685.701.8VL - Data Science: Modeling and Analytics
Description
This course advances the design of data modeling as it applies to the field of data science while leveraging key concepts from AI, machine learning, and statistics. Data modeling is a combination of various fields which allow the processing of various data types, and representing the data in an expressive way that shows the relationships between data points and intrinsic patterns. The course will show how to identify, design, and implement the modeling process by outlining the framework, determining the appropriate model type, evaluating the model, and representing the outputs in an explainable way. The models used will be based on intelligent algorithms (reasoning, optimization, and pattern recognition), machine learning algorithms (supervised and unsupervised), and statistical methods (descriptive statistics, inferential statistics, multi-variate, and regression). The focus will be developing and applying models using Python-based frameworks to datasets from online resources such as Kaggle, Data.gov, and open-source repositories.
Instructor
Benjamin Rodriguez
Course Structure
The course materials are divided into 14 Modules which can be accessed by clicking Modules on the course menu. A module will have several sections including the overview, content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. The modules run for a period of seven (7) days, a wrap-up of each of the modules is contained in the Course Outline listed under the Course Information by clicking Home on the course menu. You should regularly check the Calendar and Announcements for assignment due dates. If you are taking the course in the Summer, please note that Modules 2 and 3 are combined as well as Modules 13 and 14 to ensure all 14 modules are covered during the 12 weeks for the Summer semester.
Course Topics
- Introduction to Models for Data Science and Analytics
- Data Processing I
- Data Processing II
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Deep Learning Models
- Computer Vision
- Natural Language Processing
- Evolutionary Algorithms and Models
- Decision-Making Algorithms and Models
- Hybrid Learning Models
- Explainable AI Models
- Methods of Analytics (Fall and Spring Only)
- Data Visualization and Data Storytelling (Fall and Spring Only)
Course Goals
The goal of this course is to equip students with the skills to create and evaluate AI models for a range of problems, based on the type of problem and the data available. Students will learn to interpret model predictions and outputs, and understand the implications of model decisions. They will also be able to formulate and construct supervised, unsupervised, and reinforcement learning models for computer vision and natural language processing. Additionally, they will be able to demonstrate a variety of analytics methods and build solutions to practical problems. Finally, they will be able to summarize and make use of other AI building blocks such as evolutionary algorithms, decision making algorithms, hybrid algorithms, explainable AI, and visualization.
Textbooks
Not required (Optional):
Deisenroth, Marc P., Faisal, A. A., and Ong, Cheng S., Mathematics for Machine Learning, Cambridge University Press, 2019
Other Materials & Online Resources
Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006, https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop- Pattern-Recognition-and-Machine-Learning-2006.pdf.
- David Poole and Alan Mackworth, Artificial Intelligence Foundations of Computational Agents, Cambridge, 2017, https://artint.info/
Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016, https://www.deeplearningbook.org/.
Mykel J. Kochenderfer and Tim A. Wheeler, Algorithms for Optimization, MIT Press, 2019, https://algorithmsbook.com/optimization/files/optimization.pdf.
Required Software
In this class examples may be given in Python (consider the example code provided to be a guide), students in the Data Science and Artificial Intelligence programs mainly use Python.
Student Coursework Requirements
Grading consists of
- Research/Discussions (20%)– Over the semester, student generated, students will post research/resources discussions from module topics
- Homework Assignments (40%) - 4 assignments related to the module learning objectives
- Programming Assignments (40%) - 2 assignments covering the module learning objectives
Grading Policy
In this course we do not grade on a curve for the final grade.
In this course a +/- grading system is used. The assignments are graded on a curve, the final grade will not be curved or rounded up.
| Score Range | Letter Grade |
|---|---|
| 100-98 | = A+ |
| 97-94 | = A |
| 93-90 | = 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 (https://ep.jhu.edu/student-services/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. 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 Student Disability Services at Engineering for Professionals, 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 following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/
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.