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Beginner to Advanced

Data Science with R Programming Tutorial for Beginners to Expert

15 Chapters
150 Lessons
16+ Hours
120+ Code Examples
15K+ Learners
Start Learning Practice Questions
96%

Completion Rate

4.9★

Rating

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What You'll Learn

Prerequisites

  • No prior programming experience needed
  • Basic computer knowledge
  • A laptop with internet access
  • Enthusiasm to learn!

Tools & Setup

📖 Tutorial Chapters & Curriculum

Follow the structured learning path from beginner to advanced

1

Introduction to Data Science with R

1 Lessons 7 min Beginner
Learn what data science is and how R programming helps analyze and visualize data.
2

Introduction to R Programming

2 Lessons 6 min Beginner
Learn the basics of R programming and why it is widely used for data science and statistical computing.
3

Installing R Programming Language

3 Lessons 6 min Beginner
Learn how to install the R programming language for data science and statistical computing.
4

Introduction to Data Structures in R

4 Lessons 6 min Beginner
Learn the different types of data structures used in R programming for data analysis.
5

Introduction to dplyr for Data Manipulation

5 Lessons 6 min Beginner
Learn how the dplyr package simplifies data manipulation in R.
6

Introduction to Data Cleaning in R

6 Lessons 6 min Beginner
Learn why data cleaning is an essential step in data science projects.
7

Introduction to Exploratory Data Analysis in R

7 Lessons 6 min Beginner
Learn the basics of exploratory data analysis (EDA) and how it helps understand datasets before building machine learning models.
8

Introduction to Data Visualization with ggplot2

8 Lessons 6 min Beginner
Learn how ggplot2 helps create powerful and professional data visualizations in R.
9

Introduction to Feature Engineering in R

9 Lessons 6 min Beginner
Learn what feature engineering is and why it is essential for machine learning models.
10

Introduction to Machine Learning with R

10 Lessons 7 min Beginner
Learn the fundamentals of machine learning and how R is used to build predictive models.
11

Introduction to Supervised Machine Learning in R

11 Lessons 6 min Beginner
Learn the fundamentals of supervised machine learning and how labeled datasets are used for prediction.
12

Introduction to Unsupervised Learning in R

12 Lessons 6 min Beginner
Learn the fundamentals of unsupervised machine learning and how it discovers patterns in unlabeled data.
13

Introduction to Model Evaluation in R

13 Lessons 6 min Beginner
Learn how machine learning models are evaluated to measure prediction performance.
14

Introduction to Real World Data Science Projects in R

14 Lessons 7 min Beginner
Learn how real world data science projects are built using R programming.
15

Introduction to Data Science Careers with R

15 Lessons 6 min Beginner
Learn about career opportunities in data science using R programming.
16

Data Science Lifecycle in R

16 Lessons 6 min Beginner
Understand the step-by-step lifecycle of a data science project.
17

R Variables

17 Lessons 6 min Beginner
Learn how to declare and use variables in R programming.
18

Installing RStudio IDE

18 Lessons 6 min Beginner
Learn how to install RStudio IDE for professional R programming.
19

Vectors in R

19 Lessons 6 min Beginner
Learn how vectors store ordered data in R programming.
20

Selecting Columns using select()

20 Lessons 6 min Beginner
Learn how to select specific columns from datasets using dplyr.
21

Handling Missing Values in R

21 Lessons 6 min Beginner
Learn techniques to identify and handle missing values in datasets.
22

Summary Statistics in R

22 Lessons 6 min Beginner
Learn how summary statistics help understand datasets quickly.
23

Scatter Plots with ggplot2

23 Lessons 6 min Beginner
Learn how to create scatter plots using ggplot2.
24

Feature Scaling in R

24 Lessons 6 min Beginner
Learn how to scale features for machine learning algorithms.
25

Types of Machine Learning in R

25 Lessons 6 min Beginner
Learn the major types of machine learning algorithms.
26

Linear Regression in R

26 Lessons 6 min Beginner
Learn how linear regression predicts continuous numerical values.
27

Clustering Concepts in R

27 Lessons 6 min Beginner
Understand how clustering algorithms group similar data points together.
28

Confusion Matrix in R

28 Lessons 6 min Beginner
Learn how confusion matrix helps evaluate classification models.
29

Sales Forecasting Project using R

29 Lessons 7 min Intermediate
Learn how to predict sales using machine learning models in R.
30

Essential Data Science Skills for R Professionals

30 Lessons 6 min Beginner
Learn the key technical and analytical skills required for data science roles.
31

Applications of Data Science

31 Lessons 6 min Beginner
Explore real-world applications of data science across industries.
32

R Data Types

32 Lessons 6 min Beginner
Understand different data types used in R programming.
33

RStudio Interface Overview

33 Lessons 6 min Beginner
Understand the RStudio interface and workspace layout.
34

Matrices in R

34 Lessons 6 min Beginner
Learn how matrices organize data in rows and columns.
35

Filtering Rows using filter()

35 Lessons 6 min Beginner
Learn how to filter rows based on conditions using dplyr.
36

Removing Missing Values

36 Lessons 6 min Beginner
Learn how to remove rows containing missing values.
37

Understanding Data Distribution in R

37 Lessons 6 min Beginner
Learn how to analyze data distribution patterns.
38

Bar Charts with ggplot2

38 Lessons 6 min Beginner
Learn how to create bar charts for categorical data.
39

Data Normalization in R

39 Lessons 6 min Beginner
Learn how normalization transforms features to a standard range.
40

Machine Learning Workflow in R

40 Lessons 6 min Beginner
Learn the typical workflow of machine learning projects.
41

Logistic Regression in R

41 Lessons 6 min Beginner
Learn how logistic regression predicts categorical outcomes.
42

K Means Clustering in R

42 Lessons 6 min Intermediate
Learn how the K-Means algorithm groups data into clusters.
43

Precision and Recall in R

43 Lessons 6 min Beginner
Learn how precision and recall measure classification accuracy.
44

Customer Segmentation using R

44 Lessons 7 min Intermediate
Learn how clustering algorithms segment customers into groups.
45

Building a Data Science Portfolio using R

45 Lessons 6 min Beginner
Learn how to build an impressive data science portfolio.
46

Data Science Tools for R

46 Lessons 6 min Beginner
Learn the essential tools used by R data scientists.
47

R Operators

47 Lessons 6 min Beginner
Learn arithmetic and logical operators in R.
48

Working Directory in R

48 Lessons 6 min Beginner
Learn how to manage working directories in R programming.
49

Arrays in R

49 Lessons 6 min Beginner
Learn how arrays extend matrices into multiple dimensions.
50

Creating New Columns with mutate()

50 Lessons 6 min Beginner
Learn how to create new variables using mutate in dplyr.
51

Imputing Missing Values in R

51 Lessons 6 min Intermediate
Learn how to replace missing values using statistical techniques.
52

Correlation Analysis in R

52 Lessons 6 min Beginner
Learn how to identify relationships between variables.
53

Line Charts with ggplot2

53 Lessons 6 min Beginner
Learn how to visualize trends using line charts.
54

Feature Transformation Techniques

54 Lessons 6 min Beginner
Learn how transformations improve feature distributions.
55

Training and Testing Data in R

55 Lessons 6 min Beginner
Learn how datasets are split into training and testing sets.
56

Decision Trees in R

56 Lessons 6 min Intermediate
Learn how decision tree models split datasets to make predictions.
57

Hierarchical Clustering in R

57 Lessons 6 min Intermediate
Learn how hierarchical clustering builds nested clusters.
58

F1 Score in R

58 Lessons 6 min Beginner
Learn how F1 score balances precision and recall.
59

Fraud Detection System using R

59 Lessons 7 min Intermediate
Learn how machine learning detects fraudulent transactions.
60

Data Science Resume Preparation for R Professionals

60 Lessons 6 min Beginner
Learn how to create a strong resume for data science roles.
61

R vs Python for Data Science

61 Lessons 6 min Beginner
Compare R and Python for data science applications.
62

Conditional Statements in R

62 Lessons 6 min Beginner
Learn how to use if and else statements in R programming.
63

Installing R Packages

63 Lessons 6 min Beginner
Learn how to install packages in R for data science.
64

Lists in R

64 Lessons 6 min Beginner
Learn how lists store different types of data in R.
65

Sorting Data with arrange()

65 Lessons 6 min Beginner
Learn how to sort datasets using arrange in dplyr.
66

Removing Duplicate Records in R

66 Lessons 6 min Beginner
Learn how to remove duplicate rows from datasets.
67

Scatter Plots in R

67 Lessons 6 min Beginner
Learn how scatter plots visualize relationships between variables.
68

Histograms with ggplot2

68 Lessons 6 min Beginner
Learn how to visualize data distributions using histograms.
69

Encoding Categorical Variables in R

69 Lessons 6 min Beginner
Learn how to convert categorical variables into numerical values.
70

Regression Models in R

70 Lessons 6 min Beginner
Learn how regression algorithms predict numerical values.
71

Random Forest in R

71 Lessons 6 min Intermediate
Learn how ensemble learning improves prediction accuracy.
72

DBSCAN Clustering in R

72 Lessons 6 min Intermediate
Learn how DBSCAN detects clusters based on density.
73

ROC Curve and AUC in R

73 Lessons 6 min Intermediate
Learn how ROC curves evaluate classification performance.
74

Recommendation System Project in R

74 Lessons 7 min Intermediate
Learn how recommendation systems suggest products or content.
75

Top Data Science Interview Questions with R

75 Lessons 6 min Intermediate
Learn the most common data science interview questions.
76

Data Science Careers with R

76 Lessons 6 min Beginner
Learn about career opportunities for R data scientists.
77

Loops in R Programming

77 Lessons 6 min Beginner
Learn how loops automate repetitive tasks in R.
78

Loading R Packages

78 Lessons 6 min Beginner
Learn how to load and manage installed packages.
79

Data Frames in R

79 Lessons 6 min Beginner
Learn how data frames store structured tabular data.
80

Grouping Data with group_by()

80 Lessons 6 min Beginner
Learn how to group datasets for aggregation in dplyr.
81

Outlier Detection in R

81 Lessons 6 min Intermediate
Learn how to identify extreme values in datasets.
82

Histograms in R

82 Lessons 6 min Beginner
Learn how histograms display data distributions.
83

Box Plots with ggplot2

83 Lessons 6 min Beginner
Learn how box plots visualize data spread and outliers.
84

Polynomial Features in R

84 Lessons 6 min Intermediate
Learn how polynomial features improve nonlinear models.
85

Classification Models in R

85 Lessons 6 min Intermediate
Learn how classification algorithms categorize data.
86

K Nearest Neighbors Algorithm in R

86 Lessons 6 min Intermediate
Learn how the KNN algorithm classifies data based on similarity.
87

Dimensionality Reduction in R

87 Lessons 6 min Intermediate
Learn how dimensionality reduction simplifies large datasets.
88

Cross Validation in R

88 Lessons 6 min Intermediate
Learn how cross validation improves model reliability.
89

Stock Price Prediction using R

89 Lessons 7 min Intermediate
Learn how machine learning predicts stock price trends.
90

Statistics Interview Questions for Data Science

90 Lessons 6 min Intermediate
Learn common statistics questions asked in data science interviews.
91

Future of Data Science

91 Lessons 6 min Beginner
Understand the future trends of data science and AI.
92

Functions in R

92 Lessons 6 min Beginner
Learn how to create reusable functions in R programming.
93

CRAN Repository

93 Lessons 6 min Beginner
Learn about the Comprehensive R Archive Network (CRAN).
94

Factors in R

94 Lessons 6 min Beginner
Learn how categorical data is handled using factors in R.
95

Summarizing Data using summarise()

95 Lessons 6 min Intermediate
Learn how to summarize grouped data using dplyr.
96

Data Normalization in R

96 Lessons 6 min Intermediate
Learn how to normalize data for machine learning.
97

Box Plots in R

97 Lessons 6 min Beginner
Learn how box plots identify outliers and data spread.
98

Faceting in ggplot2

98 Lessons 6 min Intermediate
Learn how to split visualizations into multiple panels.
99

Feature Selection in R

99 Lessons 6 min Intermediate
Learn how to select the most important features for machine learning.
100

Making Predictions using ML Models

100 Lessons 6 min Intermediate
Learn how trained machine learning models make predictions.
101

Support Vector Machine in R

101 Lessons 6 min Intermediate
Learn how SVM algorithms classify complex datasets.
102

Principal Component Analysis in R

102 Lessons 6 min Intermediate
Learn how PCA reduces dimensionality of datasets.
103

Hyperparameter Tuning in R

103 Lessons 6 min Intermediate
Learn how to tune model parameters for better performance.
104

Customer Churn Prediction Project

104 Lessons 7 min Intermediate
Learn how machine learning predicts customer churn.
105

Machine Learning Interview Questions using R

105 Lessons 6 min Intermediate
Prepare for machine learning interview questions.
106

Data Science Workflow using R

106 Lessons 6 min Beginner
Learn how R is used across a full data science workflow.
107

R Packages

107 Lessons 6 min Beginner
Learn how packages extend R functionality.
108

Working with R Script Files

108 Lessons 6 min Beginner
Learn how to create and run R script files.
109

Subsetting Data in R

109 Lessons 6 min Beginner
Learn how to extract specific elements from data structures.
110

Joining Datasets with dplyr

110 Lessons 6 min Intermediate
Learn how to merge datasets using join functions.
111

Data Transformation Techniques

111 Lessons 6 min Intermediate
Learn how to transform datasets for analysis.
112

Feature Relationship Analysis

112 Lessons 6 min Intermediate
Learn how to analyze relationships between multiple variables.
113

Customizing Themes in ggplot2

113 Lessons 6 min Intermediate
Learn how to customize the appearance of charts.
114

Feature Importance in R

114 Lessons 6 min Intermediate
Learn how to measure the importance of variables in machine learning.
115

Bias Variance Tradeoff in R

115 Lessons 6 min Intermediate
Learn how bias and variance affect machine learning models.
116

Naive Bayes Algorithm in R

116 Lessons 6 min Intermediate
Learn how probabilistic classifiers work in machine learning.
117

Cluster Evaluation Techniques

117 Lessons 6 min Intermediate
Learn how to evaluate clustering results.
118

Grid Search in R

118 Lessons 6 min Intermediate
Learn how grid search finds the best hyperparameters.
119

Sentiment Analysis Project in R

119 Lessons 7 min Intermediate
Learn how natural language processing analyzes text sentiment.
120

Data Science Case Study Interview Preparation

120 Lessons 6 min Intermediate
Learn how to solve case study problems in data science interviews.
121

Statistics for Data Science

121 Lessons 6 min Beginner
Learn why statistics is important for data science.
122

Data Frames in R

122 Lessons 6 min Beginner
Learn how to work with tabular data using data frames.
123

Using RStudio Projects

123 Lessons 6 min Beginner
Learn how to organize data science projects using RStudio.
124

Data Structure Conversion in R

124 Lessons 6 min Beginner
Learn how to convert between vectors, matrices, and data frames.
125

Using the Pipe Operator %>%

125 Lessons 6 min Intermediate
Learn how the pipe operator simplifies data transformation workflows.
126

Encoding Categorical Data in R

126 Lessons 6 min Intermediate
Learn how categorical variables are encoded for machine learning.
127

Extracting Data Insights in R

127 Lessons 6 min Intermediate
Learn how to interpret patterns and trends from data analysis.
128

Interactive Visualization in R

128 Lessons 6 min Intermediate
Learn how to create interactive charts in R.
129

Dimensionality Reduction in R

129 Lessons 6 min Intermediate
Learn how dimensionality reduction simplifies datasets.
130

Machine Learning Pipelines in R

130 Lessons 6 min Intermediate
Learn how pipelines automate machine learning workflows.
131

Ensemble Learning in R

131 Lessons 6 min Intermediate
Learn how ensemble models combine predictions from multiple models.
132

Applications of Clustering in Data Science

132 Lessons 6 min Intermediate
Learn real world applications of clustering algorithms.
133

Overfitting vs Underfitting in R

133 Lessons 6 min Intermediate
Learn how to detect and prevent overfitting in machine learning models.
134

Image Classification Project in R

134 Lessons 7 min Advanced
Learn how machine learning classifies images.
135

Kaggle Practice Roadmap for Data Scientists

135 Lessons 6 min Beginner
Learn how Kaggle competitions help improve data science skills.
136

Why Learn R Programming for Data Science

136 Lessons 7 min Beginner
Understand why R is widely used for statistical computing.
137

R Programming Best Practices

137 Lessons 7 min Beginner
Learn best practices for writing clean and efficient R code.
138

RStudio Productivity Tips

138 Lessons 7 min Beginner
Learn tips and shortcuts to improve productivity in RStudio.
139

Built-in Datasets in R

139 Lessons 7 min Beginner
Learn how to use built-in datasets for practice and analysis.
140

dplyr Best Practices for Data Analysis

140 Lessons 7 min Intermediate
Learn best practices for efficient data manipulation using dplyr.
141

Data Validation and Preprocessing Best Practices

141 Lessons 7 min Intermediate
Learn best practices for data cleaning and preprocessing.
142

EDA Best Practices in R

142 Lessons 7 min Intermediate
Learn best practices for performing exploratory data analysis.
143

Data Visualization Best Practices in R

143 Lessons 7 min Intermediate
Learn best practices for creating clear and effective visualizations.
144

Feature Engineering Pipelines in R

144 Lessons 7 min Intermediate
Learn how to automate feature engineering workflows.
145

Machine Learning Best Practices in R

145 Lessons 7 min Intermediate
Learn best practices for building reliable ML models.
146

Regression vs Classification in R

146 Lessons 7 min Beginner
Understand the difference between regression and classification problems.
147

Unsupervised Learning Best Practices

147 Lessons 7 min Intermediate
Learn best practices for building unsupervised learning models.
148

Model Optimization Techniques in R

148 Lessons 7 min Advanced
Learn techniques to optimize machine learning model performance.
149

Data Science Case Studies using R

149 Lessons 8 min Advanced
Learn how data science is applied across industries.
150

Freelancing and Job Opportunities in Data Science

150 Lessons 7 min Beginner
Learn how to start freelancing or get jobs in data science.
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🎯 Interview Preparation

Top Python interview questions organized by difficulty

Easy 70 Questions

Freshers / Entry Level

  • Explain Vectors and Data Types in R Data Science. Provide statistical reasoning and practical implementation details. (Q1)
  • Explain Data Frames in R in R Data Science. Provide statistical reasoning and practical implementation details. (Q2)
  • Explain Factors in R in R Data Science. Provide statistical reasoning and practical implementation details. (Q3)
  • Explain Control Flow in R in R Data Science. Provide statistical reasoning and practical implementation details. (Q4)
  • Explain Functions in R in R Data Science. Provide statistical reasoning and practical implementation details. (Q5)
View All 70 Questions →
Medium 80 Questions

Experienced / Mid-Level

  • Explain Linear Regression in R in R Data Science. Provide statistical reasoning and practical implementation details. (Q71)
  • Explain Logistic Regression in R in R Data Science. Provide statistical reasoning and practical implementation details. (Q72)
  • Explain ANOVA in R in R Data Science. Provide statistical reasoning and practical implementation details. (Q73)
  • Explain Time Series in R in R Data Science. Provide statistical reasoning and practical implementation details. (Q74)
  • Explain ARIMA modeling in R Data Science. Provide statistical reasoning and practical implementation details. (Q75)
View All 80 Questions →
Hard 70 Questions

Senior / Lead Level

  • Explain Vectors and Data Types in R Data Science. Provide statistical reasoning and practical implementation details. (Q151)
  • Explain Data Frames in R in R Data Science. Provide statistical reasoning and practical implementation details. (Q152)
  • Explain Factors in R in R Data Science. Provide statistical reasoning and practical implementation details. (Q153)
  • Explain Control Flow in R in R Data Science. Provide statistical reasoning and practical implementation details. (Q154)
  • Explain Functions in R in R Data Science. Provide statistical reasoning and practical implementation details. (Q155)
View All 70 Questions →

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