- PARAMETERS SPECIFICATIONS
- Tools Used R
- Learning Mode (Classroom – Instructor based)
- Duration 58 – 52 Hours
- Batch size 5- 8 Students
- Location Delhi (Saket)
- Course includes Live scenarios, Case Studies, Project, Assessments, Mock Interview.
- Study Material PPTs, Doc, Data, PDFs etc.

- Any graduate - No prior knowledge of Data Science / Analytics is required.

- R is open source data analysis software: and widely uses by Data scientists, statisticians, Researchers and Data analysts—anyone who needs to make sense/insight of data can use R for Statistical Analysis, Data visualization, and Predictive Modeling. R is created by Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand in the 1990s as a statistical platform for their students, and thus it has been extended over the decades by thousands of user-created libraries/packages. R is a programming language: An object-oriented language created by statisticians, R provides objects, operators, and functions that allow users to explore, model, and visualize data. R is a vector language, so anyone can add functions to a single Vector without putting in a loop. And at the same time R is powerful and faster than other languages, we can easily implement Machine Learning algorithms in a fast and simple way

- R Programmer
- Data Analyst/Miner
- Data Modeler
- Data Scientist
- ML specialist
- NLP specialist and many more.

- Population and sample
- Descriptive and Inferential Statistics
- Statistical data analysis
- Variables
- Central Tendency, Sample and Population Distributions
- Central Limit Theorem (CLT)
- Estimation & Confidence interval
- Normal Distribution
- Skewness.
- Boxplot
- Standard deviation
- Standard Error
- Hypothesis testing
- P-value
- Scatter plot and correlation coefficient
- Scales of Measurements and Data Types
- Numerical Summarization
- Outliers & Summary
- Data Summarization
- Visual Summarization

- Installing & starting with R
- Basic and environmental features of R.
- Calculations with R
- Functions
- Understanding R language and programming guidelines
- Listing the objects in the workspace
- Vectors
- Extracting elements from vectors
- Vector arithmetic
- Simple patterned vectors
- Missing values and other special values
- Character vectors Factors
- More on extracting elements from vectors
- Matrices and arrays
- Data frames
- Dates and times
- Assignments with Datasets

- This module introduces you to some of the important keywords in R like Business Intelligence, Business
- Analytics, Data and Information. You can also learn how R can play an important role in solving complex analytical problems
- This module tells you what is R and how it is used by the giants like Google, Facebook, etc. Also, you will learn use of 'R' in the industry, this module also helps you compare R with other software in analytics, install R and its packages.
- Business Analytics, Data, Information Understanding Business Analytics and R Compare R with other software in analytics Install R Perform basic operations in R using command line

- Importing data into R
- CSV File
- Excel File
- Import data from text table
- DATA SCIENCE USING R-PROGRAMMING
- Topics
- Variables in R
- Scalars
- Vectors
- R Matrices
- List
- R – Data Frames
- Using c, Cbind, Rbind, attach and detach etc. functions in R
- R – Factors
- R – CSV Files
- R – Excel File
- Assignments
- Business Scenario/Group Discussion
- R Nuts and Bolts
- Entering Input. – Evaluation- R Objects- Numbers- Attributes- Creating Vectors- Mixing
- Objects- Explicit Coercion- Summary- Names- Data Frames

- The dplyr Package
- Installing the dplyr package
- select()
- filter()
- arrange()
- rename()
- mutate()
- group_by()
- %>%
- Assignments
- Business Scenario/Group Discussion

- Looping on the Command Line
- lapply()
- sapply()
- tapply()
- apply()
- Assignments
- Business Scenerio/Group Discussion

- In this module, we start with a sample of a dirty data set and perform Data Cleaning on it, resulting
- in a data set, which is ready for any analysis
- Thus using and exploring the popular functions required to clean data in R.
- Topics
- Data sorting
- Find and remove duplicates record
- Cleaning data
- Merging data
- Statistical Plotting
- Bar charts and dot charts
- Pie charts
- Histograms
- Box plots
- Scatter plots
- QQ plots
- Assignments with Datasets

- Control Structure Programming with R
- The for() loop
- The if() statement
- The while() loop
- The repeat loop, and the break and next statements
- Apply, Sapply, Lapply
- Assignments with Datasets

- Using Factors
- Manipulating Factors
- Numeric Factors
- Creating Factors from Continuous Variables
- Convert the variables in factors or in others

- Data Modifying
- Data Frame Variables
- Recoding Variables
- The recode Function
- Reshaping Data Frames
- The reshape Package
- Assignments with Datasets

- This module touches the base of Descriptive and Inferential Statistics and Probabilities & 'Regression Techniques'.
- Linear and logistic regression is explained from the basics with the examples and it is implemented in R using two case studies dedicated to each type of Regression discussed.
- Assessing the Accuracy of the Coefficient Estimates
- Assessing the Accuracy of the Model
- Estimating the Regression Coefficients.
- Some Important Questions
- Lab: Linear Regression.
- Libraries
- Simple Linear Regression
- Multiple Linear Regression
- Interaction Terms
- Qualitative Predictors
- Writing Functions
- Assignments with Different Datasets
- Business Scenario/Group Discussion