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Basic Introduction to R
Structure of program in R
Basic Data type
Advance data type
Loops and conditional
Text Manipulation in R
3. Hands on Exercise
Function in R
Some mathematical functions
Data aggregation and joining in R :
Graphics in R :
4. Basic elements of graph generation
5. ggplot2 package
6. Grammar of graphics
7. Layered structure of ggplot2
8. Basic elements of ggplot2
9. Some chart use and creation with Base R and ggplot2 package
10. Writing plot to files
11. Hands on Exercise
R connection with Database
Debugging in R
A simple project on the basis of data joining, charting and Aggregation.
Data Preprocessing in R
Characteristics of Statistical Problems :
Introduction to probability :
Confidence Interval :
Hypothesis testing :
A simple project on hypothesis testing.
This course is going to be prerequisite for many upcoming courses like SparkR, Machine learning with R, Bayesian Network analysis in R, Deep learning with R and many more…..
Why to learn R ?
Data science has emerged to rule the world for many years. The speed data science is percolating to the different segment of business, academic and research, I am in view that, at some point or other everyone will be involved in executing some or other sort of data analysis using some tool.
In order to do any sort of analysis, R is very good tool. It is having more than 8000 packages to perform different sort of analysis. All the major Big Data frameworks are having an interface to R like Hadoop is having R Hadoop and Apache Spark is inbuilt with Rspark.
Best part of R is a open source platform. Just download it, install it, offcourse it is very easy to install and start learning. Very soon you might be hired by some data analysis company or you might be persuing your high level education somewhere or at least you are using data of your organization and coming of many insight to get every one amazed. So why to be late?
There will be three projects, which will move end to end .
Project1 : Given sells data, participants has to implement data science day to day algorithm like filtering, aggregation, date and time manipulation and applying charts to understand patterns in sells of different stores.
Project 2 : Given Movie lens data, participant has to implement data joining, aggregation and charting algorithms to find meaningful patterns and informations.
Project 3 : Given Kaggle titanic data, participants have to implement data preprocessing and data cleaning algorithms. After that, participants are required to do hypothesis testing on the data to validate their hypothesis.