SlideShare ist ein Scribd-Unternehmen logo
1 von 18
Email: mnnitpawan@gmail.com
Contact: 09651323357
Currently: Data Analyst (R Programmer)
Previously: Assistant
Professor(Mathematics)
Latest Degree: M.S Mathematics & Scientific
Computing
From:
Motilal Nehru National Institute Of Technology Allahabad
Bachelor of Science from Allahabad University major in Mathematics and
Physics.
Intermediate from Kendriya Vidyalaya A.F.S Memaura Lucknow in Science.
High School from Kendriya Vidyalaya A.F.S Memaura Lucknow .
• Present:-
Data Mining
Engineer at
Bloomingfeld
Ltd
• Assistant
Professor
Mathematics
at
LDC ITS
Engineering
• Lecturer
Mathematics
at
Lovely
Professional
University
Web Analytic Tools-
Ongoing
In this project I was involved for the analysis of web log files and its
graphical representations.
For analysis I used R programming and its packages. Mainly I focused
on the graphical representation, data manipulation and data cleaning of
log files.
Main packages I used for the analysis and representation of Log files
are:
1: Lattice
2: Ggplot
3: GoogleVis
4: R markdown (R Studio)
5: Polyr
6: RMySQl, RSQLite and RODBC
7: Shiny (R Studio)
Task Management
System
 In this project I implemented the algorithm
for the determination of efficiency and
workload of unique user, project leader and
training manager.
 I have created the graphical algorithm to
represent the data on website for ROR
users.
 I called the database in R by using R
packages and did the basic analysis for the
presentation on web pages.
During the job in Bloomingfeld Ltd I have learnt a lot to
explore me and my interest in Data Analysis. Here, I am
giving the glimpse what I have explored and learnt.
 R programming:- I have been introduced with this
programming language which has given me the strength to
deploy my mathematical and statistical skills into the practice.
In R, I have learnt so many packages and methods that are
widely useful for the analysis, generating reports and
graphical representation on website like
shiny, googleVis, Gt2k, lavaan, SEM and RMySQL etc…
 Big Data Insight: This is the major area wherein I am very
much interested to involve. I did survey over this area and its
practices. I have found myself very much indulge with
hadoop, hive, Pig and of course Map Reduce methods. I am
looking more over this field and deployment of these tools
with R.
 Strong logical, assessment and interpretation skills
 Good experience in deployment of statistical technique.
 Good ability to simulate mathematical model with modern
technique.
 Eager to learn new statistical and mathematical software and
methods.
Role and Description at LDC ITS Engineering
College and Lovely Professional University
Role: Assistant Professor at LDC Engineering College.
Lecturer at Lovely Professional University.
Subject Taught:
 Engineering Mathematics: Differential Calculus, Integral
Calculus, Vector Calculus, Multiple Integral , Fourier
transformation, Laplace transformation and Z transformation.
 Operation Research.
 Complex Analysis.
 Linear Algebra.
 ODE’s and PDE’s.
 Numerical Mathematics.
 Probability and Statistics.
Task Handled
 Academic Planner of Mathematics course of Biotechnology
Department.
 In charge of the Document and ID card section in CAD
(Centre of Admission).
 Given workshops on Matlab and Singular software to the
faculties and students.
Recognized Works:
 Involved in the Robotics clubs.
 Founder of Student Activity Organization.
 Given talks on mathematical and statistical software.
 Crystal clear understanding of the underlying principles of the
subject and its relevancy to other domains
 In-depth knowledge of various techniques and approaches applied
at a research
 Perfect knowledge about the common job duties of a lecturer and
ability to perform them efficiently
 A little familiarity with the general administrative environment at
educational institutes and idea about their practices
Programming Language:
EXPERT IN
Good command over integrated knowledge of
MySQL and R
BASIC IN
Software Skills:
EXPERT IN
BASIC IN
INTERMEDIATE IN
Operating System:
Kr Pawan
Kr Pawan

Weitere ähnliche Inhalte

Was ist angesagt?

Li Cheng WUSTL resume(Amazon)
Li Cheng WUSTL resume(Amazon)Li Cheng WUSTL resume(Amazon)
Li Cheng WUSTL resume(Amazon)
Li Cheng
 
Software Programs for Data Analysis
Software Programs for Data AnalysisSoftware Programs for Data Analysis
Software Programs for Data Analysis
unmgrc
 
Using Microsoft Excel
Using Microsoft ExcelUsing Microsoft Excel
Using Microsoft Excel
cxevans
 

Was ist angesagt? (20)

Resume_xuezhi
Resume_xuezhiResume_xuezhi
Resume_xuezhi
 
resume
resumeresume
resume
 
function (mal120) By Wakil Kumar
function (mal120) By Wakil Kumarfunction (mal120) By Wakil Kumar
function (mal120) By Wakil Kumar
 
Resume
ResumeResume
Resume
 
Contextual Definition Generation
Contextual Definition GenerationContextual Definition Generation
Contextual Definition Generation
 
SE-IT DSA LAB SYLLABUS
SE-IT DSA LAB SYLLABUSSE-IT DSA LAB SYLLABUS
SE-IT DSA LAB SYLLABUS
 
Li Cheng WUSTL resume(Amazon)
Li Cheng WUSTL resume(Amazon)Li Cheng WUSTL resume(Amazon)
Li Cheng WUSTL resume(Amazon)
 
On e-Assessment
On e-AssessmentOn e-Assessment
On e-Assessment
 
Spreadsheets Chapter 8
Spreadsheets Chapter 8Spreadsheets Chapter 8
Spreadsheets Chapter 8
 
Data Structures
Data Structures Data Structures
Data Structures
 
Analysis results-of-multiple-choice-tests
Analysis results-of-multiple-choice-testsAnalysis results-of-multiple-choice-tests
Analysis results-of-multiple-choice-tests
 
Educational satistics
Educational satisticsEducational satistics
Educational satistics
 
real life application in numerical method
real life application in numerical methodreal life application in numerical method
real life application in numerical method
 
Software Programs for Data Analysis
Software Programs for Data AnalysisSoftware Programs for Data Analysis
Software Programs for Data Analysis
 
Analysis computerscience disciplines
Analysis computerscience disciplinesAnalysis computerscience disciplines
Analysis computerscience disciplines
 
Bibliometrics in Practice - evaluating REF
Bibliometrics in Practice - evaluating REFBibliometrics in Practice - evaluating REF
Bibliometrics in Practice - evaluating REF
 
8. Graph - Data Structures using C++ by Varsha Patil
8. Graph - Data Structures using C++ by Varsha Patil8. Graph - Data Structures using C++ by Varsha Patil
8. Graph - Data Structures using C++ by Varsha Patil
 
application of numerical method
application of numerical methodapplication of numerical method
application of numerical method
 
7. Tree - Data Structures using C++ by Varsha Patil
7. Tree - Data Structures using C++ by Varsha Patil7. Tree - Data Structures using C++ by Varsha Patil
7. Tree - Data Structures using C++ by Varsha Patil
 
Using Microsoft Excel
Using Microsoft ExcelUsing Microsoft Excel
Using Microsoft Excel
 

Ähnlich wie Kr Pawan

ChenXin_Daniel_Han
ChenXin_Daniel_HanChenXin_Daniel_Han
ChenXin_Daniel_Han
Daniel Han
 
An interdisciplinary course_in_digital_image_processing
An interdisciplinary course_in_digital_image_processingAn interdisciplinary course_in_digital_image_processing
An interdisciplinary course_in_digital_image_processing
Syed Muhammad Hammad
 
Santosh Sahu_MTech_CSE
Santosh Sahu_MTech_CSESantosh Sahu_MTech_CSE
Santosh Sahu_MTech_CSE
Santosh Sahu
 
R18B.Tech.CSE(DataScience)IIIIVYearTentativeSyllabus.pdf
R18B.Tech.CSE(DataScience)IIIIVYearTentativeSyllabus.pdfR18B.Tech.CSE(DataScience)IIIIVYearTentativeSyllabus.pdf
R18B.Tech.CSE(DataScience)IIIIVYearTentativeSyllabus.pdf
Naveen Kumar
 

Ähnlich wie Kr Pawan (20)

Btsdsb2018
Btsdsb2018Btsdsb2018
Btsdsb2018
 
ChenXin_Daniel_Han
ChenXin_Daniel_HanChenXin_Daniel_Han
ChenXin_Daniel_Han
 
Lecture 1.pptx
Lecture 1.pptxLecture 1.pptx
Lecture 1.pptx
 
co-po-example of bloomy taxonomy to grade your teaching methods
co-po-example of bloomy taxonomy to grade your teaching methodsco-po-example of bloomy taxonomy to grade your teaching methods
co-po-example of bloomy taxonomy to grade your teaching methods
 
An interdisciplinary course_in_digital_image_processing
An interdisciplinary course_in_digital_image_processingAn interdisciplinary course_in_digital_image_processing
An interdisciplinary course_in_digital_image_processing
 
Resume
ResumeResume
Resume
 
313 IDS _Course_Introduction_PPT.pptx
313 IDS _Course_Introduction_PPT.pptx313 IDS _Course_Introduction_PPT.pptx
313 IDS _Course_Introduction_PPT.pptx
 
Santosh Sahu_MTech_CSE
Santosh Sahu_MTech_CSESantosh Sahu_MTech_CSE
Santosh Sahu_MTech_CSE
 
DataScience_RoadMap_2023.pdf
DataScience_RoadMap_2023.pdfDataScience_RoadMap_2023.pdf
DataScience_RoadMap_2023.pdf
 
fINAL Lesson_1_Course_Introduction_v1.pptx
fINAL Lesson_1_Course_Introduction_v1.pptxfINAL Lesson_1_Course_Introduction_v1.pptx
fINAL Lesson_1_Course_Introduction_v1.pptx
 
R18B.Tech.CSE(DataScience)IIIIVYearTentativeSyllabus.pdf
R18B.Tech.CSE(DataScience)IIIIVYearTentativeSyllabus.pdfR18B.Tech.CSE(DataScience)IIIIVYearTentativeSyllabus.pdf
R18B.Tech.CSE(DataScience)IIIIVYearTentativeSyllabus.pdf
 
Oral Defense presentation
Oral Defense presentationOral Defense presentation
Oral Defense presentation
 
What is the difference between Data Science and Data Analytics.pdf
What is the difference between Data Science and Data Analytics.pdfWhat is the difference between Data Science and Data Analytics.pdf
What is the difference between Data Science and Data Analytics.pdf
 
Lecture_01.1.pptx
Lecture_01.1.pptxLecture_01.1.pptx
Lecture_01.1.pptx
 
C++chapter2671
C++chapter2671C++chapter2671
C++chapter2671
 
CS251 Intro. to SE [Lec. 0 - Course Introduction & Plan] Spring 2022.pdf
CS251 Intro. to SE [Lec. 0 - Course Introduction & Plan] Spring 2022.pdfCS251 Intro. to SE [Lec. 0 - Course Introduction & Plan] Spring 2022.pdf
CS251 Intro. to SE [Lec. 0 - Course Introduction & Plan] Spring 2022.pdf
 
Ontology-Based Data Access Mapping Generation using Data, Schema, Query, and ...
Ontology-Based Data Access Mapping Generation using Data, Schema, Query, and ...Ontology-Based Data Access Mapping Generation using Data, Schema, Query, and ...
Ontology-Based Data Access Mapping Generation using Data, Schema, Query, and ...
 
DILEEP DATA SCIERNCES PROJECT POWERPOINT PPT
DILEEP DATA SCIERNCES PROJECT POWERPOINT PPTDILEEP DATA SCIERNCES PROJECT POWERPOINT PPT
DILEEP DATA SCIERNCES PROJECT POWERPOINT PPT
 
keerthana gl resume.docx
keerthana gl resume.docxkeerthana gl resume.docx
keerthana gl resume.docx
 
Designing Object Oriented Software - lecture slides 2013
Designing Object Oriented Software - lecture slides 2013Designing Object Oriented Software - lecture slides 2013
Designing Object Oriented Software - lecture slides 2013
 

Kürzlich hochgeladen

CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
giselly40
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
Earley Information Science
 

Kürzlich hochgeladen (20)

From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men08448380779 Call Girls In Civil Lines Women Seeking Men
08448380779 Call Girls In Civil Lines Women Seeking Men
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
Raspberry Pi 5: Challenges and Solutions in Bringing up an OpenGL/Vulkan Driv...
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...Workshop - Best of Both Worlds_ Combine  KG and Vector search for  enhanced R...
Workshop - Best of Both Worlds_ Combine KG and Vector search for enhanced R...
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
GenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day PresentationGenCyber Cyber Security Day Presentation
GenCyber Cyber Security Day Presentation
 
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of ServiceCNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
 
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptxEIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
EIS-Webinar-Prompt-Knowledge-Eng-2024-04-08.pptx
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organizationScaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 

Kr Pawan

  • 2. Currently: Data Analyst (R Programmer) Previously: Assistant Professor(Mathematics)
  • 3. Latest Degree: M.S Mathematics & Scientific Computing From: Motilal Nehru National Institute Of Technology Allahabad Bachelor of Science from Allahabad University major in Mathematics and Physics. Intermediate from Kendriya Vidyalaya A.F.S Memaura Lucknow in Science. High School from Kendriya Vidyalaya A.F.S Memaura Lucknow .
  • 4. • Present:- Data Mining Engineer at Bloomingfeld Ltd • Assistant Professor Mathematics at LDC ITS Engineering • Lecturer Mathematics at Lovely Professional University
  • 5.
  • 6. Web Analytic Tools- Ongoing In this project I was involved for the analysis of web log files and its graphical representations. For analysis I used R programming and its packages. Mainly I focused on the graphical representation, data manipulation and data cleaning of log files. Main packages I used for the analysis and representation of Log files are: 1: Lattice 2: Ggplot 3: GoogleVis 4: R markdown (R Studio) 5: Polyr 6: RMySQl, RSQLite and RODBC 7: Shiny (R Studio)
  • 7.
  • 8. Task Management System  In this project I implemented the algorithm for the determination of efficiency and workload of unique user, project leader and training manager.  I have created the graphical algorithm to represent the data on website for ROR users.  I called the database in R by using R packages and did the basic analysis for the presentation on web pages.
  • 9. During the job in Bloomingfeld Ltd I have learnt a lot to explore me and my interest in Data Analysis. Here, I am giving the glimpse what I have explored and learnt.  R programming:- I have been introduced with this programming language which has given me the strength to deploy my mathematical and statistical skills into the practice. In R, I have learnt so many packages and methods that are widely useful for the analysis, generating reports and graphical representation on website like shiny, googleVis, Gt2k, lavaan, SEM and RMySQL etc…  Big Data Insight: This is the major area wherein I am very much interested to involve. I did survey over this area and its practices. I have found myself very much indulge with hadoop, hive, Pig and of course Map Reduce methods. I am looking more over this field and deployment of these tools with R.
  • 10.  Strong logical, assessment and interpretation skills  Good experience in deployment of statistical technique.  Good ability to simulate mathematical model with modern technique.  Eager to learn new statistical and mathematical software and methods.
  • 11. Role and Description at LDC ITS Engineering College and Lovely Professional University Role: Assistant Professor at LDC Engineering College. Lecturer at Lovely Professional University. Subject Taught:  Engineering Mathematics: Differential Calculus, Integral Calculus, Vector Calculus, Multiple Integral , Fourier transformation, Laplace transformation and Z transformation.  Operation Research.  Complex Analysis.  Linear Algebra.  ODE’s and PDE’s.  Numerical Mathematics.  Probability and Statistics.
  • 12. Task Handled  Academic Planner of Mathematics course of Biotechnology Department.  In charge of the Document and ID card section in CAD (Centre of Admission).  Given workshops on Matlab and Singular software to the faculties and students. Recognized Works:  Involved in the Robotics clubs.  Founder of Student Activity Organization.  Given talks on mathematical and statistical software.
  • 13.  Crystal clear understanding of the underlying principles of the subject and its relevancy to other domains  In-depth knowledge of various techniques and approaches applied at a research  Perfect knowledge about the common job duties of a lecturer and ability to perform them efficiently  A little familiarity with the general administrative environment at educational institutes and idea about their practices
  • 14. Programming Language: EXPERT IN Good command over integrated knowledge of MySQL and R BASIC IN
  • 15. Software Skills: EXPERT IN BASIC IN INTERMEDIATE IN