Introduction to Data Analytics Learning Objectives - This module introduces you to a number of the crucial key phrases in R like Business Intelligence, Business Analytics, Data and Information. You also can learn the way R can play an crucial function in fixing complicated analytical issues. This module tells you what's R and the way it's miles utilized by the giants like Google, Facebook, Bank of America, and many others. Also, you may examine use of 'R' withinside the industry, this module additionally enables you evaluate R with different software program in analytics, set up R and its programs.

• Topics - Introduction to phrases like Business Intelligence, Business Analytics, Data, Information, how data hierarchy may be improved/introduced, know-how Business Analytics and R, information approximately the R language, its network and ecosystem, apprehend using 'R' withinside the industry, evaluate R with different software program in analytics, Install R and the programs beneficial for the path, carry out fundamental operations in R the usage of command line, examine using IDE R Studio and Various GUI, use the ‘R help’ function in R, information approximately the global R network collaboration.

Introduction to R Programming Learning Objectives - This module begins offevolved from the fundamentals of R programming like datatypes and features. In this module, we gift a state of affairs and can help you consider the alternatives to remedy it, together with which datatype ought to one to keep the variable or which R feature that let you on this state of affairs. You may also discover ways to practice the 'be a part of' feature in SQL.

• Topics - The numerous styles of statistics kinds in R and its suitable makes use of, the integrated features in R like: seq(), cbind (), rbind(), merge(), information at the numerous subsetting methods, summarize statistics with the aid of using the usage of features like: str(), class(), length(), nrow(), ncol(), use of features like head(), tail(), for examining statistics, Indulge in a category pastime to summarize statistics, dplyr bundle to carry out SQL be a part of in R

Data Manipulation in R Learning Objectives - In this module, we begin with a pattern of a grimy statistics set and carry out Data Cleaning on it, ensuing in a statistics set, which is prepared for any evaluation. Thus the usage of and exploring the famous features required to easy statistics in R.

• Topics - The numerous steps worried in Data Cleaning, features utilized in Data Inspection, tackling the issues confronted for the duration of Data Cleaning, makes use of of the features like grepl(), grep(), sub(), Coerce the statistics, makes use of of the practice() features.

Data Import Techniques in R Learning Objectives - This module tells you approximately the flexibility and robustness of R that may take-up statistics in a number of codecs, be it from a csv record to the statistics scraped from a website. This module teaches you numerous statistics uploading strategies in R.

• Topics - Import statistics from spreadsheets and textual content documents into R, import statistics from different statistical codecs like sas7bdat and spss, programs set up used for database import, hook up with RDBMS from R the usage of ODBC and fundamental SQL queries in R, fundamentals of Web Scraping.

Exploratory Data Analysis Learning Objectives - In this module, you may examine that exploratory statistics evaluation is an crucial step withinside the evaluation. EDA is for seeing what the statistics can inform us past the formal modeling or hypothesis. You may also find out about the numerous obligations worried in an ordinary EDA technique.

• Topics - Understanding the Exploratory Data Analysis(EDA), implementation of EDA on numerous datasets, Boxplots, whiskers of Boxplots. know-how the cor() in R, EDA features like summarize(), llist(), more than one programs in R for statistics evaluation, the Fancy plots just like the Segment plot, HC plot in R.

Data Visualization in R Learning Objectives - In this module, you may examine that visualization is the USP of R. You will examine the standards of making easy in addition to complicated visualizations in R.

• Topics - Understanding on Data Visualization, graphical features found in R, plot numerous graphs like tableplot, histogram, Boxplot, customizing Graphical Parameters to improvise plots, know-how GUIs like Deducer and R Commander, advent to Spatial Analysis.

Data Mining: Clustering Techniques Learning Objectives - This module helps you to understand approximately the numerous Machine Learning algorithms. The Machine Learning kinds are Supervised Learning and Unsupervised Learning and the distinction among the 2 kinds. We may also talk the technique worried in 'K-method Clustering', the numerous statistical measures you want to understand to put in force it on this module.

• Topics - Introduction to Data Mining, Understanding Machine Learning, Supervised and Unsupervised Machine Learning Algorithms, K-method Clustering.

Data Mining: Association Rule Mining & Collaborative filtering Learning Objectives - In this module, you may discover ways to locate the institutions among many variables the usage of the famous statistics mining approach known as the "Association Rule Mining", and put in force it to are expecting buyers' subsequent purchase. You may also examine a brand new approach that may be used for advice cause known as "Collaborative Filtering". Various real-time primarily based totally eventualities are proven the usage of those strategies on this module.

• Topics - Association Rule Mining, User Based Collaborative Filtering (UBCF), Item Based Collaborative Filtering (IBCF)

Linear and Logistic Regression Learning Objectives - This module touches the bottom of 'Regression Techniques’. Linear and logistic regression is defined from the fundamentals with the examples and it's miles carried out in R the usage of case research devoted to every form of Regression mentioned.

• Topics - Linear Regression, Logistic Regression.

Anova and Sentiment Analysis Learning Objectives - This module tells you approximately the Analysis of Variance (Anova) Technique. The set of rules and numerous elements of Anova had been mentioned on this module. Additionally, this module additionally offers with Sentiment Analysis and the way we will fetch, extract and mine stay statistics from Twitter to discover the sentiment of the tweets.

• Topics - Anova, Sentiment Analysis.

Data Mining: Decision Trees and Random Forest Learning Objectives - This module covers the standards of Decision Trees and Random Forest. The set of rules for introduction of bushes and class of choice bushes and the numerous elements just like the Impurity feature Gini Index, Pruning, Entropy and many others are drastically taught on this module. The set of rules of Random Forests is mentioned in a step-clever method and defined with real-lifestyles examples. At the give up of the class, those standards are carried out on a real-lifestyles statistics set.

• Topics - Decision Tree, the three factors for class of a Decision Tree, Entropy, Gini Index, Pruning and Information Gain, bagging of Regression and Classification Trees, standards of Random Forest, running of Random Forest, capabilities of Random Forest, amongst others.

Project Work Learning Objectives - This module discusses numerous standards taught at some stage in the path and their implementation in a project.

• Topics - Analyze census statistics to are expecting insights at the earnings of the people, primarily based totally at the elements like: age, education, work-class, career the usage of Decision Trees, Logistic Regression and Random Forest. Analyze the Sentiment of Twitter statistics, wherein the statistics to be analyzed is streamed stay from twitter and sentiment evaluation is completed at the same.