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DataScience with R Training in BTM Layout

Home/DataScience with R Training in BTM Layout
DataScience with R Training in BTM Layout 2018-04-25T16:53:50+00:00

DataScience with R Syllabus                                                                    Total Duration : 37:00:00 hrs

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.

Thanks for the best ever Training. I did my Android Course here. Trainer is very good in knowledge. They provides the best ever Support to me.

Kaviya

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