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Multithreads-NITK-HackVerse
January 25, 2020 – January 26, 2020
AADS (Automated animal detection System and Prevention) created during Hackathon in NITK by Team MULTITHREADS
View ProjectMachine-Learning
January 13, 2020 – June 28, 2020
Learning and implementation of machine learning algorithms
View Projecttask2_computernetworks
October 14, 2019 – October 14, 2019
Practice Socket Programming (Multithreaded TCP and UDP servers, polling of sockets, blocking and nonblocking read write sendto recvfrom ) and NS-3 (CSMA lans, P2P lans, Installing TCP and UDP applications on various nodes and evaluate your network).
View Projecttask2_computernetworks
October 14, 2019 – October 14, 2019
Practice Socket Programming (Multithreaded TCP and UDP servers, polling of sockets, blocking and nonblocking read write sendto recvfrom ) and NS-3 (CSMA lans, P2P lans, Installing TCP and UDP applications on various nodes and evaluate your network).
View Projectmultithread-tcp-server
October 5, 2019 – December 12, 2019
A multithreaded tcp server which services the requests of the client parallely using threads in c++ and maintain a separate tcp connection with each client using sockets.
View Projectserver-client-messagequeue
September 15, 2019 – November 8, 2019
Server which handles multiple clients and performs requests according to priority using message queues
View Projectprograms
August 27, 2018 – November 2, 2018
C++ programs for different questions and algorithms
View ProjectCultural Fit Analysis
The candidate's projects are primarily personal and academic, focusing on C++ network programming and some machine learning. While there's a project related to 'Automated animal detection System and Prevention' from a hackathon, the overall project diversity is moderate. The target role is 'Data Scientist', and while there's a 'Machine-Learning' project, the majority of projects lean towards systems/network programming. This suggests a potential misalignment with the core responsibilities of a Data Scientist role, which typically requires stronger emphasis on statistical modeling, data analysis, and specific ML frameworks. The breadth of skills is limited to C++, Python, Shell, and Jupyter Notebook, with no explicit mention of data science specific libraries (e.g., Pandas, NumPy, Scikit-learn beyond general ML) or tools (e.g., SQL, cloud platforms).
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's project descriptions are brief, and there are no psychometric or English test results available.