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Data Science with R Certification Course

Data Science with R certification course makes you an expert in data analytics using the R programming language. This data science with R course enables you to take your Data Science skills into a variety of companies, helping them analyze data...
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  • Data Science with R Certification Course

Data Science with R Certification Course

Data Science with R certification course makes you an expert in data analytics using the R programming language. This data science with R course enables you to take your Data Science skills into a variety of companies, helping them analyze data and make more informed business decisions. With dedicated mentoring sessions, this R course incorporates cutting-edge curriculum to help you develop job-ready skills. Enroll in our R training and build expertise in data science right away.

  • Course Overview
  • Course Content
  • Exam & Certification
  • FAQs

About the course

The Data Science with R programming certification training 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.

The Big Data Analytics market is expected to reach $40.6 billion by 2023, at a growth rate of 29.7-percent. Randstad reports that pay hikes in the analytics industry are 50-percent higher than the IT industry. Learning R can help you begin a career in data science.

Eligibility

This Data Science with R certification training is beneficial for all aspiring data scientists including, IT professionals or software developers looking to make a career switch into Data analytics, professionals working in data and business analysis, graduates wishing to build a career in Data Science, and experienced professionals willing to harness Data Science in their fields.

Pre-requisites

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.

Skills Covered

  • 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

Course Content

Data Science with R Programming

  • Lesson 00 - Course Introduction
    • Course Introduction
    • Accessing Practice Lab
  • Lesson 01 - Introduction to Business Analytics
    • 1.1 Overview
    • 1.2 Business Decisions and Analytics
    • 1.3 Types of Business Analytics
    • 1.4 Applications of Business Analytics
    • 1.5 Data Science Overview
    • 1.6 Conclusion
    • Knowledge Check
  • Lesson 02 - Introduction to R Programming
    • 2.1 Overview
    • 2.2 Importance of R
    • 2.3 Data Types and Variables in R
    • 2.4 Operators in R
    • 2.5 Conditional Statements in R
    • 2.6 Loops in R
    • 2.7 R script
    • 2.8 Functions in R
    • 2.9 Conclusion
    • Knowledge Check
  • Lesson 03 - Data Structures
    • 3.1 Overview
    • 3.2 Identifying Data Structures
    • 3.3 Demo Identifying Data Structures
    • 3.4 Assigning Values to Data Structures
    • 3.5 Data Manipulation
    • 3.6 Demo Assigning values and applying functions
    • 3.7 Conclusion
    • Knowledge Check
  • Lesson 04 - Data Visualization
    • 4.1 Overview
    • 4.2 Introduction to Data Visualization
    • 4.3 Data Visualization using Graphics in R
    • 4.4 ggplot2
    • 4.5 File Formats of Graphic Outputs
    • 4.6 Conclusion
    • Knowledge Check
  • Lesson 05 - Statistics for Data Science-I
    • 5.1 Overview
    • 5.2 Introduction to Hypothesis
    • 5.3 Types of Hypothesis
    • 5.4 Data Sampling
    • 5.5 Confidence and Significance Levels
    • 5.6 Conclusion
    • Knowledge Check
  • Lesson 06 - Statistics for Data Science-II
    • 6.1 Overview
    • 6.2 Hypothesis Test
    • 6.3 Parametric Test
    • 6.4 Non-Parametric Test
    • 6.5 Hypothesis Tests about Population Means
    • 6.6 Hypothesis Tests about Population Variance
    • 6.7 Hypothesis Tests about Population Proportions
    • 6.8 Conclusion
    • Knowledge Check
  • Lesson 07 - Regression Analysis
    • 7.1 Overview
    • 7.2 Introduction to Regression Analysis
    • 7.3 Types of Regression Analysis Models
    • 7.4 Linear Regression
    • 7.5 Demo Simple Linear Regression
    • 7.6 Non-Linear Regression
    • 7.7 Demo Regression Analysis with Multiple Variables
    • 7.8 Cross Validation
    • 7.9 Non-Linear to Linear Models
    • 7.10 Principal Component Analysis
    • 7.11 Factor Analysis
    • 7.12 Conclusion
    • Knowledge Check
  • Lesson 08 - Classification
    • 8.1 Overview
    • 8.2 Classification and Its Types
    • 8.3 Logistic Regression
    • 8.4 Support Vector Machines
    • 8.5 Demo Support Vector Machines
    • 8.6 K-Nearest Neighbours
    • 8.7 Naive Bayes Classifier
    • 8.8 Demo Naive Bayes Classifier
    • 8.9 Decision Tree Classification
    • 8.10 Demo Decision Tree Classification
    • 8.11 Random Forest Classification
    • 8.12 Evaluating Classifier Models
    • 8.13 Demo K-Fold Cross Validation
    • 8.14 Conclusion
    • Knowledge Check
  • Lesson 09 - Clustering
    • 9.1 Overview
    • 9.2 Introduction to Clustering
    • 9.3 Clustering Methods
    • 9.4 Demo K-means Clustering
    • 9.5 Demo Hierarchical Clustering
    • 9.6 Conclusion
    • Knowledge Check
  • Lesson 10 - Association
    • 10.1 Overview
    • 10.2 Association Rule
    • 10.3 Apriori Algorithm
    • 10.4 Demo Apriori Algorithm
    • 10.5 Conclusion
    • Knowledge Check

Math Refresher

  • Math Refresher
    • Math Refresher

Statistics Essential for Data Science

  • Lesson 01: Course Introduction
    • 1.1 Course Introduction
    • 1.2 What Will You Learn
  • Lesson 02: Introduction to Statistics
    • 2.1 Learning Objectives
    • 2.2 What Is Statistics
    • 2.3 Why Statistics
    • 2.4 Difference between Population and Sample
    • 2.5 Different Types of Statistics
    • 2.6 Importance of Statistical Concepts in Data Science
    • 2.7 Application of Statistical Concepts in Business
    • 2.8 Case Studies of Statistics Usage in Business
    • 2.9 Recap
  • Lesson 03: Understanding the Data
    • 3.1 Learning Objectives
    • 3.2 Types of Data in Business Contexts clas
    • 3.3 Data Categorization and Types of Data
    • 3.3 Types of Data Collection
    • 3.4 Types of Data
    • 3.5 Structured vs. Unstructured Data clas
    • 3.6 Sources of Data
    • 3.7 Data Quality Issues
    • 3.8 Recap
  • Lesson 04: Descriptive Statistics
    • 4.1 Learning Objectives
    • 4.2 Mathematical and Positional Averages clas
    • 4.3 Measures of Central Tendancy: Part A
    • 4.4 Measures of Central Tendancy: Part B
    • 4.5 Measures of Dispersion
    • 4.6 Range Outliers Quartiles Deviation
    • 4.7 Mean Absolute Deviation (MAD) Standard Deviation Variance
    • 4.8 Z Score and Empirical Rule
    • 4.9 Coefficient of Variation and Its Application
    • 4.10 Measures of Shape
    • 4.11 Summarizing Data
    • 4.12 Recap
    • 4.13 Case Study One: Descriptive Statistics
  • Lesson 05: Data Visualization
    • 5.1 Learning Objectives
    • 5.2 Data Visualization
    • 5.3 Basic Charts
    • 5.4 Advanced Charts
    • 5.5 Interpretation of the Charts
    • 5.6 Selecting the Appropriate Chart
    • 5.7 Charts Do's and Dont's
    • 5.8 Story Telling With Charts
    • 5.9 Recap
    • 5.10 Case Study Two: Data Visualization
  • Lesson 06: Probability
    • 6.1 Learning Objectives
    • 6.2 Introduction to Probability
    • 6.3 Key Terms in Probability
    • 6.4 Conditional Probability
    • 6.5 Types of Events: Independent and Dependent
    • 6.6 Addition Theorem of Probability
    • 6.7 Multiplication Theorem of Probability
    • 6.8 Bayes Theorem
    • 6.9 Recap
  • Lesson 07: Probability Distributions
    • 7.1 Learning Objectives
    • 7.2 Random Variable
    • 7.3 Probability Distributions Discrete vs.Continuous: Part A
    • 7.4 Probability Distributions Discrete vs.Continuous: Part B
    • 7.5 Commonly Used Discrete Probability Distributions: Part A
    • 7.6 Discrete Probability Distributions: Poisson
    • 7.7 Binomial by Poisson Theorem
    • 7.8 Commonly Used Continuous Probability Distribution
    • 7.9 Applicaton of Normal Distribution
    • 7.10 Recap
  • Lesson 08: Sampling and Sampling Techniques
    • 8.1 Learnning Objectives
    • 8.2 Introduction to Sampling and Sampling Errors
    • 8.3 Advantages and Disadvantages of Sampling
    • 8.4 Probability Sampling Methods: Part A
    • 8.5 Probability Sampling Methods: Part B clas
    • 8.6 Non-Probability Sampling Methods: Part A clas
    • 8.7 Non-Probability Sampling Methods: Part B
    • 8.8 Uses of Probability Sampling and Non-Probability Sampling
    • 8.9 Sampling
    • 8.10 Probability Distribution
    • 8.11 Theorem Five Point One
    • 8.12 Center Limit Theorem
    • 8.13 Recap
    • 8.14 Case Study Three: Sample and Sampling Techniques
    • 8.15 Spotlight
  • Lesson 09: Inferential Statistics
    • 9.1 Learning Objectives
    • 9.2 Hypothesis and Hypothesis Testing in Businesses
    • 9.3 Null and Alternate Hypothesis
    • 9.4 P Value
    • 9.5 Levels of Significance
    • 9.6 Type One and Two Errors
    • 9.7 Z Test
    • 9.8 Confidence Intervals and Percentage Significance Level: Part A
    • 9.9 Confidence Intervals: Part B
    • 9.10 One Tail and Two Tail Tests
    • 9.11 Notes to Remember for Null Hypothesis
    • 9.12 Alternate Hypothesis
    • 9.13 Recap
    • 9.14 Case Study Four: Inferential Statistics
    • Hypothesis Testing
  • Lesson 10: Application of Inferential Statistics
    • 10.1 Learning Objectives
    • 10.2 Bivariate Analysis
    • 10.3 Selecting the Appropriate Test for EDA
    • 10.4 Parametric vs. Non-Parametric Tests
    • 10.5 Test of Significance
    • 10.6 Z Test
    • 10.7 T Test
    • 10.8 Parametric Tests ANOVA
    • 10.9 Chi-Square Test
    • 10.10 Sign Test
    • 10.11 Kruskal Wallis Test
    • 10.12 Mann Whitney Wilcoxon Test
    • 10.13 Run Test for Randomness
    • 10.14 Recap
  • Lesson 11: Relation between Variables
    • 11.1 Learning Objectives
    • 11.2 Correlation
    • 11.3 Karl Pearson's Coefficient of Correlation
    • 11.4 Karl Pearsons: Use Cases
    • 11.5 Spearmans Rank Correlation Coefficient
    • 11.6 Causation
    • 11.7 Example of Regression
    • 11.8 Coefficient of Determination
    • 11.9 Quantifying Quality
    • 11.10 Recap
  • Lesson 12: Application of Statistics in Business
    • 12.1 Learning Objectives
    • 12.2 How to Use Statistics In Day to Day Business
    • 12.3 Example: How to Not Lie With Statistics
    • 12.4 How to Not Lie With Statistics
    • 12.5 Lying Through Visualizations
    • 12.6 Lying About Relationships
    • 12.7 Recap
    • 12.8 Spotlight
  • Lesson 13: Assisted Practice
    • Assisted Practice: Problem Statement
    • Assisted Practice: Solution

What do I need to do to unlock my certificate?

To obtain the certification, you must:

  • Complete 85% of the course.
  • Complete 1 project

FAQs

  • What is R programming?

    R is a programming language and free software developed in 1993, made up of a collection of libraries architectured especially for data science. As a tool, R is considered to be clear and accessible.

  • How do I enrol in this online training?

    You can enrol in this training on our website and make an online payment using any of the following option

    • Visa Credit or Debit Card
    • MasterCard
    • American Express
    • Diners Club
    • PayPal

    Once payment is received, you will automatically receive a payment receipt and access information via email.

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