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R Programming for Data Science- Practise 250 Exercises-Part1

Name: R Programming for Data Science- Practise 250 Exercises-Part1
Rating: 4.1 (37 reviews)

Learn by Doing: Practical R Programming with Data Frames, ggplot2, and dplyr for Data Science using RStudio

Created bySheik Jamil Ahmed

Last updated 3/2025

English

What you'll learn

Develop a strong foundation in R programming by solving diverse exercises, reinforcing key concepts like data types, control structures, and functions.
Gain hands-on experience with popular R libraries such as dplyr, ggplot2, tidyverse, and caret to manipulate and visualize datasets effectively.
Apply data wrangling techniques to clean, transform, and organize real-world datasets using R.
Master data visualization by creating insightful and professional-quality plots with ggplot2 and other visualization libraries.
Enhance your statistical analysis skills by performing descriptive statistics, hypothesis testing, and regression analysis in R.
Explore different datasets available in R and use them to practice machine learning algorithms such as linear regression, classification, and clustering.
Debug and optimize R code by identifying common errors and applying best practices for efficient coding.
Prepare for real-world data science challenges by solving exercises that reflect common tasks in data analysis and machine learning projects.

Course content

14 sections • 507 lectures • 2h 54m total length

Welcome to the course0:30
Introduction to R Programming Language3:00
Programming Index0:01
Unlocking AI, ML and DL Simplified3:02
Course Overview2:03
Installing R and R Studio0:44
R Programming for Data Science Part20:02

Problem 10:31
Solution 10:01
Problem 20:40
Tackle problem 2 in the R programming for data science, practise 250 exercises part 1 course, building hands-on R skills through focused problem-solving practice.
Soution 20:35
Problem 30:31
Tackle problem 3 in R programming for data science practice 250 exercises part 1 to reinforce data science skills through hands-on R practice.
Solution 30:22
Problem 40:30
Solution 40:20
Problem 50:30
Solution 51:38
Problem 60:31
Solution 60:32
Problem 70:31
Solution 70:32
Problem 80:33
Solution 81:00
Explore solution 8 for R programming in data science, as you work through part 1 of the 250 exercises to reinforce core R skills.
Problem 90:31
Solution 90:05
Problem 100:30
Solution 101:00
Explore solution 10 from part 1 of R programming for data science, practicing 250 exercises to strengthen problem-solving and coding skills.
Problem 110:31
practice 250 R programming exercises for data science, covering problem 11 in part 1 curriculum.
Solution 111:00
review solution 11 for exercise 11 in the r programming for data science practice series, part 1. reinforce core r coding skills through 250 hands-on exercises.
Problem 120:34
Tackle problem 12 from the R programming for data science practice set in part 1, reinforcing core R programming skills through hands-on exercises.
Solution 121:11
Solve exercise 12 in the R programming for data science series part 1, delivering hands-on practice with 250 exercises to strengthen R skills.
Problem 130:31
Practice 250 exercises in r programming for data science, problem 13, part 1, for learners.
Solution 131:00
Problem 140:30
Solution 141:00
Problem 150:30
Solution 151:00
Problem 160:30
Tackle problem 16 in R programming for data science, practice 250 exercises, part 1, to reinforce foundational data analysis techniques.
Solution 161:00
Problem 170:30
Tackle problem 17 from the R programming for data science practise 250 exercises part 1 to strengthen practical R skills.
Solution 171:08
Problem 180:30
Solution 181:12
Problem 190:30
Solution 191:00
Problem 200:30
Solve problem 20 in R programming for data science, part 1, through 250 exercises now.
Solution 201:00
Solve solution 20 from the R programming for data science practice set, part 1, demonstrating practical techniques for completing one of 250 exercises.

Problem 210:32
Solution 211:21
Solve exercise 21 in R programming for data science, part 1, as part of the 250 practice exercises course.
Problem 220:30
Practice problem 22 from R programming for data science, part 1, as part of the practise 250 exercises series.
Solution 221:00
Problem 230:30
Solution 231:00
Solve exercise 23 in r programming for data science, part 1, as you work through 250 practice exercises.
Problem 240:30
Practice 250 exercises in data science with R programming by solving problem 24, reinforcing core data analysis skills and foundational coding techniques.
Solution 240:59
Problem 250:30
Master data science with R programming through practice. Tackle problem 25 from 250 exercises in part 1, enhancing core coding and data analysis skills.
Solution 251:00
Explore the solution to exercise 25 in the 250 exercises R programming for data science course, part 1.
Problem 260:31
Practice problem 26 from the R programming for data science course, part 1, as part of a 250-exercise series.
Solution 261:00
Problem 270:33
Solution 271:00
Problem 280:30
Practice solving problem 28 in the R programming for data science course, part 1, to reinforce core data analysis techniques through 250 hands-on exercises.
Solution 281:05
Problem 290:30
Practice 250 R programming exercises for data science, solving problem 29 from part 1 to strengthen coding skills and data-driven problem solving.
Solution 291:01
Problem 300:30
Solution 301:00
Problem 310:30
Solution 311:01
Problem 320:30
Solution 321:00
Problem 330:30
Solution 331:00
Problem 340:30
Practice 250 exercises part 1 in R programming for data science strengthens data analysis skills through problem 34, emphasizing hands-on coding and practical problem solving.
Solution 341:00
Problem 350:32
Solution 350:59
Master data science techniques in R by working through 250 practice exercises in part 1 and reviewing solution 35.
Problem 360:30
Tackle problem 36 from the R programming for data science practice 250 exercises part 1.
Solution 360:59
Problem 370:31
Solution 37
Problem 380:40
Solution 38
Problem 390:30
Solution 39
Problem 400:30
Solution 40

Problem 410:40
Solution 41
Problem 420:32
Practice problem 42 from the R programming for data science course, practice 250 exercises—part 1—to strengthen core data analysis and coding skills.
Solution 42
Problem 430:30
Solution 43
Problem 440:34
Solution 44
Problem 450:30
Solution 45
Problem 460:30
Solution 46
Problem 470:30
Solution 47
Problem 480:36
Solve problem 48 from R programming for data science part 1, part of a 250-exercise practice series to sharpen hands-on R programming skills.
Solution 48
Problem 490:30
Solution 49
Problem 500:30
Solution 50
Problem 510:30
Tackle problem 51 from the r programming for data science part 1 course, practicing a key exercise in the 250 exercises series to sharpen r coding and data analysis.
Solution 51
Problem 520:32
Tackle problem 52 from R programming for data science, part 1, and sharpen practical R skills through 250 practice exercises.
Solution 52
Problem 530:30
Solution 53
Problem 540:30
Solution 54
Problem 550:30
Tackle problem 55 from the R programming for data science course, part 1, as part of a 250-exercise practice series.
Solution 55
Problem 560:30
Solve problem 56 to strengthen R programming for data science through part 1, practicing 250 exercises to enhance data analysis and coding fluency.
Solution 56
Problem 570:30
Tackle problem 57 from the R programming for data science practice series, Part 1, and build skills through 250 hands-on exercises.
Solution 57
Problem 580:30
Solution 58
Problem 590:30
Solution 59
Problem 600:31
Solution 60

Problem 610:30
Tackle problem 61 in the R programming for data science practice set, part 1, and strengthen essential R coding skills through focused, hands-on exercises.
Solution 61
Problem 620:30
Solution 62
Problem 630:33
Solution 63
Problem 640:37
Solution 64
Problem 650:30
Solution 65
Problem 660:33
Solution 66
Problem 670:31
Tackle problem 67 in R programming for data science, part 1, as part of the practise 250 exercises series to reinforce data manipulation and basic coding skills.
Solution 67
Problem 680:31
Solution 68
Problem 690:30
tackle problem 69 in r programming for data science, part 1, and practice across 250 exercises.
Solution 69
Problem 700:30
Solution 70
Problem 710:31
Work through problem 71 in R programming for data science part 1, one of 250 practice exercises.
Solution 71
Problem 720:30
Tackle problem 72 from the R programming for data science practice set, part 1, to reinforce fundamental R coding skills and data analysis techniques.
Solution 72
Problem 730:30
Tackle problem 73 from the R programming for data science practice 250 exercises part 1 to build core data analysis skills through focused practice with R techniques.
Solution 73
Problem 740:30
Solution 74
Problem 750:30
Master problem 75 in R programming for data science by practicing from a 250-exercise part i set, building foundational skills.
Solution 75
Problem 760:31
Solve problem 76 from the R programming for data science course, part 1, as part of 250 practice exercises to strengthen R coding skills.
Solution 76
Problem 770:30
Practice problem 77 from R programming for data science, part 1, as part of a 250-exercise course designed to build practical R skills.
Solution 77
Problem 780:31
Solution 78
Problem 790:30
Solution 79
Problem 800:31
Tackle problem 80 from the R programming for data science practice 250 exercises part 1 set, reinforcing core data science programming skills through hands-on exercises.
Solution 80

Problem 810:31
Tackle problem 81 from the 250 exercise series in R programming for data science part 1, reinforcing hands-on data analysis practice.
Solution 81
Problem 820:30
Solution 82
P roblem 830:39
Solution 83
Problem 840:34
Solution 84
Problem 850:37
practice problem 85 from the R programming for data science course, part 1, as you work through one of the 250 exercises.
Solution 85
Problem 860:30
Solution 86
Problem 870:30
Solution 87
Problem 880:30
Solution 88
Problem 890:49
Tackle problem 89 in R programming for data science, part 1, as part of the 250 practice exercises to reinforce R programming skills.
Solution 89
Problem 900:30
Solution 90
Problem 910:30
Solve problem 91 in the R programming for data science practise 250 exercises part 1 course, reinforcing data science skills through practical coding challenges.
Solution 91
Problem 920:31
Solution 92
Problem 930:30
Solution 93
Problem 940:32
Tackle problem 94 in R programming for data science as part of 250 practice exercises in this part 1 course.
Solution 94
Problem 950:31
Tackle problem 95 to reinforce R programming skills for data science through a practice exercise.
Solution 95
Problem 960:30
Solution 96
Problem 970:31
Solution 97
Problem 980:35
Solution 98
Problem 990:32
solve problem 99 in R programming for data science to reinforce practical skills through part 1 of the 250 exercises.
Solution 99
Problem 1000:47
Solution 100

Problem 1010:32
Solution 101
Problem 1020:30
Solution 102
Problem 1030:30
Solution 103
Problem 1040:31
Solution 104
Problem 1050:30
Solution 105
Problem 1060:31
Work on problem 106 from R programming for data science, practise 250 exercises in part 1.
Solution 106
Problem 1070:32
Solution 107
Problem 1080:31
Practice problem 108 in R programming for data science Part 1 teaches applying R language to data analysis through 250 exercises.
Solution 108
Problem 1090:30
Solution 109
Problem 1100:30
practice problem 110 in R programming for data science, part 1, designed to master data analysis skills through 250 exercises.
Solution 110
Problem 1110:30
Tackle problem 111 in R programming for data science, part 1, through 250 practice exercises to strengthen data analysis skills.
Soln 111
Problem 1120:30
Soln 112
Problem 1130:30
Soln 113
Problem 1140:35
Solution 114
Problem 1150:31
Solve problem 115 in a comprehensive practice set for data science with R, reinforcing core R programming skills through hands-on exercises.
Soln 115
Problem 1160:30
Soln 116
Problem 1170:30
Soln 117
Problem 1180:38
Soln 118
Problem 1190:33
Soln 119
Problem 1200:31
Soln 120

Problem 1210:30
Practice your R programming for data science with problem 121 in part 1 of a 250-exercise practice series.
Soln 121
Problem 1220:30
Soln 122
Problem 1230:31
Soln 123
Problem 1240:23
Soln 124
Problem 1250:30
Soln 125
Problem 1260:33
Soln 126
Problem 1270:30
Soln 127
Problem 1280:35
Soln 128
Problem 1290:32
Soln 129
Problem 1300:22
Tackle problem 130 within R programming for data science by practicing a structured set of 250 exercises, reinforcing core R concepts through hands-on coding practice.
Soln 130
Problem 1310:30
Soln 131
Problem 1320:30
Soln 132
Problem 1330:34
Soln 133
Problem 1340:31
Soln 134
Problem 1350:30
Soln 135
Problem 1360:32
Soln 136
Problem 1370:30
Soln 137
Problem 1380:30
Soln 138
Problem 1390:30
Soln 139
Problem 1400:39
Practice problem 140 from the r programming for data science part 1 series challenges you to apply core r coding skills through a focused, hands-on exercise.
Soln 140

Problem 1410:30
Soln 141
Problem 1420:34
Soln 142
Problem 1430:30
Tackle problem 143 to strengthen R programming and data science skills through part 1 of the 250 exercise series.
Soln 143
Problem 1440:31
Soln 144
Problem 1450:23
Solve problem 145 in the R programming for data science course, as part of the 250 exercises in part 1.
Soln 145
Problem 1460:30
Soln 146
Problem 1470:30
Soln 147
Problem 1480:30
Practice problem 148 in R programming for data science, part 1, as part of a series of 250 exercises.
Soln 148
Problem 1490:31
Tackle problem 149 within the data science focused practice set of 250 R programming exercises, reinforcing core R concepts and problem-solving techniques.
Soln 149
Problem 1501:00
Practice 250 exercises in R programming for data science, solving problem 150 to build data manipulation and analysis skills.
Soln 150
Problem 1510:31
Soln 151
Problem 1520:35
Solve problem 152 in the R programming for data science practice set part 1, sharpening practical skills across 250 exercises.
Soln 152
Problem 1530:31
Soln 153
Problem 1540:31
Soln 154
Problem 1550:30
Soln 155
Problem 1560:32
Soln 156
Problem 1570:31
Soln 157
Problem 1580:31
Tackle problem 158 in R programming for data science, part 1, through one of the 250 practice exercises.
Soln 158
Problem 1590:30
Soln 159
Problem 1600:30
Tackle problem 160 in R programming for data science as part 1 of the practise 250 exercises series.
Soln 160

Problem 1610:30
Soln 161
Problem 1620:33
Soln 162
Problem 1630:30
Soln 163
Problem 1640:30
Soln 164
Problem 1650:30
Soln 165
Problem 1660:30
Soln 166
Problem 1670:31
Soln 167
Problem 1680:30
Tackle problem 168 in the R programming for data science practice 250 exercises part 1.
Soln 168
Problem 1690:30
Soln 169
Problem 1700:36
Soln 170
Problem 1710:32
Soln 171
Problem 1720:31
Soln 172
Problem 1730:28
Soln 173
Problem 1740:36
Soln 174
Problem 1750:30
Soln 175
Problem 1760:34
Soln 176
Problem 1770:33
Soln 177
Problem 1780:31
Practice problem 178 in the R programming for data science course, part 1, by practicing from the 250 exercises to reinforce practical R skills.
Soln 178
Problem 1790:30
Soln 179
Problem 1800:30
Tackle problem 180 in the R programming for data science practice exercises, part 1, to strengthen practical coding skills and data handling in R.
Soln 180

Requirements

Basic understanding of programming concepts
Introductory knowledge of R programming
Familiarity with basic statistics and data analysis

Description

This course is designed to help you master R programming through 250 practical, hands-on exercises. Whether you’re a beginner or looking to strengthen your R skills, this course covers a wide range of topics that are essential for data science. Let’s dive into what this course has to offer!

1. Learn the Fundamentals of R Programming

Start by understanding the core concepts of R programming, including variables, data types, and basic syntax. These exercises will give you the foundation needed to tackle more advanced topics later in the course.

2. Master Data Cleaning and Transformation

Gain practical experience with data wrangling using popular libraries like dplyr and tidyverse. Learn to clean, transform, and organize real-world datasets, preparing them for analysis.

3. Visualize Data Using ggplot2

Data visualization is crucial in data science. In this section, you'll work with ggplot2 to create informative and attractive plots. This will help you gain insights from your data more effectively.

4. Explore Statistical Analysis Techniques

Get hands-on practice with statistics in R, learning how to calculate mean, median, variance, and standard deviation. You will also perform hypothesis testing and regression analysis.

5. Apply Machine Learning Algorithms

Work on basic machine learning techniques like linear regression, classification, and clustering using real datasets. This section will help you understand how to apply machine learning models in R.

6. Practice Debugging and Code Optimization

As you progress, you'll encounter coding challenges that will sharpen your debugging and optimization skills. Learn how to identify and fix errors in your code while ensuring it runs efficiently.

7. Work with Real-World Datasets

Throughout the course, you’ll be working with various real-world datasets available in R. From health statistics to economic data, these datasets provide a diverse range of challenges to solve.

8. Test Your Knowledge with Challenging Exercises

Each problem is designed to test your knowledge and improve your understanding of R. By the end of the course, you'll be equipped to apply R programming in real-world data science projects.

9. Get Ready for Part 2!

Once you've completed Part 1, you're encouraged to enroll in "R Programming for Data Science—Practice 250 Questions—Part 2" for even more advanced exercises and deeper insights into R programming. Keep the momentum going and continue mastering your skills!

Who this course is for:

Aspiring data scientists looking to strengthen their R programming skills through hands-on practice.
R programmers seeking to improve their problem-solving abilities and apply advanced R libraries in real-world data analysis.
Students and professionals in data science who want to enhance their understanding of data manipulation, visualization, and machine learning in R.
Self-learners and enthusiasts interested in applying R to solve diverse data challenges using real-world datasets.
Anyone preparing for data science job interviews or certifications that require proficiency in R programming and data analysis techniques.

R Programming for Data Science- Practise 250 Exercises-Part1

What you'll learn

Explore related topics

Course content

Introduction7 lectures • 9min

1-2040 lectures • 27min

21-4040 lectures • 27min

41-6040 lectures • 10min

61-8040 lectures • 10min

81-10040 lectures • 11min

101-12040 lectures • 10min

121-14040 lectures • 10min

141-16040 lectures • 11min

161-18040 lectures • 10min

Requirements

Description

Who this course is for: