Data Science Principles - 05/2025

• Course Code: EXE-DSP2025.01
• Course Duration: 5 – 10 hours per week; four weeks from May 29 – June 28, 2025
• Language: The course will be delivered in English by Harvard lecturer and in Vietnamese by Fulbright lecturer.
• Venue: Online on Harvard Online Platform; and on-campus at Fulbright School of Public Policy and Management or Online (at learner's selection)
• Course Tuition: 25,000,000 Vietnam Dong. Discount of 5% will be applied for early bird registration before March 31, 2025
• For group registration, please contact Executive Education team (email: exedu.fsppm@fulbright.edu.vn/ 091 33 55 911) for the discount information.
• Course schedule and syllabus: HERE
• Apply for the course: Download the application form HERE and send the application form to the email: Exedu.fsppm@fulbright.edu.vn no later than May 15, 2025

COURSE DESCRIPTION

Data Science Principles is an introduction to data science course for beginners and managers to better understand what data science is, how to work with data scientists, and how to positively contribute to their company's data collection and analysis efforts. Foundational topics in data science are made approachable and relevant by using real-world examples that prompt you to think critically about applying these understandings to your workplace. This course provides a nearly code- and math-free introduction to prediction, causality, visualisation, data wrangling, privacy, and ethics.

LECTURERS

Fulbright School of Public Policy and Management, Fulbright University Vietnam

• Dr. Huynh Nhat Nam

Harvard University

• Prof. Dustin Tingley

PROSPECTIVE LEARNERS 

The course is designed for early and mid-career professionals who want to understand how data is transforming industries and develop a data-driven mindset to advance their career opportunities, as well as for students and recent graduates looking to build a strong foundation in essential data-related concepts, vocabulary, skills, and business intuition to prepare for their careers.

COURSE REQUIREMENTS

Prior analytics experience would be ideal but not compulsory. The course is not intended to be mathematical intensive. Mathematical details will be kept to a minimum and, where required, will be presented only to help students better understand the analytics concepts and associated techniques that will be introduced in the course.

LEARNING OUTCOMES

By the end of this course, participants will be able to:

• Understand the importance of data collection and the factors affecting data quality.
• Recognize different data types and how data organisation impacts information extraction.
• Understand the structure of predictive algorithms and how human decisions influence their design.
• Understand the importance of establishing causal relationships and the challenges involved in different contexts.
• Understand the importance of data privacy, recognise potential violations, and evaluate existing privacy policies.
• Be aware of ethical guidelines to ensure responsible data handling and decision-making.
• Understand the importance of data transformation and wrangling, along with common technologies in the data science ecosystem.
• Explore the relationship between data science tasks, software and hardware tools, and identify potential bottlenecks in the process.
• Identify problems that algorithms can solve and the challenges of using data science tools beyond their intended purpose.
• Recognize key steps in the data science process that require auditing to ensure accuracy and ethical use.

Participants who complete all 8 modules & instructor-led sessions, including satisfactory completion of the associated quizzes, by stated deadlines, can earn the Dual Certificates from Program, in which a Certificate of Participation from Fulbright School of Public Policy and Management, and a Certificate of Completion from Harvard Online.