Data science with R Training in Bangalore

Data Science with R Course Syllabus

Total Duration: 37:00:00

Module 1- Introduction to Data Analytics

Duration : 04:00:00 hrs

Objectives:
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.
Topics

• 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
• Learn the use of IDE R Studio
• Use the ‘R help’ feature in R

Module 2- Introduction to R programming

Duration : 03:00:00 hrs

Objectives:
This module starts from the basics of R programming like datatypes and functions.
In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario.
You will also learn how to apply the ‘join’ function in SQL.
Topics

• Variables in R
• Scalars
• Vectors
• Matrices
• List
• Data frames
• Using c, Cbind, Rbind, attach and detach functions in R
• Factors

Module 3- Data Manipulation in R

Duration : 04:00:00 hrs

Objectives:
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
• Recoding data
• Merging data
• Slicing of Data
• Merging Data
• Apply functions

Module 4- Data Import techniques in R

Duration : 04:00:00 hrs

Objectives:
This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file to the data scraped from a website.
This module teaches you various data importing techniques in R.
Topics

• Reading Data
• Writing Data
• Basic SQL queries in R
• Web Scraping

Module 5- Exploratory data Analysis

Duration : 04:00:00 hrs

Objectives:
In this module, you will learn that exploratory data analysis is an important step in the analysis.
EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.
Topics

• Box plot
• Histogram
• Pareto charts
• Pie graph
• Line chart
• Scatterplot
• Developing Graphs

Module 6- Basics of Statistics & Linear & Logistic Regression

Duration : 05:00:00 hrs

Objectives:
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.
Topics

• Basics of Statistics
• Inferencial statistics
• Probability
• Hypothesis
• Standard deviation
• Outliers
• Correlation
• Linear & Logistic Regression

Module 7- Data Mining: Clustering techniques, Regression & Classification

Duration : 04:00:00 hrs

Objectives:
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.
The two Machine Learning types are Supervised Learning and Unsupervised Learning and the difference between the two types.
We will also discuss the process involved in ‘K-means Clustering’, the various statistical measures you need to know to implement it in this module.
Topics

• Introduction to Data Mining
• Understanding Machine Learning
• Supervised and Unsupervised Machine Learning Algorithms
• K- means clustering

Module 8- Project work

Duration : 08:00:00 hrs

2 Real-time projects
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