Understanding/ Team Understanding [generally competent; information is thin and commonplace].
• <70 = F—Student Preparation and Participation/ Team Preparation and Participation [individual/ team roles and responsibilities not well understood; individual/ team has difficult with use of applicable software tools; individual/ team work product(s) partially prepared]; Student Understanding/ Team Understanding [rudimentary and superficial; no analysis or insight displayed].

Item	% of Grade
Preparation and Participation	20%
Assignments	40%
Course Project and Presentation	30%
Module Review Questions	10%

Grading Policy

EP uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10).

Score Range	Letter Grade
100-97	= A+
96-93	= A
92-90	= A−
89-87	= B+
86-83	= B
82-80	= B−
79-77	= C+
76-73	= C
72-70	= C−
69-67	= D+
66-63	= D
<63	= F

Academic Policies

Deadlines for Adding, Dropping, and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar. Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course. 

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students. This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Our courses are designed with a proactive approach to accessibility to minimize the need for disability disclosure and accommodation requests, but we recognize that you may need additional support. Students with disabilities (including those with psychological conditions, medical conditions, and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.  For further information or to start the process of requesting accommodations, please contact EP Student Disability Services at ep-disability-svcs@jhu.edu. 

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. For a full description of the code please visit the Student Conduct Code website.

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team (EP-Registration@exchange.johnshopkins.edu) in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.