Data Science with R Certification Course
Learner rating  8.4 

 Course platform: Simplilearn
 Level: Advanced
 Full lifetime access
 Paid course
 Class length: Approx. 64 hrs.
Learn Data Science with R
About Data Science
Data science provides meaningful information based on large amounts of complex data or big data. Data science combines different fields of work in statistics and computation to interpret data for decisionmaking purposes.
A data scientist collects, analyzes, and interprets large volumes of data, in many cases, to improve a company’s operations. Data science professionals develop statistical models that analyze data and detect patterns, trends, and relationships in data sets.
About the R programming language
When you see powerful analytics, statistics, and visualizations used by data scientists and business leaders, chances are that the R language is behind them. Opensource R is the statistical programming language that data experts the world overuse for everything from mapping broad social and marketing trends online to developing financial and climate models that help drive our economies and communities. R lets experts quickly, easily interpret and interact with and visualize data.
Why Data Science with R programming?
R is the most popular language in the world of Data Science. It is heavily used in analyzing data that is both structured and unstructured. This has made R, the standard language for performing statistical operations. R also allows various features that set it apart from other Data Science languages. R plays a very vital role in Data Science, you will be benefited from the following operations in R programming:
1. You can run your code without any compiler
 R is an interpreted language. Hence you can run code without any compiler. R interprets the code and makes the development of code easier.
2. Many calculations done with vectors
 R is a vector language, so anyone can add functions to a single Vector without putting in a loop. Hence, R is powerful and faster than other languages.
3. Statistical Language
 R used in biology, genetics as well as in statistics. R is a turning complete language where any type of task can perform.
About this Course
The data science with R course covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.
Moreover, at the end of every course, you will be subjected to a project where you can apply your knowledge thought in the class to solve realworld problems.
Course Curriculum:
Lesson 1: Introduction to Business Analytics
 Business Decisions and Analytics, Types of Business Analytics & Applications of Business Analytics
Lesson 2: Introduction to R Programming
 Importance of R
 Data Types, Variables in R & Operators in R
 Conditional Statements in R, Loops in R, R script & Functions in R
Lesson 3: Data Structures
 Identifying Data Structures with demo
 Assigning Values to Data Structures & Data Manipulation with demo
Lesson 4: Data Visualization
 Introduction to Data Visualization
 Data Visualization using Graphics in R
 ggplot2 and File Formats of Graphics Outputs
Lesson 5: Statistics for Data ScienceI
 Introduction to Hypothesis
 Types of Hypothesis
 Data Sampling
 Confidence and Significance Levels
Lesson 6: Statistics for Data ScienceII
 Parametric Test
 NonParametric Test
 Hypothesis Tests about Population Means
 Hypothesis Tests about Population Proportions
Lesson 7: Regression Analysis
 Introduction to Regression Analysis
 Types of Regression Analysis Models Linear Regression
 Demo: Simple Linear Regression and NonLinear Regression
 Demo: Regression Analysis with Multiple Variables
 CrossValidation and NonLinear to Linear Models
 Principal Component Analysis and Factor Analysis
Lesson 8: Classification
 Classification and Its Types
 Logistic Regression
 Support Vector Machines and Demo: Support Vector Machines
 KNearest Neighbours, Naive Bayes Classifier, Naive Bayes Classifier, and Decision Tree Classification
 Decision Tree Classification and Random Forest Classification
 Evaluating Classifier Models and Demo: KFold CrossValidation
Lesson 9: Clustering
 Introduction to Clustering and Clustering Methods
 Demo: Kmeans Clustering and Hierarchical Clustering
Lesson 10: Association
 Association Rule and Apriori Algorithm with demo
What you will learn from the data science with R course
 Business analytics
 R programming and its packages
 Data structures and data visualization
 Apply functions and DPLYR function
 Graphics in R for data visualization
 Hypothesis testing
 Apriori algorithm
 Kmeans and DBSCAN clustering
Join the rapidly growing community of R users worldwide to see how opensource R continues to shape the future of statistical analysis and data science.
Prerequisites
There are no prerequisites for this Data Science with R certification course. If you are a beginner in Data Science, this is one of the best courses to start with.
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