CodeLucky

Subscribers 7.4K

Videos 10K

Views 1.1M

Channels Like CodeLucky

CodeLucky is a tech learning channel from India focused on coding, cloud, data, and finance across 40+ complete series with 5000+ videos. The channel emphasizes tutorials on languages like Python, JavaScript, C/C++, Kotlin, PHP, SQL, HTML, and topics such as Cloud, DevOps, Linux, Git, Docker, Kubernetes, Big Data, and AI/ML, with recent videos centered on statistics concepts like probability distributions, random variables, and expected value. It publishes a large volume of content (10,000 videos published) and averages around 56 views per video with typical runtimes around 4 minutes, indicating frequent uploads since joining Jan 2, 2025, from India.

Results last updated 3 months ago.

Similar Channels

We found 50 YouTube channels similar to CodeLucky

#1

Khan Academy

79% relevance

Subscribers 9.3M

Videos 9K

Views 2.2B

Appearances 23

SERP 100%

Similarity 65%

probability distribution random variables binomial distribution

Both target learners seeking probability distributions and related statistics, with high search overlap on those topics (100% for search) and moderate content alignment (65%), indicating CodeLucky shares audience interests but differs in delivery style.

The Organic Chemistry Tutor YouTube channel thumbnail

#2

The Organic Chemistry Tutor view on YouTube ▶

77% relevance

Subscribers 10.5M

Videos 3.1K

Views 1.7B

Appearances 21

SERP 88%

Similarity 69%

statistics tutorial probability distribution binomial distribution

Similar to CodeLucky in offering statistics tutorials (probability distribution, binomial distribution) that attract viewers, reflected by high search overlap (88%) and solid content similarity (69%), implying overlapping audience interests with a slightly broader topic range.

#3

numiqo

67% relevance

Subscribers 284K

Videos 221

Views 19.5M

Appearances 10

SERP 52%

Similarity 78%

statistics tutorial standard deviation variance statistics

Targets statistics topics like standard deviation and variance with strong content similarity (78%) and moderate search overlap (52%), indicating an overlapping stats-focused audience but less alignment on search queries.

StatQuest with Josh Starmer YouTube channel thumbnail

#4

StatQuest with Josh Starmer view on YouTube ▶

63% relevance

Subscribers 1.6M

Videos 293

Views 89.3M

Appearances 10

SERP 48%

Similarity 73%

probability distribution binomial distribution expected value

Shares core statistics topics (probability distribution, binomial distribution, expected value) with CodeLucky, shown by substantial content similarity (73%) and notable search alignment (48%), signaling overlapping audience and topic scope with distinct presentation style.

#5

Simplilearn view on YouTube ▶

62% relevance

Subscribers 6.1M

Videos 10K

Views 509.1M

Appearances 18

SERP 60%

Similarity 63%

cloud computing concepts sql query optimization data science fundamentals

Although broader in scope, it covers data science fundamentals and analytics concepts that intersect with CodeLucky’s statistics content, as reflected by moderate search overlap (60%) and comparable content similarity (63%), indicating overlapping but diversified audience needs.

#6

Steve Brunton

62% relevance

Subscribers 510K

Videos 573

Views 31.7M

Appearances 12

SERP 44%

Similarity 74%

probability distribution random variables binomial distribution

Covers probability distribution, random variables, and binomial distribution similarly to CodeLucky, with solid content alignment (74%) and meaningful search overlap (44%), implying shared topics but different audience capture and format.

Content Landscape

Top competitors include Khan Academy (79% match) and The Organic Chemistry Tutor (77% match), both aligning with statistics tutorials and probability concepts such as probability distributions and binomial distribution. Additional overlap with numiqo (67% match) and StatQuest with Josh Starmer (63% match) reinforces competition around probability, distributions, and expected value. Simplilearn (62% match) also overlaps on data science fundamentals and cloud topics, broadening the competition. CodeLucky has 7.4K subscribers, while the identified competitors range from 284K to 10.5M subscribers, illustrating a substantial size gap but similar content themes in statistics, probability, and data science education.

Video Highlights

Didn't Make the Cut

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#27	48%	Steven Bradburn	70%	130K	13%	2
#28	47%	Dr. Trefor Bazett	71%	585K	12%	2
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Search Queries Used

statistics tutorial probability distribution random variables binomial distribution expected value standard deviation variance statistics probability mass function continuous random variables discrete random variables mean of random variable probability rules random variables explained statistics for beginners DSA probability probability basics bayes theorem explained linear regression tutorial data structures basics system design basics cloud computing concepts linux command line sql query optimization data science fundamentals machine learning basics devops pipelines docker fundamentals kubernetes tutorials big data fundamentals web development basics api design principles information security basics interview coding problems algorithms efficiency software testing basics

Frequently Asked Questions

Which YouTube channels are most similar to CodeLucky?

CodeLucky's biggest YouTube competitors are Khan Academy (9.3M subscribers, 79% match), The Organic Chemistry Tutor (10.5M subscribers, 77% match), and numiqo (284K subscribers, 67% match). All three channels provide educational content focused on foundational math and science topics, similar to CodeLucky's probability and statistics topics.

What type of content does CodeLucky make?

CodeLucky produces educational content focused on statistics and probability, with video titles like Binomial Distribution Explained, Variance of Discrete Random Variable Explained, and What is Expected Value. The channel averages about 56 views per video, and the provided data suggests multiple recent uploads per week, though an exact upload frequency is not specified.

How do we determine which channels are similar to CodeLucky?

We analyze CodeLucky's recent videos, generate topic-relevant search queries, check YouTube search results, and compare the meaning of each channel's content to measure similarity. The result is a ranked list sorted by SERP overlap, semantic similarity, and search appearances.

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