SlideShare a Scribd company logo
1 of 16
Visualising the world of
Competitive Programming
Anuj Menta
–<random_senior>
“Learn something other than Python. It won’t fetch
you a job”
• more commonly known as algorithmic programming.
• Popular among Computer Science graduates across all
universities (:P)
• Optimizing different operations using complex data
structures and algorithms like Binary Trees, Dynamic
Programming etc
• Common websites to practice: Codechef, Hackerrank,
Codeforces, Hackerearth, SPOJ etc.
Then why Codeforces?
• Only has programming contests hosted on a weekly basis.
• The most important thing : Every participants code is
public
• A unique user rating system(A bit similar to the elo rating
system)
• Very diverse in terms of programming languages and
types of problems.
–Anuj Menta
“All the numbers depicted might have a confidence
interval of about 90%. The study does not mean to
undermine or promote any website”
Number of questions
Just to start off with a small comparison
0
1000
2000
3000
4000
Codeforces Hackerrank Codechef Hackerearth
Hackerrank and Hackerearth have
problem sets on other topics like
Artificial Intelligence, Math etc (+10
points :P)
Number of questions | Category-wise
Categories: Sorting - Dynamic Programming
0
1
Codeforces Hackerrank Codechef Hackerearth
Number of submissions
Total number of submissions made for existing
problem sets
0
1
Codeforces Hackerrank Codechef Hackerearth
And finally….
(drumroll)
Comparison of Programming Languages
A comparison on all the four websites on 10
languages
0
1
–Some C++ stud
“Competitive programming is independent of
programming language”
But still..
How far can you possibly go if you
only know Python?
Broad Categories of Questions
• Simple Implementation
• Sorting
• Search
• Trees
• Linked Lists/Heaps/Queues/Stacks/Heaps
• Graph Theory
• Greedy algorithms
• Dynamic Programming
• Recursion
• NP Complete
• Advanced Data Structures
Proportion of Python submissions
The percentage of python submissions for different
categories of questions
0
0.25
0.5
0.75
1
Implementation
Sorting
Search
Trees
Comparisons to be added:
• Performance : Python vs. others
• Computation time: Python vs. others
• Number of chars/lines of code : Python vs. others
• Programming style : High rated coders vs. Low rated
coders
• Users churn rate( classified by programming language)

More Related Content

What's hot

Introducción a NLP (Natural Language Processing) en Azure
Introducción a NLP (Natural Language Processing) en AzureIntroducción a NLP (Natural Language Processing) en Azure
Introducción a NLP (Natural Language Processing) en AzurePlain Concepts
 
Personalized Page Generation for Browsing Recommendations
Personalized Page Generation for Browsing RecommendationsPersonalized Page Generation for Browsing Recommendations
Personalized Page Generation for Browsing RecommendationsJustin Basilico
 
Machine learning 101 sit hvr
Machine learning 101 sit hvrMachine learning 101 sit hvr
Machine learning 101 sit hvrfredverheul
 
Recommendations for Building Machine Learning Software
Recommendations for Building Machine Learning SoftwareRecommendations for Building Machine Learning Software
Recommendations for Building Machine Learning SoftwareJustin Basilico
 
Machine Learning at Netflix Scale
Machine Learning at Netflix ScaleMachine Learning at Netflix Scale
Machine Learning at Netflix ScaleAish Fenton
 
Moving and receiving xAPI data in an LRS rich environment
Moving and receiving xAPI data in an LRS rich environment Moving and receiving xAPI data in an LRS rich environment
Moving and receiving xAPI data in an LRS rich environment Rustici Software
 
Model Drift Monitoring using Tensorflow Model Analysis
Model Drift Monitoring using Tensorflow Model AnalysisModel Drift Monitoring using Tensorflow Model Analysis
Model Drift Monitoring using Tensorflow Model AnalysisVivek Raja P S
 
Adding xAPI to your RFPs: Rethinking your process
Adding xAPI to your RFPs: Rethinking your processAdding xAPI to your RFPs: Rethinking your process
Adding xAPI to your RFPs: Rethinking your processRustici Software
 
Personalized Job Recommendation System at LinkedIn: Practical Challenges and ...
Personalized Job Recommendation System at LinkedIn: Practical Challenges and ...Personalized Job Recommendation System at LinkedIn: Practical Challenges and ...
Personalized Job Recommendation System at LinkedIn: Practical Challenges and ...Benjamin Le
 
Machine Learning at Quora (2/26/2016)
Machine Learning at Quora (2/26/2016)Machine Learning at Quora (2/26/2016)
Machine Learning at Quora (2/26/2016)Nikhil Dandekar
 
Preparing for IEEEXtreme 12.0 &amp; mora xtreme
Preparing for IEEEXtreme 12.0 &amp; mora xtremePreparing for IEEEXtreme 12.0 &amp; mora xtreme
Preparing for IEEEXtreme 12.0 &amp; mora xtremeSupun Abeysinghe
 
Build a Sentiment Model using ML.Net
Build a Sentiment Model using ML.NetBuild a Sentiment Model using ML.Net
Build a Sentiment Model using ML.NetCheah Eng Soon
 
TechTalk #13 Grokking: Marrying Elasticsearch with NLP to solve real-world se...
TechTalk #13 Grokking: Marrying Elasticsearch with NLP to solve real-world se...TechTalk #13 Grokking: Marrying Elasticsearch with NLP to solve real-world se...
TechTalk #13 Grokking: Marrying Elasticsearch with NLP to solve real-world se...Grokking VN
 

What's hot (20)

Introducción a NLP (Natural Language Processing) en Azure
Introducción a NLP (Natural Language Processing) en AzureIntroducción a NLP (Natural Language Processing) en Azure
Introducción a NLP (Natural Language Processing) en Azure
 
Personalized Page Generation for Browsing Recommendations
Personalized Page Generation for Browsing RecommendationsPersonalized Page Generation for Browsing Recommendations
Personalized Page Generation for Browsing Recommendations
 
Machine learning 101 sit hvr
Machine learning 101 sit hvrMachine learning 101 sit hvr
Machine learning 101 sit hvr
 
Recommendations for Building Machine Learning Software
Recommendations for Building Machine Learning SoftwareRecommendations for Building Machine Learning Software
Recommendations for Building Machine Learning Software
 
Data Wrangling Week 4
Data Wrangling Week 4Data Wrangling Week 4
Data Wrangling Week 4
 
Machine Learning at Netflix Scale
Machine Learning at Netflix ScaleMachine Learning at Netflix Scale
Machine Learning at Netflix Scale
 
Moving and receiving xAPI data in an LRS rich environment
Moving and receiving xAPI data in an LRS rich environment Moving and receiving xAPI data in an LRS rich environment
Moving and receiving xAPI data in an LRS rich environment
 
Model Drift Monitoring using Tensorflow Model Analysis
Model Drift Monitoring using Tensorflow Model AnalysisModel Drift Monitoring using Tensorflow Model Analysis
Model Drift Monitoring using Tensorflow Model Analysis
 
Adding xAPI to your RFPs: Rethinking your process
Adding xAPI to your RFPs: Rethinking your processAdding xAPI to your RFPs: Rethinking your process
Adding xAPI to your RFPs: Rethinking your process
 
Personalized Job Recommendation System at LinkedIn: Practical Challenges and ...
Personalized Job Recommendation System at LinkedIn: Practical Challenges and ...Personalized Job Recommendation System at LinkedIn: Practical Challenges and ...
Personalized Job Recommendation System at LinkedIn: Practical Challenges and ...
 
Data wrangling week2
Data wrangling week2Data wrangling week2
Data wrangling week2
 
Machine Learning at Quora (2/26/2016)
Machine Learning at Quora (2/26/2016)Machine Learning at Quora (2/26/2016)
Machine Learning at Quora (2/26/2016)
 
Preparing for IEEEXtreme 12.0 &amp; mora xtreme
Preparing for IEEEXtreme 12.0 &amp; mora xtremePreparing for IEEEXtreme 12.0 &amp; mora xtreme
Preparing for IEEEXtreme 12.0 &amp; mora xtreme
 
Data wrangling week3
Data wrangling week3Data wrangling week3
Data wrangling week3
 
Data wrangling week 6
Data wrangling week 6Data wrangling week 6
Data wrangling week 6
 
Resume new
Resume newResume new
Resume new
 
Data wrangling week 5
Data wrangling week 5Data wrangling week 5
Data wrangling week 5
 
Data Wrangling Week 7
Data Wrangling Week 7Data Wrangling Week 7
Data Wrangling Week 7
 
Build a Sentiment Model using ML.Net
Build a Sentiment Model using ML.NetBuild a Sentiment Model using ML.Net
Build a Sentiment Model using ML.Net
 
TechTalk #13 Grokking: Marrying Elasticsearch with NLP to solve real-world se...
TechTalk #13 Grokking: Marrying Elasticsearch with NLP to solve real-world se...TechTalk #13 Grokking: Marrying Elasticsearch with NLP to solve real-world se...
TechTalk #13 Grokking: Marrying Elasticsearch with NLP to solve real-world se...
 

Similar to Visualising the competitive programming world

Machine Learning 101 | Essential Tools for Machine Learning
Machine Learning 101 | Essential Tools for Machine LearningMachine Learning 101 | Essential Tools for Machine Learning
Machine Learning 101 | Essential Tools for Machine LearningHafiz Muhammad Attaullah
 
OSCON 2014: Data Workflows for Machine Learning
OSCON 2014: Data Workflows for Machine LearningOSCON 2014: Data Workflows for Machine Learning
OSCON 2014: Data Workflows for Machine LearningPaco Nathan
 
Data Workflows for Machine Learning - Seattle DAML
Data Workflows for Machine Learning - Seattle DAMLData Workflows for Machine Learning - Seattle DAML
Data Workflows for Machine Learning - Seattle DAMLPaco Nathan
 
W1-Intro to python.pptx
W1-Intro to python.pptxW1-Intro to python.pptx
W1-Intro to python.pptxNaziaPerwaiz2
 
Data Workflows for Machine Learning - SF Bay Area ML
Data Workflows for Machine Learning - SF Bay Area MLData Workflows for Machine Learning - SF Bay Area ML
Data Workflows for Machine Learning - SF Bay Area MLPaco Nathan
 
Transferring Software Testing Tools to Practice
Transferring Software Testing Tools to PracticeTransferring Software Testing Tools to Practice
Transferring Software Testing Tools to PracticeTao Xie
 
Software Analytics - Achievements and Challenges
Software Analytics - Achievements and ChallengesSoftware Analytics - Achievements and Challenges
Software Analytics - Achievements and ChallengesTao Xie
 
Lessons Learned from Building Machine Learning Software at Netflix
Lessons Learned from Building Machine Learning Software at NetflixLessons Learned from Building Machine Learning Software at Netflix
Lessons Learned from Building Machine Learning Software at NetflixJustin Basilico
 
2R-3KS03-OOP_UNIT-I (Part-A)_2023-24.pptx
2R-3KS03-OOP_UNIT-I (Part-A)_2023-24.pptx2R-3KS03-OOP_UNIT-I (Part-A)_2023-24.pptx
2R-3KS03-OOP_UNIT-I (Part-A)_2023-24.pptxGauravGamer2
 
Curriculum Vitae of Er. Sanpreet Singh (Presentation)
Curriculum Vitae of Er. Sanpreet Singh (Presentation)Curriculum Vitae of Er. Sanpreet Singh (Presentation)
Curriculum Vitae of Er. Sanpreet Singh (Presentation)Sanpreet Singh
 
Dev Concepts: The 4 Essential Developer Skills
Dev Concepts: The 4 Essential Developer SkillsDev Concepts: The 4 Essential Developer Skills
Dev Concepts: The 4 Essential Developer SkillsSvetlin Nakov
 
Finding Defects in C#: Coverity vs. FxCop
Finding Defects in C#: Coverity vs. FxCopFinding Defects in C#: Coverity vs. FxCop
Finding Defects in C#: Coverity vs. FxCopCoverity
 
Parallel and distributed computing.zhang zhiguo.2009w 1
Parallel and distributed computing.zhang zhiguo.2009w 1Parallel and distributed computing.zhang zhiguo.2009w 1
Parallel and distributed computing.zhang zhiguo.2009w 1feliugarcia
 
From SQL to Python - A Beginner's Guide to Making the Switch
From SQL to Python - A Beginner's Guide to Making the SwitchFrom SQL to Python - A Beginner's Guide to Making the Switch
From SQL to Python - A Beginner's Guide to Making the SwitchRachel Berryman
 

Similar to Visualising the competitive programming world (20)

Machine Learning 101 | Essential Tools for Machine Learning
Machine Learning 101 | Essential Tools for Machine LearningMachine Learning 101 | Essential Tools for Machine Learning
Machine Learning 101 | Essential Tools for Machine Learning
 
OSCON 2014: Data Workflows for Machine Learning
OSCON 2014: Data Workflows for Machine LearningOSCON 2014: Data Workflows for Machine Learning
OSCON 2014: Data Workflows for Machine Learning
 
Data Workflows for Machine Learning - Seattle DAML
Data Workflows for Machine Learning - Seattle DAMLData Workflows for Machine Learning - Seattle DAML
Data Workflows for Machine Learning - Seattle DAML
 
W1-Intro to python.pptx
W1-Intro to python.pptxW1-Intro to python.pptx
W1-Intro to python.pptx
 
Data Workflows for Machine Learning - SF Bay Area ML
Data Workflows for Machine Learning - SF Bay Area MLData Workflows for Machine Learning - SF Bay Area ML
Data Workflows for Machine Learning - SF Bay Area ML
 
IT_Tools_in_Research.ppt
IT_Tools_in_Research.pptIT_Tools_in_Research.ppt
IT_Tools_in_Research.ppt
 
LSESU a Taste of R Language Workshop
LSESU a Taste of R Language WorkshopLSESU a Taste of R Language Workshop
LSESU a Taste of R Language Workshop
 
Transferring Software Testing Tools to Practice
Transferring Software Testing Tools to PracticeTransferring Software Testing Tools to Practice
Transferring Software Testing Tools to Practice
 
01.intro
01.intro01.intro
01.intro
 
Software Analytics - Achievements and Challenges
Software Analytics - Achievements and ChallengesSoftware Analytics - Achievements and Challenges
Software Analytics - Achievements and Challenges
 
Unit no_1.pptx
Unit no_1.pptxUnit no_1.pptx
Unit no_1.pptx
 
Lessons Learned from Building Machine Learning Software at Netflix
Lessons Learned from Building Machine Learning Software at NetflixLessons Learned from Building Machine Learning Software at Netflix
Lessons Learned from Building Machine Learning Software at Netflix
 
Plc part 1
Plc part 1Plc part 1
Plc part 1
 
Machine Learning
Machine LearningMachine Learning
Machine Learning
 
2R-3KS03-OOP_UNIT-I (Part-A)_2023-24.pptx
2R-3KS03-OOP_UNIT-I (Part-A)_2023-24.pptx2R-3KS03-OOP_UNIT-I (Part-A)_2023-24.pptx
2R-3KS03-OOP_UNIT-I (Part-A)_2023-24.pptx
 
Curriculum Vitae of Er. Sanpreet Singh (Presentation)
Curriculum Vitae of Er. Sanpreet Singh (Presentation)Curriculum Vitae of Er. Sanpreet Singh (Presentation)
Curriculum Vitae of Er. Sanpreet Singh (Presentation)
 
Dev Concepts: The 4 Essential Developer Skills
Dev Concepts: The 4 Essential Developer SkillsDev Concepts: The 4 Essential Developer Skills
Dev Concepts: The 4 Essential Developer Skills
 
Finding Defects in C#: Coverity vs. FxCop
Finding Defects in C#: Coverity vs. FxCopFinding Defects in C#: Coverity vs. FxCop
Finding Defects in C#: Coverity vs. FxCop
 
Parallel and distributed computing.zhang zhiguo.2009w 1
Parallel and distributed computing.zhang zhiguo.2009w 1Parallel and distributed computing.zhang zhiguo.2009w 1
Parallel and distributed computing.zhang zhiguo.2009w 1
 
From SQL to Python - A Beginner's Guide to Making the Switch
From SQL to Python - A Beginner's Guide to Making the SwitchFrom SQL to Python - A Beginner's Guide to Making the Switch
From SQL to Python - A Beginner's Guide to Making the Switch
 

Recently uploaded

Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...VICTOR MAESTRE RAMIREZ
 
Call Girls Narol 7397865700 Independent Call Girls
Call Girls Narol 7397865700 Independent Call GirlsCall Girls Narol 7397865700 Independent Call Girls
Call Girls Narol 7397865700 Independent Call Girlsssuser7cb4ff
 
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escortsranjana rawat
 
Past, Present and Future of Generative AI
Past, Present and Future of Generative AIPast, Present and Future of Generative AI
Past, Present and Future of Generative AIabhishek36461
 
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube ExchangerStudy on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube ExchangerAnamika Sarkar
 
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur EscortsHigh Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escortsranjana rawat
 
Application of Residue Theorem to evaluate real integrations.pptx
Application of Residue Theorem to evaluate real integrations.pptxApplication of Residue Theorem to evaluate real integrations.pptx
Application of Residue Theorem to evaluate real integrations.pptx959SahilShah
 
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...ZTE
 
Microscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptxMicroscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptxpurnimasatapathy1234
 
HARMONY IN THE NATURE AND EXISTENCE - Unit-IV
HARMONY IN THE NATURE AND EXISTENCE - Unit-IVHARMONY IN THE NATURE AND EXISTENCE - Unit-IV
HARMONY IN THE NATURE AND EXISTENCE - Unit-IVRajaP95
 
power system scada applications and uses
power system scada applications and usespower system scada applications and uses
power system scada applications and usesDevarapalliHaritha
 
Gfe Mayur Vihar Call Girls Service WhatsApp -> 9999965857 Available 24x7 ^ De...
Gfe Mayur Vihar Call Girls Service WhatsApp -> 9999965857 Available 24x7 ^ De...Gfe Mayur Vihar Call Girls Service WhatsApp -> 9999965857 Available 24x7 ^ De...
Gfe Mayur Vihar Call Girls Service WhatsApp -> 9999965857 Available 24x7 ^ De...srsj9000
 
(ANVI) Koregaon Park Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(ANVI) Koregaon Park Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...(ANVI) Koregaon Park Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(ANVI) Koregaon Park Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...ranjana rawat
 
VIP Call Girls Service Hitech City Hyderabad Call +91-8250192130
VIP Call Girls Service Hitech City Hyderabad Call +91-8250192130VIP Call Girls Service Hitech City Hyderabad Call +91-8250192130
VIP Call Girls Service Hitech City Hyderabad Call +91-8250192130Suhani Kapoor
 
HARMONY IN THE HUMAN BEING - Unit-II UHV-2
HARMONY IN THE HUMAN BEING - Unit-II UHV-2HARMONY IN THE HUMAN BEING - Unit-II UHV-2
HARMONY IN THE HUMAN BEING - Unit-II UHV-2RajaP95
 
SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )Tsuyoshi Horigome
 
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdfCCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdfAsst.prof M.Gokilavani
 
Model Call Girl in Narela Delhi reach out to us at 🔝8264348440🔝
Model Call Girl in Narela Delhi reach out to us at 🔝8264348440🔝Model Call Girl in Narela Delhi reach out to us at 🔝8264348440🔝
Model Call Girl in Narela Delhi reach out to us at 🔝8264348440🔝soniya singh
 
Sachpazis Costas: Geotechnical Engineering: A student's Perspective Introduction
Sachpazis Costas: Geotechnical Engineering: A student's Perspective IntroductionSachpazis Costas: Geotechnical Engineering: A student's Perspective Introduction
Sachpazis Costas: Geotechnical Engineering: A student's Perspective IntroductionDr.Costas Sachpazis
 
What are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptxWhat are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptxwendy cai
 

Recently uploaded (20)

Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...
 
Call Girls Narol 7397865700 Independent Call Girls
Call Girls Narol 7397865700 Independent Call GirlsCall Girls Narol 7397865700 Independent Call Girls
Call Girls Narol 7397865700 Independent Call Girls
 
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts
(MEERA) Dapodi Call Girls Just Call 7001035870 [ Cash on Delivery ] Pune Escorts
 
Past, Present and Future of Generative AI
Past, Present and Future of Generative AIPast, Present and Future of Generative AI
Past, Present and Future of Generative AI
 
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube ExchangerStudy on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
 
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur EscortsHigh Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
High Profile Call Girls Nagpur Isha Call 7001035870 Meet With Nagpur Escorts
 
Application of Residue Theorem to evaluate real integrations.pptx
Application of Residue Theorem to evaluate real integrations.pptxApplication of Residue Theorem to evaluate real integrations.pptx
Application of Residue Theorem to evaluate real integrations.pptx
 
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...
ZXCTN 5804 / ZTE PTN / ZTE POTN / ZTE 5804 PTN / ZTE POTN 5804 ( 100/200 GE Z...
 
Microscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptxMicroscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptx
 
HARMONY IN THE NATURE AND EXISTENCE - Unit-IV
HARMONY IN THE NATURE AND EXISTENCE - Unit-IVHARMONY IN THE NATURE AND EXISTENCE - Unit-IV
HARMONY IN THE NATURE AND EXISTENCE - Unit-IV
 
power system scada applications and uses
power system scada applications and usespower system scada applications and uses
power system scada applications and uses
 
Gfe Mayur Vihar Call Girls Service WhatsApp -> 9999965857 Available 24x7 ^ De...
Gfe Mayur Vihar Call Girls Service WhatsApp -> 9999965857 Available 24x7 ^ De...Gfe Mayur Vihar Call Girls Service WhatsApp -> 9999965857 Available 24x7 ^ De...
Gfe Mayur Vihar Call Girls Service WhatsApp -> 9999965857 Available 24x7 ^ De...
 
(ANVI) Koregaon Park Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(ANVI) Koregaon Park Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...(ANVI) Koregaon Park Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
(ANVI) Koregaon Park Call Girls Just Call 7001035870 [ Cash on Delivery ] Pun...
 
VIP Call Girls Service Hitech City Hyderabad Call +91-8250192130
VIP Call Girls Service Hitech City Hyderabad Call +91-8250192130VIP Call Girls Service Hitech City Hyderabad Call +91-8250192130
VIP Call Girls Service Hitech City Hyderabad Call +91-8250192130
 
HARMONY IN THE HUMAN BEING - Unit-II UHV-2
HARMONY IN THE HUMAN BEING - Unit-II UHV-2HARMONY IN THE HUMAN BEING - Unit-II UHV-2
HARMONY IN THE HUMAN BEING - Unit-II UHV-2
 
SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )
 
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdfCCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
CCS355 Neural Network & Deep Learning UNIT III notes and Question bank .pdf
 
Model Call Girl in Narela Delhi reach out to us at 🔝8264348440🔝
Model Call Girl in Narela Delhi reach out to us at 🔝8264348440🔝Model Call Girl in Narela Delhi reach out to us at 🔝8264348440🔝
Model Call Girl in Narela Delhi reach out to us at 🔝8264348440🔝
 
Sachpazis Costas: Geotechnical Engineering: A student's Perspective Introduction
Sachpazis Costas: Geotechnical Engineering: A student's Perspective IntroductionSachpazis Costas: Geotechnical Engineering: A student's Perspective Introduction
Sachpazis Costas: Geotechnical Engineering: A student's Perspective Introduction
 
What are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptxWhat are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptx
 

Visualising the competitive programming world

  • 1. Visualising the world of Competitive Programming Anuj Menta
  • 2. –<random_senior> “Learn something other than Python. It won’t fetch you a job”
  • 3. • more commonly known as algorithmic programming. • Popular among Computer Science graduates across all universities (:P) • Optimizing different operations using complex data structures and algorithms like Binary Trees, Dynamic Programming etc • Common websites to practice: Codechef, Hackerrank, Codeforces, Hackerearth, SPOJ etc.
  • 4. Then why Codeforces? • Only has programming contests hosted on a weekly basis. • The most important thing : Every participants code is public • A unique user rating system(A bit similar to the elo rating system) • Very diverse in terms of programming languages and types of problems.
  • 5. –Anuj Menta “All the numbers depicted might have a confidence interval of about 90%. The study does not mean to undermine or promote any website”
  • 6. Number of questions Just to start off with a small comparison 0 1000 2000 3000 4000 Codeforces Hackerrank Codechef Hackerearth
  • 7. Hackerrank and Hackerearth have problem sets on other topics like Artificial Intelligence, Math etc (+10 points :P)
  • 8. Number of questions | Category-wise Categories: Sorting - Dynamic Programming 0 1 Codeforces Hackerrank Codechef Hackerearth
  • 9. Number of submissions Total number of submissions made for existing problem sets 0 1 Codeforces Hackerrank Codechef Hackerearth
  • 11. Comparison of Programming Languages A comparison on all the four websites on 10 languages 0 1
  • 12. –Some C++ stud “Competitive programming is independent of programming language”
  • 13. But still.. How far can you possibly go if you only know Python?
  • 14. Broad Categories of Questions • Simple Implementation • Sorting • Search • Trees • Linked Lists/Heaps/Queues/Stacks/Heaps • Graph Theory • Greedy algorithms • Dynamic Programming • Recursion • NP Complete • Advanced Data Structures
  • 15. Proportion of Python submissions The percentage of python submissions for different categories of questions 0 0.25 0.5 0.75 1 Implementation Sorting Search Trees
  • 16. Comparisons to be added: • Performance : Python vs. others • Computation time: Python vs. others • Number of chars/lines of code : Python vs. others • Programming style : High rated coders vs. Low rated coders • Users churn rate( classified by programming language)