Data Analytics using R

About the Course

Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, reaching conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, while being used in different business, science, and social science domains. The most popular package for researchers in Science for data analysis today is R. R is an open source programming language and software environment for statistical computing and graphics that is supported by the R Foundation for Statistical computing. The R language is widely used among researchers in Science for data analysis and for statistical modeling. The workshop is designed to orient Faculty members and Researchers in data analysis and programming from medium to advanced level. The program shall be a mix of imparting knowledge through programming in R in Meta Analysis, Analysis of Categorical Data andBayesian Data Analysis. The prime concern of this workshop is to showcase the participants the process of analysis of data in R with basic focus concentrating on Meta analysis and Bayesian data analysis. Starting from medium level to some of the advanced statistical techniquesboth through R-programming and R-commands shall be on display in the workshop. For Bayesian data analysis Winbugs shall also be covered. At the end of the workshop, participants should be able to:

• Get familiar with R programming language;
• Understand statistical analysis using R programming;
• Explore how to analyze research data using R programming;
• Explore how to represent data using basic graphs in R.

Objective of Training

The program shall be operated through some recent advances in Statistical Research with main focus on R-programming and fundamental data analysis in R. As the applicants are expected to have some theoretical back ground in Statistics so the main focus beyond the research advances shall be on hands on training in R and practical lab sessions in R. The design of the program is to inform the participants on the following topics: 

• Data management in R
• Descriptive Statistics and Graphics in R
• Categorical Data Analysis in R.
• Meta Analysis in R.
• Bayesian Data Analysis in R.

Topics to be Covered in Workshop  

• Overview
  • History of R
  • Advantages and disadvantages
  • Downloading and installing
  • How to find documentation
• Introduction
  • Using the R console
  • Getting help
  • Learning about the environment
  • Writing and executing scripts
  • Saving your work
• Installing Packages
  • Finding resources
  • Installing resources
• Data Structures, Variables
  • Variables and assignment
  • Data types
  • Indexing, subsetting
  • Viewing data and summaries
  • Naming conventions
  • Objects
• Getting Data into the R Environment
  • Built-in data
  • Reading data from structured text files
  • Reading data using ODBC
• Control Flow
  • Truth testing
  • Branching
  • Looping
  • Vectorized calculations
• Functions in Depth
  • Parameters
  • Return values
  • Variable scope
  • Exception handling
• Handling Dates in R
  • Date and date-time classes in R
  • Formatting dates for modeling
• Descriptive Statistics
  • Continuous data
  • Categorical data
• Inferential Statistics
  • Bivariate correlation
  • T-test and non-parametric equivalents
  • Chi-squared test
  • Distribution testing
  • Power testing
• Group By Calculations
  • Split apply combine strategy
• Base Graphics
  • Base graphics system in R
  • Scatterplots, histograms, barcharts, box and whiskers, dotplots
  • Labels, legends, Titles, Axes
  • Exporting graphics to different formats
• Advanced R Graphics: GGPlot2
  • Understanding the grammar of graphics
  • Quick plot function
  • Building graphics by pieces
• Linear Regression
  • Linear models
  • Regression plots
  • Confounding / Interaction in regression
  • Scoring new data from models (prediction)
• Categorical Data Analysis in R
• Predictive & Prescriptive Analytics
• Bayesian Data Analysis in R
• Meta Analysis in R.

Duration: The duration of this workshop will be five consecutive days, with 6-7 hours session per day

Certification Policy:

• Certificate of Participation for all the workshop participants.
• At the end of this workshop, a small competition will be organized among the participating students and winners will be awarded with a 'Certificate of Excellence'.
• Certificate of Coordination for the coordinators of the campus workshops.

Eligibility: There are no prerequisites. Anyone interested, can join this workshop.