The Next Step in My Data Science Journey: Mentorship at DDSA
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Greetings to all,
A Call for Pioneering Research: “Harnessing the Power of Data: Innovative Data-Driven Models for Energy and Water Management” in Springer’s Discover Data Journal
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Dear colleagues and friends,
Nomination for ‘R Project Sprint 2023’: A Celebration of Collaboration and Innovation
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Details of my nomination for R Project Sprint 2023
Nomination
I am excited to share that I have been nominated to participate in the upcoming R Project Sprint 2023! This annual event unites R enthusiasts, developers, and experts from around the globe to collaborate on projects that aim to enhance and expand the R ecosystem. The R Project Sprint is a true testament to the power of community and collaboration, and I am honored to be part of this incredible gathering.
Call for Papers in Special Issue “Data Driven Approaches for Environmental Sustainability 2023” at Energies Journal
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Dear Colleagues,
Google Scholar profile reached 1000 citations!
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Thank you, Google for the delightful email.
VedicDateTime - An R Package to Implement Vedic Calendar System
in Viggnette
This post is to demonstrate an R Package for implementing Vedic Calendar System
The VedicDateTime package provides a platform for the Vedic calendar system having several functionalities to facilitate conversion between Gregorian and Vedic calendar systems, and is helpful in examining its impact in the time series analysis domain. The background is described in Neeraj Dhanraj Bokde et al. (2021), Karanam L. Ramakumar et al. (2011), [K. S. Charak et al. (2012], (https://www.amazon.in/Elements-Vedic-Astrology-K-S-Charak/dp/8190100807), Satish BD et al. (2013).
cleanTS - a tool funded by Google Summer of Code (GSoC) - 2021 is now published in Neurocomputing (Elsevier) journal
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This post is an update on my recent publication on cleanTS package
I am glad to share that our R package cleanTS is now accepted for publication in Neurocomputing (Elsevier) journal. This is a recent package joining the league of Automated Machine Learning (AutoML) tools contributed by me and my teams. While working on huge and voluminous time-series datasets, I felt that there is a need to automate the processes of data cleaning, and this led to the concept of cleanTS.
Google Summer of Code (GSoC) - 2022
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Contributors are invited to Google Summer of Code (GSoC) - 2022!!!
Update on 21-05-2022
I am glad to share that all three proposed project in Google Summer of Code (GSoC) - 2022 with ‘R Project for Statistical Computing’ organization are accepted. I must congratulate the contributors for their efforts in submitting outstanding proposals for these projects. Those who could not shortlist this time, I wish a huge success for them and hope to connect again next year for the advanced projects.
Collection of my ‘Data Science’ and ‘Research’ related articles on Medium
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This post is a collection of my articles published on Medium which are majorly related to Time Series Analysis, Data Science, and Research.
My research work is majorly moving around Data Science, Time Series Analysis and their applications in different domains. While doing so, I came across several difficulties, problems, and possible solutions. I have been collaborating with many professionals, which involves Professors, Researchers, Data Scientists, and Managers among others. Each professional had his distinct skills and I have tried to grab some of them. Based on my experience, I have tried to document my thoughts and research directions in the form of Medium stories. This is my Medium writer profile: https://neerajdhanraj.medium.com/. You may follow me on Medium for regular updates.
Vignette for GuessCompx, an R package for empirical estimation of time and memory algorithm complexities
in Viggnette
This post is a demonstration for GuessCompx, an R package for empirical estimation of time and memory algorithm complexities.
This post introduces GuessCompx which is an R package that performs an empirical estimation on the time and memory complexities of an algorithm or a function, and provides a reliable, convenient and simple procedure for estimation process. This package is available on CRAN (https://cran.r-project.org/package=GuessCompx) and its published article is: Agenis-Nevers, M., Bokde, N.D., Yaseen, Z.M. et al. An empirical estimation for time and memory algorithm complexities: newly developed R package. Multimed Tools Appl 80, 2997–3015 (2021). https://doi.org/10.1007/s11042-020-09471-8.