Syllabus of Data Science Course in Bangalore
Introduction to R
- What is R?
- Why R?
- Installing R
- R environment
- How to get help in R
- R Studio Overview
The understanding R data structure
- Variables in R
- Scalars
- Vectors
- Matrices
- List
- Data frames
- Cbind, Rbind, attach and detach functions in R
- Factors
- Getting a subset of Data
- Missing values
- Converting between vector types
Importing data
- Reading Tabular Data files
- Reading CSV files
- Importing data from excel
- Loading and storing data with a clipboard
- Accessing database
- Saving in R data
- Loading R data objects
- Writing data to file
- Writing text and output from analyses to file
Manipulating Data
- Selecting rows/observations
- Rounding Number
- Creating a string from a variable
- Search and Replace a string or Number
- Selecting columns/fields
- Merging data
- Relabeling the column names
- Data sorting
- Data aggregation
- Finding and removing duplicate records
Using functions in R
- Apply Function Family
- Commonly used Mathematical Functions
- Commonly used Summary Functions
- Commonly used String Functions
- User defined functions
- local and global variable
- Working with dates
R Programming
- While loop
- If loop
- For loop
- Arithmetic operations
Charts and Plots
- Box plot
- Histogram
- Pie graph
- Line chart
- Scatterplot
- Developing graphs
- Cover all the current trending packages for Graphs
Machine Learning Algorithm:
- Sentiment analysis with Machine learning
- C 5.0
- Support vector Machines
- K Means
- Random Forest
- Naïve Bayes algorithm
Statistics:
- Correlation
- Linear Regression
- Non-Linear Regression
- Predictive time series forecasting
- K means clustering
- P value
- Find outlier
- Neural Network
- Error Measure
Leading Topics:
- Overture of R Shiny
- What is Hadoop
- Integration of Hadoop in R
- Data Mining using R
- Clinical research preface in R
- API in R (Twitter and Facebook)
- Word Cloud in R





