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Data Analytics with R Programming

The Data Analytics with R course will assist you in gaining the 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 and Social Media.

Prerequisites
  • The pre-requisites for learning 'Data Analytics with R Programming' include basic statistics knowledge.
Target Audience

This course is meant for students and professionals who are interested in working in the analytics industry and who would like to enhance their technical skills with exposure to cutting-edge practices. This is a great course for those who are ambitious to become ‘Data Analysts’ in the near future. This is a must learn course for professionals from Mathematics, Statistics or Economics backgrounds who are interested in learning Business Analytics.

Certification

COMNet Group Certification

Exam

COMNet Group Exam

Accreditation

COMNet Group Accreditation

Course Content
1. Introduction to Data Analytics

Learning 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 R is and how it is used by the giants like Google, Facebook, Bank of America, etc. Also, you will learn about the use of ‘R’ in the industry.  This module also helps you learn to compare R with other software in analytics, and to install R and its packages.

Topics – 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, understanding the use of ‘R’ in the industry, comparing R with other software in analytics, installing R and the packages useful for the course, performing basic operations in R using command line, learning the use of IDE R Studio and Various GUI, using the ‘R help’ feature in R, knowledge about the worldwide R community collaboration.

2. Introduction to R Programming

Learning 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 – 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.

3. Data Manipulation in R

Learning 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 – 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(), coercing the data, uses of the apply() functions.

4. Data Import Techniques in R

Learning 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 – 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.

5. Exploratory Data Analysis

Learning 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 – 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.

6. Data Visualization in R

Learning Objectives – In this module, you will learn that visualization is the USP of R. You will learn the concepts of creating simple as well as complex visualizations in R.

Topics – Understanding Data Visualization, graphical functions present in R, plotting various graphs like tableplot, histogram, Boxplot, customizing Graphical Parameters to improvise plots, understanding GUIs like Deducer and R Commander, introduction to Spatial Analysis.

7. Data Mining: Clustering Techniques

Learning Objectives – This module teaches you about the various Machine Learning algorithms. 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’, and 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.

8. Data Mining: Association Rule Mining & Collaborative Filtering

Learning Objectives – In this module, you will learn how to find the associations between many variables using the popular data mining technique called the “Association Rule Mining”, and implement it to predict buyers’ next purchase. You will also learn a new technique that can be used for recommendation purposes called “Collaborative Filtering”. Various real-time based scenarios are shown using these techniques in this module.

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

9. Linear and Logistic Regression

Learning Objectives – This module covers ‘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 – Linear Regression, Logistic Regression.

10. Anova and Sentiment Analysis

Learning Objectives – This module tells you about the Analysis of Variance (Anova) Technique. The algorithm and various aspects of Anova have been discussed in this module. Additionally, this module also deals with Sentiment Analysis and how we can fetch, extract and mine live data from Twitter to find out the sentiment of the tweets.

Topics – Anova, Sentiment Analysis

11. Data Mining: Decision Trees and Random Forest

Learning Objectives – This module covers the concepts of Decision Trees and Random Forest. The algorithm for creation of trees and classification of decision trees and the various aspects like the Impurity function Gini Index, Pruning, Entropy etc are extensively taught in this module. The algorithm of Random Forests is discussed in a step-wise approach and explained with real-life examples. At the end of the class, these concepts are implemented on a real-life data set.

Topics – 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.

12. Project Work

Learning Objectives – This module discusses various concepts taught throughout the course and their implementation in a project.

Topics – 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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