Data Science R Programming Certification - eLearning

450,00 EUR

eLearning

The Data Science with R Certification course enables you to take your data science skills into a variety of companies, helping them analyze data and make more informed business decisions. The 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.

Course timeline

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  1. Introduction to Business Analytics

    Lesson 01

    - Overview

    - Business Decisions and Analytics

    - Types of Business Analytics

    - Applications of Business Analytics

    - Data Science Overview

    - Conclusion

    - Knowledge Check

  2. Introduction to R Programming

    Lesson 02

    - Overview

    - Importance of R

    - Data Types and Variables in R

    - Operations in R

    - Conditional Statements in R

    - Loops in R

    - Conclusion

    - Knowledge Check

  3. Data Structures

    Lesson 03

    - Overview

    - Identify Data Structures

    - Demo: Identify Data Structures

    - Assigning Values to Data Structures

    - Data Manipulation

    - Demo: Assigning Values and Applying Functions

    - Conclusion

    - Knowledge Check

  4. Data Visualization

    Lesson 04

    - Overview

    - Introduction to Data Visualization

    - Data Visualization Using Graphics in R

    - Ggplot2

    - File Formats of Graphic Outputs R

    - Conclusion

    - Knowledge Check

  5. Statistics for Data Science-I

    Lesson 05

    - Overview

    - Introduction to Hypothesis

    - Types of Hypothesis

    - Data Sampling

    - Confidence and Significance Levels

    - Conclusion

    - Knowledge Check

  6. Statistics for Data Science - II

    Lesson 06

    - Overview

    - Hypothesis Test

    - Parametric Test

    - Non-Parametric Test

    - Hypothesis Tests about Population Means

    - Hypothesis Tests about Population Variance

    - Hypothesis Tests about Population Proportions

    - Conclusion

    - Knowledge Check

  7. Regression Analysis

    Lesson 07

    - Overview

    - Introduction to Regression Analysis

    - Types of Regression Analysis Models

    - Linear Regression

    - Demo: Simple Linear Regression

    - Non-Linear Regression

    - Demo: Regression Analysis with Multiple Variables

    - Cross Validation

    - Non-linear to Linear Models

    - Principal Component Analysis

    - Factor Analysis

    - Conclusion

    - Knowledge Check

  8. Classification

    Lesson 08

    - Overview

    - Classification and its Types

    - Logistic Regression

    - Support Vector Machines

    - Demo: Naive Bayes Classifier

    - Demo: Naive Bayers Classifier

    - Decision: Tree Classification

    - Demo: Decision Tree Classification

    - Random Forest Classification

    - Evaluating Classifier Models

    - Demo: K-Fold Cross Validation

    - Conclusion

    - Knowledge Check

  9. Clustering

    Lesson 09

    - Overview

    - Introduction to Clustering

    - Clustering Methods

    - Demo: K-means Clustering

    - Demo: Hierarchical Clustering

    - Conclusion

    - Knowledge Check

  10. Association

    Lesson 10

    - Overview

    - Association Rule

    - Apriori Algorithm

    - Demo: Apriori Algorithm

    - Conclusion

    - Knowledge Check

Learning Outcomes

At the end of this Data Science R Programming eLearning Course, you will be able to:

Gain a foundational understanding of business analytics

Install R, RStudio, workspace setup, and learn about the various R packages

Master R programming and understand how various statements are executed in R Gain an in-depth understanding of data structure used in R and learn to import/export data in R

Define, understand and use the various apply functions and DPLYR functions

Master R programming and understand how various statements are executed in R

Gain an in-depth understanding of data structure used in R and learn to import/export data in R

Define, understand and use the various apply functions and DPLYR functions

Understand and use the various graphics in R for data visualization

Gain a basic understanding of various statistical concepts

Understand and use the hypothesis testing method to drive business decisions

Understand and use linear and non-linear regression models, and classification techniques for data analysis

Learn and use the various association rules the Aprioiri algorithm

Learn and use clustering methods including k-means, DBSCAN, and hierarchical clustering.

Who Should Enroll in this Program?

There is an increasing demand for skilled data scientists across all industries, making this data science certification course well-suited for participants at all levels of experience. We recommend this data science training course for the descending categories.

IT professionals

Analytics Professionals

Software Developers

Data scientist

Business Intelligent

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