Data Science with R Programming Certification - eLearning

450,00 EUR

  • 40 hours
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.

Key Features

Language

Course and material are in english

Level

Beginner - intermediate level

Access

1 year access to the self-paced study eLearning platform 24/7

6 hours of video content

with 40 hours recommended study time & practices

Practices

Virtual labs, Quizzes, Test simulation, End-Projects

No Exam

No exam for the course but student will get certification of training completion

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Learning Outcomes

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

Mastering R Programming

Develop proficiency in R and its packages to effectively handle data analysis tasks.

Data Exploration and Visualization

Learn techniques for exploring datasets and creating insightful visualizations to uncover patterns and insights.

Statistical Analysis

Understand and apply various statistical concepts to interpret data accurately.

Predictive and Descriptive Analytics

Gain the ability to perform both predictive and descriptive analytics to inform decision-making processes.

Data Import and Export

Acquire skills to import and export data in R, facilitating seamless data handling.

Cluster Analysis and Forecasting

Learn methods for grouping data and making forecasts based on data trends.

Course timeline

Hero
  1. Introduction to Business Analytics

    Lesson 01

    • Overview
    • Business Decisions and Analytics
    • Types of Business Analytics
    • Applications of Business Analytics
    • Data Science Overview

  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
  3. Data Structures

    Lesson 03

    • Identify Data Structures
    • Demo: Identify Data Structures
    • Assigning Values to Data Structures
    • Data Manipulation
    • Demo: Assigning Values and Applying Functions
  4. Data Visualization

    Lesson 04

    • Introduction to Data Visualization
    • Data Visualization Using Graphics in R
    • Ggplot2
    • File Formats of Graphic Outputs R
  5. Statistics for Data Science-I

    Lesson 05

    • Introduction to Hypothesis
    • Types of Hypothesis
    • Data Sampling
    • Confidence and Significance Levels
  6. Statistics for Data Science - II

    Lesson 06

    • Hypothesis Test
    • Parametric Test
    • Non-Parametric Test
    • Hypothesis Tests about Population Means
    • Hypothesis Tests about Population Variance
    • Hypothesis Tests about Population Proportions
  7. Regression Analysis

    Lesson 07

    • 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
  8. Classification

    Lesson 08

    • 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
  9. Clustering

    Lesson 09

    • Introduction to Clustering
    • Clustering Methods
    • Demo: K-means Clustering
    • Demo: Hierarchical Clustering
  10. Association

    Lesson 10

    • Association Rule
    • Apriori Algorithm
    • Demo: Apriori Algorithm
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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.

IT professionals

Analytics Professionals

Software Developers

Data scientist

Business Intelligent

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Prerequisites

There is no formal requirement for this course. However, it is recommended to have:

  • Basic Statistics: A fundamental understanding of statistics (mean, median, standard deviation, etc.) will aid in grasping the course content, especially when learning data analysis techniques.
  • Mathematics Fundamentals: Basic math skills, especially in areas like algebra and probability, will help in understanding some of the more advanced data analysis and modeling topics.
  • Familiarity with Data: A basic understanding of datasets, data types (numerical, categorical), and structures like tables will be useful.

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Licensing and accreditation

Data Science with R Programming Certification training is offered by Simplilearn. AVC promote this course based on Partner's Agreement and meets the accreditation requirements.

Equality policy

Simplilearn currently does not provide test accommodations due to a disability or medical condition of any students. Candidates are encouraged to reach out to AVC for guidance and support throughout the accommodation process.

Frequently Asked Question

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