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Nityam C - Numpy developerLD Talent logo

Nityam C

The star rating is calculated by averaging the reviews provided by all clients the talent has worked with for a significant amount of time.

If such data is not available, the star rating is calculated from the fraction of work sessions approved by their past clients versus the total tracked by the talent.

If such data is not available, the star rating defaults to the interview rating of the talent at the time of the vetting process.

Other data including reviews from interviews are available on the talents’ profiles but do not influence their star rating, though they do influence their position in the search results.
Software Engineer/Data Scientist Preferred Title
$20.00 /hr $ 30.0K /yr Hourly Rate and Yearly Salary
Overview Vetting Experiences Qualifications
Overview
Basic Summary
Vetting Summary
  • Code Quality 60%
  • Soft Skill Attributes 60%
  • Expertise 2/3
  • Coding Challenges 60%
  • No. of Lifelong Learning Projects 2
  • No. of Coding Challenges Completed 2
  • More details
Interview Scores (3.3/5)
  • Software Engineering Process 2.0/5
  • Technical Breadth 4.0/5
  • Algorithmic Thinking 3.0/5
  • Technical Strength 3.0/5
  • Teamwork 3.0/5
  • Intellectual Merit 3.0/5
  • English Communication 5.0/5
Talent's Devices
  • Desktop Windows
  • Phone Android
General
  • Member Since May 29, 2022
  • Profile Last Updated Sep 20, 2022
  • Last Activity Sept. 20, 2022, 5:32 p.m. UTC
  • Location India
  • Current Status Student
Profile Summary
I am self-motivated to challenge the limits, with a constant drive to explore new ideas and create new solutions. Computer Science to me is an unending endeavor for opportunities and a means to deliver utilitarian projects. Over the past three years, under the guidance of my faculty, I have enhanced my skills in various programming languages and domains. Being an active communicator and team member, I am open to listening and learning from various stakeholders.
Skills
Total Experience: 2+ years
  • Numpy (2E, 1Y)

    2 experiences, 1 year
  • Jupyter (2E, 1Y)

    2 experiences, 1 year
  • Data Analytics (2E, 1Y)

    2 experiences, 1 year
  • Pandas (1E, 1Y)

    1 experience, 1 year
  • Data visualization (1E, 1Y)

    1 experience, 1 year
  • Apache Spark (1E, 1Y)

    1 experience, 1 year
  • Apache Hadoop (1E, 1Y)

    1 experience, 1 year
  • Natural language processing (1E, < 1Y)

    1 experience, less than a year
  • Deep learning (1E, < 1Y)

    1 experience, less than a year
  • Machine learning (1E, < 1Y)

    1 experience, less than a year
  • scikit-learn (1E, < 1Y)

    1 experience, less than a year
Weekly Availability
Timezone Overlap Per Week: 4h PST, 20h UTC
Day
Sunday
Friday
Saturday
UTC
04 - 13
11 - 17
04 - 13
PST
21 - 06
04 - 10
21 - 06
Vetting
  • Interview Data
  • Agile Development Process
  • Productivity and Responsiveness
  • Teamwork
  • Software Engineering
  • Logical Thinking
  • Technical Strength
  • Intellectual Merit
  • English Communication
  • Documentation
  • System Design
  • Coding Challenges
    60%
  • Algorithms Score
    60%
  • General Score
    60%
  • Easy Algorithm
  • Correctness
    60%
  • Performance
    60%
  • Medium Algorithm
  • Correctness
    60%
  • Performance
    60%
  • Hard Algorithm
  • Correctness
    60%
  • Performance
    60%
  • Expertise
    2/3
  • Design Patterns and Architectures
    2/3
  • Debugging
    2/3
  • Stack Traces
    2/3
  • Testing
    2/3
  • System Administration
    2/3
  • Soft Skill Attributes
    60%
  • Entrepreneurial
    60%
  • Whole Brained
    60%
  • Divergent Thinking / Creativity
    60%
  • Design Ability
    60%
  • Empathy
    60%
  • Project Management Ability
    60%
  • Security
    60%
  • Code Quality
    60%
  • Complex Logic
    60%
  • Models
    60%
  • Controllers
    60%
  • Templates
    60%
  • APIs
    60%
  • Training/Testing Data Models
    60%
  • Code Readability
    60%
  • Ongoing Evaluation
  • Number of Lifelong Learning Project
    2
  • Number of Coding Challenge Completed
    3
  • Lifelong Learning Project 1
  • Lifelong Learning Project 1
  • Coding Challenges 1
  • Coding Challenges 2
  • Coding Challenges 3
Experience
Student
Passion Project
Aug 2021 - Sep 2022
PES University Company
Technology Industry
Project: Covid Data Analysis
  • Data Analytics
  • Numpy
  • Pandas
  • Jupyter
  • Data visualization
  • This project involved the analysis of the Covid data obtained from the covid19.org website in India. This project was implemented on a Jupyter notebook.
  • Extensive use of Data Analytics and Data Visualization skills were involved to drive this project to completion. Libraries like Pandas, Numpy, etc were also used.
Github Links
  • https://github.com/nityam007/Covid-Data-Analysis
Student
Internship
Aug 2020 - Mar 2021
Centre For Cloud Computing and Big Data Company
Technology Industry
Project: Spark Streaming
  • Apache Spark
  • This project involved using a spam dataset to classify mail/text as spam or not spam. The classifiers used were SGD, Naive Bayes, Gaussian Naive Bayes, and Passive Aggressive Classifier.
  • This project involved taking data on the fly using Apache Spark, streaming, and analyzing the same using Machine Learning models.
Github Links
  • https://github.com/nityam007/Machine-Learning-with-Spark-Streaming-BD_131_251_307_403
Student
Internship
Aug 2020 - Mar 2021
Centre For Cloud Computing and Big Data Company
Technology Industry
Project: Page Rank
  • Apache Hadoop
  • This project involved ranking pages (each page represented a number and each number had a link to another). Apache Hadoop was used to perform the page rank operation on a large scale of data.
  • It contained 2 mappers and reducers to perform the tasks. An iterative approach was used to mark the end of the operation and come up with the final page ranks.
Github Links
  • https://github.com/nityam007/Big-Data-Assignments/tree/main/A2
Student
Course Project
Apr 2020 - Jun 2020
PES University Company
Technology Industry
Project: Stock Price Prediction with Sentiment Analysis
  • Data Analytics
  • Deep learning
  • Numpy
  • Natural language processing
  • Jupyter
  • scikit-learn
  • Machine learning
  • This project involved the use of Data Analytics, Machine Learning, and Deep Learning models. I managed to sync the sentiment of the stock (using Natural Language Processing) along with stock prices.
  • All of these functionalities were implemented on a Jupyter notebook. It also involved the use of libraries like Pandas, Numpy, scikit-learn, etc.
Github Links
  • https://github.com/nityam007/Stock-Price-Prediction-with-Sentiment-Analysis
Qualifications
Education
PES University
Aug 2019 - Aug 2023
B. Tech (Computer Science)
Courses
Big Data
Aug 2021 - Dec 2022
PES University
  • Apache Hadoop
  • Apache Spark
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