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International Certificate Program in Environmental Data Analytics (ICPEDA)

Last date for Registration : 27 November 2020
Attention registered participants: We will open the payment gateway to transfer the course fees this week. Please lookout for an intimation in your mailbox.

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.


This course covers the fundamentals and application of Python in analyzing environmental data

Course Objectives

  • Recap of statistical concepts (measures of central tendency, dispersion: when to use what).
  • Learn to represent data graphically (Histograms, Boxplot, Line graphs, Maps, etc.)
  • Statistical analysis (standard error, confidence intervals, t-test, correlation, linear model)
  • Data quality challenges and solutions
  • Understand important programming languages
  • Understand lists, sets, dictionaries, conditions and branching, objects and classes in Python
  • Initiate your python program (using variables, strings, functions, loops, and conditions)
  • First-hand experience in data handling in Python (reading, writing files, loading, working, etc.)
  • Interpreting results (in Python - includes multi-dimensional arrays in NumPy, manipulate Data Frames, use SciPy library of mathematical routines, and execute machine learning using Scikit-Learn)
  • Data analytics using popular Python libraries
Course Delivery
Online Hybrid (synchronous (live) and asynchronous online/ offline guided sessions)
Course Duration
12 Weeks (30 Nov 2020- 22 Feb 2021)
11 Weeks instruction and 1-week assessment
Geographic Scope
Global
Time Commitment
1+4 Hours/Week
Who Should Attend
Graduates, Post Graduates, Researchers, Professionals
Learning Outcomes
Upon successfully completing the course, participants will carry out the fundamental statistical analysis of environmental data. They will gain confidence and be initiated to Python and environmental data analytics using Python.
Course Fee
INR 5,000 + 18% GST – Resident Indians
INR 7,000 – Non Resident Indians /International

Program Coordinator


Dr. R Jaishanker
Professor, C V Raman Laboratory of Ecological Informatics IIITM-K,
Thiruvananthapuram, Kerala, India.

Course Instructor


Dr. Sushant Singh
Head of Artificial Intelligence Competency of
Health Care and Life Science
Virtusa Corporation, MA, USA.

Course outline and Schedule

Week Module Activities Assignment
Week 1 Introduction to the course
Revisiting Statistics-1
  • Get to know each-other
  • Course introduction
  • Class readiness
  • Introduction of environmental data analytics
  • Job opportunities in environmental data analytics
Read article-1
Lab-1
Week 2 Data and data types
  • Data types in statistics
  • Data visualization
Lab-2
Quiz-1
Week 3 Revisiting Statistics-2
  • Fundamentals of statistics
  • Probability distribution
  • Hypothesis testing
  • Univariate analysis
  • Bivariate analysis
Lab-3
Quiz-2
Read article-2
Assignment-1
Week 4 The world of programming
  • Python
  • R
  • SQL
  • Introduction to Jupyter Notebooks and installation
Lab-4
Quiz-3
Week 5 Fundamentals of Python programming
  • Good scripting practices
  • Importing and exporting data in Python
  • Data types and operators
  • Data structure
  • Control flow
  • Functions
Lab-5
Quiz-4
Week 6 Python for analytics
  • Python packages for environmental data analytics
  • Data preprocessing
Lab-6
Week 7 Python for analytics
  • Descriptive data analysis in Python
Lab-7
Week 8 My Python program!
  • Develop a simple Python program
Lab-8
Assignment-2
Week 9 Building up my Python skills
  • Project topic discussion
  • Project report structure
  • Citation and Referencing
Lab-9
Quiz-5
Week 10 Python based Environmental Data Analytics (EDA)
  • Understanding Python application in EDA
  • Learn existing case studies
Lab-10
Assignment-3
Reading
Week 11 EDA Case Study with Python
  • Develop own case study
No assignment
Week 12 Assessment Week
  • Submission of report
End of the course

Program Calendar

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
  • Dates in black font - Instructor led sessions (7.30 - 8.30 AM Indian Standard Time- IST):
  • Dates in white font - Teaching Assistant guided sessions - 9.30 AM to 12.30 PM and 2.00 PM to 4.30 PM IST. [specific time to be scheduled independently by participants over IIITM-K Learning Management System].
  • Dates in white font - Teaching Assistant guided sessions - 9.30 AM to 12.30 PM and 2.00 PM to 4.30 PM IST. [specific time to be scheduled independently by participants].
  • Dates in yellow font - Assessment Week

Assessment and Grading
  1. Assignment (Theory): 20%
  2. Assignment (Lab): 30%
  3. Quiz: 20%
  4. Final Project Report: 30%
Grades
A: 91 to 100%  B: 81 to 90%
C: 71 to 80%  D: 61 to 70%
Pass : 21 - 60%
Participation without grade 0-50%

Course Policies

Communication

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 classroom

Regular 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 plagiarism

Students 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.


Teaching Assistants :
  1. Sajeev C Rajan, Doctoral Fellow, C V Raman Laboratory of Ecological Informatics, IIITM-K
  2. Lijimol Dominic, Research Fellow, C V Raman Laboratory of Ecological Informatics, IIITM-K
  3. Minu Merin Sabu, MPhil Scholar, C V Raman Laboratory of Ecological Informatics, IIITM-K
Upcoming Certificate Programs (in Summer 2021)
  1. International Certificate Program in Geospatial Environmental Analysis (ICPEGA)
  2. International Certificate Program in Environmental Data and Information Management (ICPEDIM)
  3. Competency in Environmental Data Analysis using JMP. (CEDA)

Launching in 2021
Master of Science in Environmental Science with Specializations in
  1. Environmental Data Analytics
  2. Geospatial Technology
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