All You Need To Know About R Analytics Course Upgrade

Datetime:2016-08-23 02:13:41          Topic: Cluster Analysis  R Program           Share

Here’s some great news! Our Data Analytics with R course has been upgraded to keep pace with the rapid changes in the analytics industry. The R analytics course is now packed with modules and features that will help you master tools and techniques that are routinely used in the industry.

This course upgrade comes at a time when R has emerged as a popular programming language that is increasingly being adopted by businesses for its richness of packages, statistical computation capabilities and graphical techniques. It is also the preferred language for Data Scientists, and learning data analytics with R will help you embark on a Data Science learning path. There can be no doubt that R programming can be your gateway to a successful analytics career! Keeping this in mind, our data analytics with R course upgrade has been designed to equip you with the hottest analytics skills in the industry and prepare you to make the most of the career opportunities that come your way.

Upgraded R Analytics Course Features

The already power-packed R analytics course has now been further bolstered further to include “dplyr”, collaborative filtering, statistical measures associated with k-means clustering, and decision tree concepts. Here are the upgraded course features in detail:

  1. Introduction to various topics like Business Intelligence, Business Analytics, Data, Information, Information hierarchy.
  2. Using the R package “dplyr” in SQL-like joins.
  3. Exhaustive explanations for statistical measures associated with k-means clustering, such as, cluster, centers, totss, withinss, tot.withinss and betweenss.
  4. Collaborative Filtering – User Based Collaborative Filtering (UBCF), Item Based Collaborative Filtering (IBCF).
  5. Decision Tree concepts like Impurity function, Gini Index, Pruning, Entropy in detail.
  6. You will also get to work on hands-on projects on market basket analysis and customer segmentation, using decision trees, random forest and logistic regression concepts.

In addition to these, the course upgrade provides you with bonus training in the form of self-paced videos on the following topics:

  1. Market Basket Analysis
  2. Segmentation Case-Study

Why Learn R Programming?

R is a language and environment for statistical computing and graphics and is highly extensible. It is a powerful language that provides a wide variety of statistical techniques such as linear and non-linear modeling, classical statistical tests, time-series analysis, classification, clustering, and graphical capabilities. R allows it users to manage and analyze data with Hadoop with RHadoop utility wherein Hadoop will be used as data store and “R” for analytics. R wins over SAS on statistical capability, graphical capability, cost and rich set of packages among other reasons.

Elementary statistical knowledge and quantitative aptitude and affinity towards numbers are the prerequisites for you to start learning R programming. Even professionals from non-IT backgrounds such as Marketing, Sales and Economics who are keen to make a career in analytics can learn R. It is also the most recommended skill for Data Science aspirants.

The Edureka R Analytics Course has been specially curated with industry experts to help you learn essential R skills such as loading data, data manipulation, exploratory data analysis, data visualization, techniques of regression, predictive analytics, data mining, sentiment analysis and usage of R’s programming tools. Check out the upcoming R batch dates here .

Got a question for us? Please mention it in the comments section and we will get back to you.





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