Aim: A three day hands-on course in data analysis for life sciences using R.

Participants: PhD students and researchers working in various areas of life sciences who use statistics, data analysis methods and algorithms for the in-depth analysis of data.

Prerequisites: Knowledge of basic mathematics and basic statistics is a must. The participant should have some exposure to basic statistics. Prior experience in working with programming languages will definitely be an added advantage, but is not necessary for joining this training program. Most of the programming ideas employed in R framework will be taught in the course. Those who have some experience in performing statistical analysis in R are encouraged to apply.

Methodology: The entire course is hands-on with the participant learning the data analysis methods in R under direct, personal interaction with the trainer.

For every topic, theory will be explained in detail followed by hands-on tutorial session in R with real data sets.

The candidate will be made to analyze more data sets for further practice.

Example data sets and other necessary material will be given to the candidates before the course using standard procedures followed by the organization.

Syllabus:

Part-A. Quick learning/Review of R
A1. Getting started
A2. Data structures
A3. Programming aspects
A4. File read/write operations
A5. Graphical representation of data: R graphics package
Part-B. Data analysis in R
B1. Statistical parameters
B2. Discrete and continuous probability distributions
B3. Errors analysis
B4. Fundamentals of hypothesis testing
B5. Parametric and non-parametric statistical tests
B6. Linear and non-linear regression
B7. Clustering analysis methods

Duration: Three days, each day 7 hours of class.

Number of participants: Maximum twenty.

Logistics: Standard meeting/training hall with computer tables and connections for the participants, white board and overhead projector. Internet connection.

R statistical package installed in the computers used by the participants.