Data Analytics with R training will help you gain expertise in R Programming, Data Manipulation, Exploratory Data Analysis, Data Visualization, Data Mining, Regression, Sentiment Analysis and using R Studio for real life case studies on Retail, Social Media. This course is specially designed to provide the requisite knowledge and skills to become a successful analytics professional. It covers concepts of Data Manipulation, Exploratory Data Analysis, etc before moving over to advanced topics like the Ensemble of Decision trees, Collaborative filtering, etc.
After the completion of this Data Analytics with R course, you should be able to:
Understand concepts around Business Intelligence and Business Analytics
Explore Recommendation Systems with functions like Association Rule Mining, user-based collaborative filtering and Item-based collaborative filtering among others
Apply various supervised machine learning techniques
Perform Analysis of Variance (ANOVA)
Learn where to use algorithms - Decision Trees, Logistic Regression, Support Vector Machines, Ensemble Techniques etc.
Use various packages in R to create fancy plots
Work on a real-life project, implementing supervised and unsupervised machine learning techniques to derive business insights
The Data Analytics with R training certifies you in mastering the most popular Analytics tool. "R" wins on Statistical Capability, Graphical capability, Cost, rich set of packages and is the most preferred tool for Data Scientists.
This course is meant for all those students and professionals who are interested in working in analytics industry and are keen to enhance their technical skills with exposure to cutting-edge practices. This is a great course for all those who are ambitious to become 'Data Analysts' in near future. This is a must learn course for professionals from Mathematics, Statistics or Economics background and interested in learning Business Analytics.
The pre-requisites for learning 'Data Analytics with R' includes basic statistics knowledge. We provide a complimentary course "Statistics Essentials for R" to all the participants who enroll for the Data Analytics with R Training. This course helps you brush up your statistics skills.
Introduction to terms like Business Intelligence, Business Analytics, Data, Information, how information hierarchy can be improved/introduced, understanding Business Analytics and R, knowledge about the R language, its community and ecosystem, understand the use of 'R' in the industry, compare R with other software in analytics, Install R and the packages useful for the course, perform basic operations in R using command line, learn the use of IDE R Studio and Various GUI, use the ‘R help’ feature in R, knowledge about the worldwide R community collaboration.
The various kinds of data types in R and its appropriate uses, the built-in functions in R like: seq(), cbind (), rbind(), merge(), knowledge on the various subsetting methods, summarize data by using functions like: str(), class(), length(), nrow(), ncol(), use of functions like head(), tail(), for inspecting data, Indulge in a class activity to summarize data, dplyr package to perform SQL join in R
The various steps involved in Data Cleaning, functions used in Data Inspection, tackling the problems faced during Data Cleaning, uses of the functions like grepl(), grep(), sub(), Coerce the data, uses of the apply() functions.
Import data from spreadsheets and text files into R, import data from other statistical formats like sas7bdat and spss, packages installation used for database import, connect to RDBMS from R using ODBC and basic SQL queries in R, basics of Web Scraping.
Understanding the Exploratory Data Analysis(EDA), implementation of EDA on various datasets, Boxplots, whiskers of Boxplots. understanding the cor() in R, EDA functions like summarize(), llist(), multiple packages in R for data analysis, the Fancy plots like the Segment plot, HC plot in R.
Understanding on Data Visualization, graphical functions present in R, plot various graphs like tableplot, histogram, Boxplot, customizing Graphical Parameters to improvise plots, understanding GUIs like Deducer and R Commander, introduction to Spatial Analysis.
Decision Tree, the 3 elements for classification of a Decision Tree, Entropy, Gini Index, Pruning and Information Gain, bagging of Regression and Classification Trees, concepts of Random Forest, working of Random Forest, features of Random Forest, among others.
Analyze census data to predict insights on the income of the people, based on the factors like: age, education, work-class, occupation using Decision Trees, Logistic Regression and Random Forest. Analyze the Sentiment of Twitter data, where the data to be analyzed is streamed live from twitter and sentiment analysis is performed on the same.
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