Data Science with R Certification Course

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