Business Analytics using R

Business Analytics, or simply analytics, is the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions.Thus Business Analytics is “a process of transformingdata into actions through analysis and insights in the context of organizational decision making and problem solving.”

This is an introductory training on analyzing different aspects of businesses using R. This training covers areas that include introduction to R, the R basics, logical operator, data structure, data management in R, data visualization and graphics in R, and basic statistical functions and inferences.

LEARNING OUTCOMES
After completion of this training, the participants will be able to:
• use real data sets and perform analysis using R
• understand different types of the data
• make graphs and charts by using R
• use R for various statistical computations and
inferences
• apply different statistical test in real data
• use regression analysis in real data

SESSIONS OUTLINE

DAY 1:

An introduction to R Data Structure in R: Business analytics, R software and its purpose, installing R and R Studio, The R environment andworking with R, The R packages (Meaning and purpose of packages, installing, loading and learning Packages), Basic math, variables, data types, basic R function, basic statistics. Data Structure, Data Entering, Reading and Management in R: Data entering, Data reading from external files (CSVs, Excel, SPSS, Stata files), creating new variables, recoding, sorting data, merging data sets
(Adding columns and rows to the data frame, working with data frame), Data manipulation by using dplyr package.

DAY 2:

Graphics, Visualization and Summary Measures: Bar chart, Pie chart, Box plot, Histogram, Line graph, Density plots, Normal Q-Q plot, Scatter plot, Basics of ggplot, Measures of central tendency, Measures of variation.Inferential Analysis: Review of descriptive and inferential analysis, hypothesis testing, one sample t test, independent sample t test, paired sample t test, one-way ANOVA.

Chi Square test, Correlation and Regression:
Testing for independence in contingency tables using chi-squared, correlation coefficient, correlation matrix, linear regression, assumptions, standardized coefficient, overall test (F test), individual t test.