About

Arkajyoti Bhattacharjee is an incoming Statistics Ph.D. student at Ohio State University, with a strong mathematical and computational background. He completed his Master's in Statistics from the Indian Institute of Technology, Kanpur, where he was inspired by various statistical learning algorithms and their applications. He is currently self-learning data science and aims to make a career out of it. Arkajyoti's technical skills include data science, normalization, and Python. He has 1.68 years of relevant experience, having worked as a Data Science Analyst at Accenture and as a Subject-matter Expert at Chegg. At Accenture, he developed an AutoML-based tool with a GUI that allows users to use multiple data pre-processing methods, select among classical and state-of-the-art algorithms, and use model optimization based on statistical or business-smart custom metrics. At Chegg, he answered questions on Statistics and Probability, thereby helping students from all around the world in the subject. Arkajyoti is currently an Open Source Developer at Google Summer of Code, where he is working on improving the nls() function of The R Project for Statistical Computing. He has developed an R package 'nlsCompare' that compares the accuracy of characteristics derived using existing or new R functions for nonlinear least-squares. He has also written tests for 'nlsj', an interim R package developed in an attempt to improve the functionalities in nls(), to ensure better code structure and robust code. Arkajyoti's education history includes a Bachelor of Science (Honours) in Statistics from Presidency University, Kolkata, and two Master of Science degrees in Statistics from Presidency University and the Indian Institute of Technology, Kanpur. He also attended St. Lawrence High School. Arkajyoti is passionate about statistics and data science and believes that being a statistician allows one to play in everybody else's backyard. He loves to find out what story lies hidden in data and hopes to make a meaningful contribution to the field.