Designed for scientists and engineers in technical leadership positions, this course provides an immersive introduction to data science and data management concepts for data-driven organizations. Through lectures, hands-on exercises, and project assignments, the course walks students through the full data science ecosystem: data management and wrangling, AI and machine learning methods, statistical fundamentals, commercial AI/ML tooling, solution deployment, and result evaluation. Students will learn the terminology and skills necessary to effectively lead the teams and processes behind data-driven work — from problem formulation and project design through operational delivery. Policy, governance, and the ethical dimensions of Data Science and AI round out the curriculum, equipping leaders to make decisions that are not only technically sound but organizationally defensible.This course explores the use of emerging AI technologies, such as Generative AI, throughout the data science process. Although there is a temptation to rely on Generative AI as a primary analytical tool, effective use of these capabilities depends on a working understanding of the data science and data engineering fundamentals underneath them. Leaders who have developed that foundation are better positioned to validate AI-enabled results, recognize the influence of data quality on outputs, and exercise the independent judgment needed to determine when those results can be trusted.Familiarity with desktop operating systems and software is required. Only basic coding familiarity is needed, as the emphasis throughout is on leadership judgment, critical evaluation, and decision-making across the AI and data science process.
The course materials are divided into 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. Modules run for 7 days You should regularly check the Calendar and Announcements for assignment due dates; some modules may have multiple parts due on different days, and a few modules allow for more than one week to complete portions of the assignment.
There is no required single textbook for this course, due to the diversity of topics and the survey nature of the course. Throughout the course, students will be assigned to read almost all the sections of the book "Data Science" by Kelleher and Tierney, 2018, MIT Press (237 pages). A link to specific sections will be provided with each reading assignment.
This book is available as an e-book through the JHU library at: Kelleher J.D., & Tierney, B. (2018). Data science. MIT Press [https://catalyst.library.jhu.edu/catalog/bib_7262400.]
It is also available for purchase at Amazon for usually under $10, here: Amazon.
Articles are available at provided links in the reading assignments. Some of these links connect you with Oreilly Online, which you should have access to through your JHU library privileges.
If you have trouble accessing an article please first try the "eReserves" menu item from the left menu, and then contact the instructors for assistance.
There is one reference - Toomey - which is available as a PDF on the ereserves link.
Students are expected to complete each module in order. Some modules have an assignment or a project, a discussion, and a self-check quiz.
The final grade is weighted as follows:
Most assignments include a rubric with the following structure: 95% of the grade is focused on demonstrating understanding of the content, and an additional 5% goes toward demonstration of deeper insights and applications of the content, and effective integration with other course topics.
A final project evaluation form will be provided in Module 14.
The submission of this form is mandatory for completion of the course.
Assignment Timeliness of Submission Policy:
This course is designed as an immersive experience, with both passive learning and active learning modalities. Students are expected to participate in a timely manner, particularly as both discussions and group work depend on everyone being involved. As such, late submissions will be graded as follows:
After 1 week due date – 25% reduction in earned credit*
After 2 weeks – 50% reduction in earned credit
After 3 weeks late – 75% reduction in earned credit
After 4 weeks - No assignment credit
We recognize that students may encounter unexpected and extenuating circumstances. Students are encouraged to contact the professors as soon as possible in these cases to request an extension.
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
Students with Disabilities - Accommodations and Accessibility
Student Conduct 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.