Master Python coding through hands-on research projects, from data analysis to scientific computing - build practical skills for real applications.
Master Python coding through hands-on research projects, from data analysis to scientific computing - build practical skills for real applications.
Elevate your Python skills from introductory to advanced level with Harvard's comprehensive course. Bridge the gap between basic Python knowledge and its application in research settings. Review Python 3 basics before diving into specialized research tools like NumPy and SciPy. Explore statistical learning with scikit-learn and tackle diverse case studies across scientific disciplines. Gain hands-on experience applying Python to real-world research scenarios.
4.2
(51 ratings)
3,48,570 already enrolled
Instructors:
English
Arabic, German, English, 9 more
What you'll learn
Review and reinforce Python 3 programming basics
Master essential Python tools for research, including NumPy and SciPy
Apply Python skills to diverse case studies across scientific disciplines
Explore statistical learning techniques using the scikit-learn library
Develop practical coding skills for real-world research applications
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
Closed caption
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by

Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.





There are 4 modules in this course
This course is designed to advance Python programming skills for research applications. It begins with a review of Python 3 basics and then introduces essential scientific computation modules like NumPy and SciPy. The curriculum includes six case studies from various disciplines, allowing students to apply their skills to real-world research scenarios. A new module on statistical learning using scikit-learn has been added, enhancing the course's relevance for data-driven research. Throughout the course, students will practice applying Python tools to practical research settings, bridging the gap between introductory programming and advanced research applications.
Week 1: Python Basics
Module 1
Week 2: Python Research Tools
Module 2
Weeks 3 & 4: Case Studies
Module 3
Week 5: Statistical Learning
Module 4
Fee Structure
Instructor

1 Course
Pioneer in Digital Phenotyping and Network Science
Professor Jukka-Pekka "JP" Onnela serves as Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and Co-Director of the Master in Health Data Science program. Born in Oulu, Finland in 1976, he earned his doctorate in network science from Helsinki University of Technology (now Aalto University) in 2006, where his dissertation received the university's Dissertation of the Year award. His academic journey included positions as a Junior Research Fellow at Oxford University, a Fulbright Scholar at Harvard Kennedy School, and a postdoctoral fellow at Harvard Medical School before joining Harvard Chan School in 2011. His groundbreaking research focuses on statistical network science and digital phenotyping, introducing the concept of "moment-by-moment quantification of individual-level human phenotype using personal digital devices." He directs the Onnela Lab, which developed the open-source Beiwe Research Platform for smartphone-based digital phenotyping, and received the prestigious NIH Director's New Innovator Award in 2013 for his pioneering work in digital phenotyping. His research has revolutionized the use of cell phone data to study human social behavior and its connections to health outcomes.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
4.2 course rating
51 ratings
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.