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Computer Programming



R is a programming language used for statistical analysis and visual representation of data. It was developed by Ross lhaka and Robert Gentleman at the University of Auckland, New Zealand, and the R Development Core Team currently develops it. It is an open-source free software that is often used for statistical computing and graphics. R is an interpreted and highly extensible language with a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc.) and graphical techniques.


With this course, we, at The Nature’s Eye, aim to teach the current use of R programming for conducting scientific analyses.

About the speaker:

Mr. Chandan Pandey is a data scientist currently working as a research assistant at the Centre for Wildlife Studies, Bangalore. He has an extensive working knowledge of both python and R programming languages. Previously he has worked as an ecosystem ecologist, biotechnology engineer, and host-parasite system.

Course Structure:

The course is divided into three levels:

  1. Beginner: In this model, a concept of programming as well as an understanding of the terminologies of R programming will be taught.

  2. Intermediate: In this model, we will learn about how to conduct simple and classical statical analyses using R.

  3. Advanced: Here we will use all the skills to conduct an advanced statical analysis such as mixed effect modeling, experiment design, Matrix multiplication, and multivariate analysis. 

Beginner: (24th - 28th April)

  • Introduction to R and R studio, Why R?

  • Concept of variable, operator, data type/class in R.
    Concept of objects.

  • Conditional statements, loops, and functions.

  • Introduction to R package and notebook.

  • Plot, import, and export data in R.

Intermediate: (01st - 05th May)

  • Descriptive statistics and measurements in Statistics.

  • Hypothesis testing, distributions, Parametric and non-parametric tests.

  • Linear regression with one parameter.

  • Linear regression with multiple parameters.

  • Randomization in R for study design.

Advanced: (08th - 12th May)

  • Linear mixed effect model.

  • Generalized mixed effect model.

  • Simulation studies in R.

  • Solving Matrix related problems in R with the example of Leslie matrix.

  • Multivariate analysis and graphs in R.

Course Details:

  • Date:

    • Beginner: 24 April 2023 – 28 April 2023

    • Intermediate: 01 May 2023 – 05 May 2023

    • Advanced: 08 May 2023 – 12 May 2023

  • Time: 7:00 pm – 9:00 pm

  • Platform: Zoom

  • Registration fee: ₹1200/- (for each level)

  • Complete course registration fee: ₹3300/-

  • Session recordings will be provided to all the participants.

  • Lecture notes and recommended readings will be provided to the participants.

  • Essential papers/articles and additional suggestions for books to read will be provided.

  • E-certificates will be provided based on the assignments submitted by the participants.

  • For further queries:
    Call/WhatsApp: 9771185438

Please note:

  • Assignments and projects will be assigned at the end of each module.

  • Participants will be evaluated based on their submitted works and their participation during the sessions.

  • No single textbook is followed in this course.

  • Basic understanding of statistics and its formulas is a pre-requisite. Reading material will be provided for the revision of these concepts.

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