I have in-depth experience on quantitative research analysis on generating testable hypotheses, formatting and managing large data sets, conducting descriptive, inferential statistics and evaluating machine learning models using open source tools like R, Python, database query language like SQL and other visualization tools like Tableau.
My specialties include but are not limited to: regression analysis, multivariate analysis, time series analysis, data cleaning, modeling and visualisation.
Please connect with me about any of your data science and analytic needs so we can discuss how i can help you and your team.
Master of Computer Application(MCA)
RG Kedia College(Osmania University)
BSc in Mathematics, Statistics & Computer Science
Aurora college(Kakatiya University)
Fri, Dec 1, 2017,
Mon, Nov 13, 2017, event-Hands-on Data Jamboree
Thu, Sep 14, 2017, Portland R User Group Workshop
Fri, Jun 24, 2016, Hands-on Data Jamboree
A guide to getting up and running with blogdown, GitHub, and Netlify
Improving conversational use of spoken language is an important goal for many new interventions and treatments for children with neurodevelopmental disorders. However, progress in testing these treatments is limited by the lack of informative outcome measures to indicate whether or not an intervention or treatment is having the desired effect on a child’s conversational use of language (i.e., discourse skills). The goal of this project is to evaluate whether Natural Language Processing methods can be translated into meaningful outcome measure for individuals with a range of neurodevelopmental disorders. This project was recently funded by the National Institute of Deafness and Other Communication Disorders.
The goal of this project is to develop and validate a novel objective measurement tool, the Multi-modal Autism Phenotype Snapshot (MAPS), for use in clinical trials targeting core symptoms of autism. This project was funded by a Catalyst Award from the Oregon Clinical & Translational Research Institute.
The objective of this project is to further understanding of sex differences in the fundamental patterns of behavioral and social functioning relevant to the clinical presentation of ASD. Guided by our previous research, we applied Natural Language Processing based methods to transcripts of natural language samples in order to quantify features of atypical language use in females with ASD.