Data Science and AI Principles is a nearly code- and math-free introduction to the foundational ideas behind data science and the AI technologies it powers, including machine learning and large language models. It makes concepts such as prediction, causality, data wrangling, privacy and ethics approachable through real-world examples, and prompts you to think critically about how these ideas apply to your own workplace.
This is an official Harvard Online course, featuring faculty from the Harvard Faculty of Arts & Sciences and delivered on Harvard Online's learning platform. IPMC International handles your registration, payment and local support, then enrols you on the course. The format is self-guided within a scheduled cohort, and the Certificate of Completion is issued by Harvard Online.
The course builds three connected sets of skills:
The programme is taught through real case studies. There are no prerequisites, and no mathematics or programming experience is required. To earn the Certificate of Completion from Harvard Online, participants complete all modules and the associated assignments by the stated deadlines.
With Professor Dustin Tingley and Mauricio Santillana, explore why data collection matters, what affects data quality, that not all data is numerical, and how the organisation of data shapes what you can learn from it.
Using a sepsis-prediction case with Craig Umscheid of University of Chicago Medicine, learn the basic structure of a predictive algorithm, where human decisions shape it, and how to evaluate its success.
Study the Google Tax case to see why establishing causal relationships matters, the barriers to doing so, and why randomisation can help establish cause while creating other problems.
With Latanya Sweeney, explore why data privacy matters, what can constitute a violation, how to critique privacy policies, and how to set ethical tenets to guide data work in your own organisation.
Identify sources of non-numerical data and why it is useful, and understand the difference between supervised and unsupervised learning and where neural networks apply.
Using a case on reducing food waste, describe common data science algorithms such as regression, why they grow more complex, the human role in overseeing them, and the trade-offs of more sophisticated models.
Explain the importance of data transformation and wrangling, list common technologies used in data science, and connect data science tasks to software and hardware tools while spotting potential bottlenecks.
Explain how AI helps organisations move from reactive to proactive problem-solving, map AI capabilities to real business tasks, assess data readiness, and propose leadership strategies that balance innovation, safety and ethics.
Work on a health care prioritisation case to recognise problems an algorithm might solve, the challenges of using tools outside their intended use, and the steps in the process that need auditing.
Dustin Tingley: Your instructor. A data scientist at Harvard, the Thomas D. Cabot Professor of Public Policy with a joint appointment in the Harvard Kennedy School and the Government Department, and Deputy Vice Provost for Advances in Learning, where he helps direct Harvard's education-focused data science and technology team.
Learners also hear from industry experts including Mauricio Santillana (Harvard professor and faculty member at Boston Children's Hospital), Latanya Sweeney (Director of the Data Privacy Lab in IQSS at Harvard) and Dan Restuccia (Chief Product and Analytics Officer, Burning Glass Technologies). Affiliations are listed for identification purposes only.
The course works at the conceptual level with no prerequisites, so it is particularly good for people who aren't data scientists but who work alongside them as managers, decision-makers or colleagues. It also suits graduates and organisations who want a globally recognised Harvard Online credential to advance their careers or capabilities.
On successful completion you receive a digital Certificate of Completion from Harvard Online. No letter grades are assigned; participation is evaluated on a complete/incomplete basis. Once earned, you can add it to your CV and LinkedIn. For example, under LinkedIn "Licenses & Certifications" list Harvard Online as the issuing organisation and the certificate for Data Science and AI Principles as the credential.
The course fee is confirmed on enquiry and put in writing before payment.
Pay IPMC securely by debit/credit card in UAE Dirhams (UAE) or by UPI, card or net banking in Indian Rupees (India).
Yes. The course is delivered by Harvard Online and the Certificate of Completion is issued by Harvard Online.
Yes. It is designed for people who need to work with data scientists and AI systems as managers, decision-makers or colleagues. No coding or advanced maths background is required.
You complete all modules and assignments thoughtfully by the stated deadlines and contribute to discussions on the platform. You set your own pace within the cohort. No letter grades are given. Your work is assessed on a complete or incomplete basis.
After the final deadline, coursework is reviewed and, once eligibility is confirmed, your certificate is delivered by email. You keep access to the materials for 60 days after the final deadline.
This course runs as a scheduled Harvard Online cohort. The next cohort starts 26 Aug 2026. Contact us to reserve your place.
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