Data Analytics with R Programming Certification Training

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

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(), Coerce the data, uses of the apply() functions.

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.

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.

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 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.

Learning Objectives - This module lets you know 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', 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.

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 purpose 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)

Learning Objectives - This module touches the base of '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.

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.

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.

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.

The prerequisites for Edureka's Data Analytics with R training include the fundamental understanding of Computer Programming Languages. Fundamentals of Data Analysis practiced over any data analysis tool like SAS/R will be a plus. However, you will be provided with complimentary “R Essentials” as a self-paced course once you enroll in the Data Analytics with R Programming certification course.

After completing this Data Analytics with R Programming Certification course, you will be able to:

Edureka offers the best online course for Data Analytics with R. Enroll now in our Data Analytics with R Programming training and get a chance to learn from industrial giants.

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.

Below is a blog that will help you understand the significance of R and Data Science: Mastering R Is The First Step For A Top-Class Data Science Career

Having Data Science skills is a highly preferred learning path after the Data Analytics with R training. Check out the upgraded Data Science Course

R has been one of the most widely used, premier, and powerful open-source languages that offers comprehensive libraries which provide interactive graphics and makes data visualization and representation easy to analyze. It has been used for over a decade to perform simple and complex mathematical and statistical calculations on data. R produces plots and graphics that are free of error. It continues to be a favorite choice for data scientists who build and use machine learning applications and other scientific computations. It has also evolved into the most preferred language for Data Analytics. The increasing search trends also indicate that it is a must for professionals delving into the Data Analytics domain.

Your system should have a minimum 4GB RAM and i3 processor or above.

For doing your practicals, you would need to install R set-up or R-Studio on your system. The step-wise installation guides for setting up the environment on various operating systems are present in the LMS. In case you come across any doubt, the 24*7 support team will promptly assist you.