Course Description
In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, and organizing and commenting R code. Topics in statistical data analysis and optimization will provide working examples.
R’s open-source nature offers companies the required boost. This is because this is a world that is focused on data and you have to shine in the competition. Data science is a great business priority as per Gartner Research. Now the reputation of R Training is going to see even more surge. Suppose you are keen to expand your horizons and you possess data evaluation skills, then R Language can be an amazing language
Introduction
- What are Data Analysis, Data Analytics and Data Science?
- Business Decisions
- Case study of Walmart
Various analytics tools
- Descriptive
- Predictive
- Web Analytics
- Google Analytics
- R and features
- Evolution of R?
- Big data Hadoop and R
Data Types
- R & R Studio Installation
- Scalar
- Vectors
- Matrix
- List
- Data frames
- Factors
- Handling date in R
- Conversion of data types
- Operators in R
Importing Data
- CSV files
- Database data (Oracle 11g)
- XML files
- JSON files
- Reading & Writing PDF files
- Reading & Writing JPEG files
- Saving Data in R
Manipulating Data
- Cbind, Rbind
- Sorting
- Aggregating
- dplyr
Conditional Statements and Functions
- If …else
- For loop
- While loop
- Repeat loop
- Apply()
- sApply()
- rApply()
- tApply
Statistical Concepts
- Descriptive Statistics
- Inferential Statistics
- Central Tendency (Mean,Mode,Median)
- Hypothesis Testing
- Probability
- tTest
- zTest
- Chi Square test
- Correlation
- Covariance
- Anova
Predictive Modelling
- Linear Regression
- Normal distribution
- Density
Data Visualisation in R using GGPlot
- Box Plot
- Histograms
- Scatter Plotter
- Line chart
- Bar Chart
- Heat maps
Misc. functions and Data Visualization using Plotly
- 3D-view
- Geo Maps
- Null Handling
- Merge
- Grep
- Scan
Advance Topics in R
- Text Mining
- Exploratory Data Analysis
- Machine Learning with R (concept)
Schedule
- Week 1: Overview of R, R data types and objects, reading and writing data
- Week 2: Control structures, functions, scoping rules, dates and times
- Week 3: Loop functions, debugging tools
- Week 4: Simulation, code profiling