Tomorrow’s dawn gifts us the fruits of today’s actions.
Our actions have transformed the planet Earth much more than what four generations of our ancestors together have! The gifts that time has, in store for us will more likely be unpleasant. However, the meteoric rise in the ability to handle and process data; extract more information, and communicate appropriate strategies in almost-real time, at a planetary scale presents a window of opportunity that can reduce future harshness.
Tomorrow’s environmental scientists and conservation champions must embrace disruptive technologies as a norm and not an exception, lest they miss the opportunity. Globally, Environmental Science is at the cusp of dramatic metamorphosis.
The International Certificate Program in Environmental Data Analytics (ICPEDA) is designed to teach data analytics in Python. It will provide a first-hand experience in handling and analyzing environmental data and interpreting and presenting the results.
While prior knowledge of Python will be an advantage, it is not a pre-requisite to register and gain from the course. ICPEDA will elevate the skill level of postgraduate students, doctoral scholars and early career researchers, who look forward to a career in environmental/ conservation sciences. The course is also an excellent cross-over bridge for physical science researchers who want to explore greener pastures in interdisciplinary domains.
Week | Module | Activities | Assignment |
---|---|---|---|
Week 1 | Introduction to the course Revisiting Statistics-1 |
|
Read article-1 Lab-1 |
Week 2 | Data and data types |
|
Lab-2 Quiz-1 |
Week 3 | Revisiting Statistics-2 |
|
Lab-3 Quiz-2 Read article-2 Assignment-1 |
Week 4 | The world of programming |
|
Lab-4 Quiz-3 |
Week 5 | Fundamentals of Python programming |
|
Lab-5 Quiz-4 |
Week 6 | Python for analytics |
|
Lab-6 |
Week 7 | Python for analytics |
|
Lab-7 |
Week 8 | My Python program! |
|
Lab-8 Assignment-2 |
Week 9 | Building up my Python skills |
|
Lab-9 Quiz-5 |
Week 10 | Python based Environmental Data Analytics (EDA) |
|
Lab-10 Assignment-3 Reading |
Week 11 | EDA Case Study with Python |
|
No assignment |
Week 12 | Assessment Week |
|
End of the course |
Week 1 | 30-Nov-20 | 01-Dec-20 | 2 | 3 | 4 |
Week 2 | 7 | 8 | 9 | 10 | 11 |
Week 3 | 14 | 15 | 16 | 17 | 18 |
Week 4 | 21 | 22 | 23 | 28 | 29 |
Week 5 | 04-Jan-21 | 5 | 6 | 7 | 8 |
Week 6 | 11 | 12 | 13 | 14 | 15 |
Week 7 | 18 | 19 | 20 | 21 | 22 |
Week 8 | 25 | 27 | 28 | 29 | 30 |
Week 9 | 01-Feb-20 | 2 | 3 | 4 | 5 |
Week 10 | 8 | 9 | 10 | 11 | 12 |
Week 11 | 15 | 16 | 17 | 18 | 19 |
Week 12 | 22 | 23 | 24 | 25 | 26 |
E-mails will be the mode of all announcements and communications. Hence, frequently checking e-mail is required. Participants must use their registered e-mail account for communication.
Attendance and etiquettes in the classroomRegular attendance and active class participation are required. Absence in the classroom may affect the final grade. Inform in advance if the participant misses a class due to any legitimate reason. No texting or web-browsing in the class unless instructed by the instructor.
Academic integrity and plagiarismStudents are expected to be honest in all of their academic work. If you are using ideas or words from other students, published writers, or any other sources, you should cite the author or the citation in the text and provide a list of references. The class will have zero-tolerance for any form of plagiarism.