Resume
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Sohom Ghosh b in.linkedin.com/in/sohomghosh T +91 8001734384 B [email protected] E github.com/sohomghosh S UMMARY – Data Science Enthusiast with relevant research, industrial experience & Publications in International Journals [ISSE (Springer, NASA Journal), IJARCST] & Conferences [ICACNI (Springer), ICACCE (IEEE)] – Completed courses on Big Data Analytics, Data Mining [IIT -KGP]; Social and Economic Networks [Stanford University (Coursera)]; Machine Learning for Data Science and Analytics [Columbia Univer- sity (edX)]; Artificial Intelligence – Expertise in Text Analytics, Recommendation System & Statistical Modeling T ECHNICAL S KILLS TECHNOLOGIES R, Python, SQL, Hadoop, Spark, Tableau, MS-Office TECHNIQUES Regression, Random Forest, SVM, GBM, Neural Net, Deep Learning, Clustering etc. WORK EXPERIENCE APRIL 2016 – PRESENT FN MathLogic Consulting Services Pvt. Ltd, Gurgoan Analyst • Project: Prediction of whether a customer will re-buy an asset product; Classified the data using Random Forest, Deep Neural Net, GBM in R (h2o) • Data Visualization: Prepared dashboards & re- ports using MS-Excel & Shiny R • Text Analytics: Topic Modeling, Text Classifica- tion [Done as part-time intern Apr - Jun’16] • Capability Development: Machine Learning, Model Assessment, Ensemble Learning, Deep Learning, Au- tomation, Time Series, Optimization, Cloud (AWS) SEPTEMBER 2015 – JANUARY 2016 Novel R & D India (P) Ltd., Kolkata Big Data Faculty (Part-Time) • Courses Taught: Big Data Analytics - Hadoop, R DECEMBER 2013 – May 2016 Heritage Institute of Technology, Kolkata Undergraduate Student Researcher • Sentiment Analysis on Movie Reviews [IJARCST, Vol 3, Issue 1, pp 41-46] (journal) – Classified reviews by Lexicon, Machine Learn- ing (SVM, Neural Net, Random Forest), Deep Learning (word2vec) based approaches, En- sembled them using Deep Neural Network – Devised an algorithm to suggest words to re- viewers by analyzing the title of reviews • Recommendation System based on Product Pur- chase Analysis [ISSE, Springer London, ISSN:1614-5054, Vol 12, Is- sue 3, pp 177-192] (NASA journal) [ICACNI, SIST Springer, ISBN: 978-81-322-2538-6, Vol 43, pp 581-591] (conference) – Analyzed various properties of Amazon Co-purchase Network (Clustering Co-efficient, Degree Dis- tributions, Popularity Trend etc.) – Analyzed dynamic buying patterns & developed algorithms to recommend products • Solving Real Life Problems using Machine Learn- ing Techniques • Predicted Sale of Products in stores across different cities (Used XGBoost, Deep Net) • Predicted Customer Churn in a Telecom Net- work (Used Random Forest, SVM, Neural Net) • Extraction & Analysis of Publication Data of Conferences [IEEE ICACCE-2015, pp 588-593] • Analysis of Computer Science publications [WIS & COLLNET 2015] (poster) – Analyzed content of research papers to develop a Recommendation System – Examined the collaboration characteristics & trends of research for 60 years JUNE 2015 – JULY 2015 Indian Statistical Institute, Kolkata Summer Research Intern • Prediction of Cancellations of Taxi Reservations Developed a predictive model for classifying new book- ings as to whether they will eventually get canceled due to unavailability of cabs; Used Naive Bayes’, SVM, Neural Net, Random Forest in R, Weka EDUCATION 2016 B. Tech (Computer Science & Engg.) Heritage Institute of Technology, 8.22/10 2012 Senior Secondary Education (CBSE) Sarvodaya Sr Secondary School, 80.80 % 2010 Secondary Education (ICSE) St. Xavier’s School, 91.57 % P ERSONAL DETAILS ADDRESS: K-36, opp. Presidium School, Sec-51, Gurgoan - 122018, Haryana, India HOBBIES: Learning from MOOCs, Solving Data Science Challenges, Playing Mouthorgan & Tabla
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Sohom Ghoshb in.linkedin.com/in/sohomghosh
T +91 8001734384
E github.com/sohomghosh
SUMMARY
– Data Science Enthusiast with relevant research,industrial experience & Publications in InternationalJournals [ISSE (Springer, NASA Journal), IJARCST]& Conferences [ICACNI (Springer), ICACCE (IEEE)]– Completed courses on Big Data Analytics, DataMining [IIT-KGP]; Social and Economic Networks[Stanford University (Coursera)]; Machine Learningfor Data Science and Analytics [Columbia Univer-sity (edX)]; Artificial Intelligence– Expertise in Text Analytics, RecommendationSystem & Statistical Modeling
TECHNICAL SKILLS
TECHNOLOGIES R, Python, SQL, Hadoop,Spark, Tableau, MS-Office
TECHNIQUES Regression, Random Forest,SVM, GBM, Neural Net, DeepLearning, Clustering etc.
WORK EXPERIENCE
APRIL 2016 – PRESENTFN MathLogic Consulting Services Pvt. Ltd, GurgoanAnalyst• Project: Prediction of whether a customer willre-buy an asset product; Classified the data usingRandom Forest, Deep Neural Net, GBM in R (h2o)• Data Visualization: Prepared dashboards & re-ports using MS-Excel & Shiny R• Text Analytics: Topic Modeling, Text Classifica-tion [Done as part-time intern Apr - Jun’16]• Capability Development: Machine Learning, ModelAssessment, Ensemble Learning, Deep Learning, Au-tomation, Time Series, Optimization, Cloud (AWS)
SEPTEMBER 2015 – JANUARY 2016Novel R & D India (P) Ltd., KolkataBig Data Faculty (Part-Time)• Courses Taught: Big Data Analytics - Hadoop, R
DECEMBER 2013 – May 2016Heritage Institute of Technology, KolkataUndergraduate Student Researcher• Sentiment Analysis on Movie Reviews [IJARCST,Vol 3, Issue 1, pp 41-46] (journal)
– Classified reviews by Lexicon, Machine Learn-ing (SVM, Neural Net, Random Forest), Deep
Learning (word2vec) based approaches, En-sembled them using Deep Neural Network
– Devised an algorithm to suggest words to re-viewers by analyzing the title of reviews
• Recommendation System based on Product Pur-chase Analysis[ISSE, Springer London, ISSN:1614-5054, Vol 12, Is-sue 3, pp 177-192] (NASA journal)[ICACNI, SIST Springer, ISBN: 978-81-322-2538-6, Vol43, pp 581-591] (conference)
– Analyzed various properties of Amazon Co-purchaseNetwork (Clustering Co-efficient, Degree Dis-tributions, Popularity Trend etc.)
– Analyzed dynamic buying patterns & developedalgorithms to recommend products
• Solving Real Life Problems using Machine Learn-ing Techniques
• Predicted Sale of Products in stores acrossdifferent cities (Used XGBoost, Deep Net)
• Predicted Customer Churn in a Telecom Net-work (Used Random Forest, SVM, Neural Net)
• Extraction & Analysis of Publication Data ofConferences [IEEE ICACCE-2015, pp 588-593]• Analysis of Computer Science publications[WIS & COLLNET 2015] (poster)
– Analyzed content of research papers to developa Recommendation System
– Examined the collaboration characteristics &trends of research for 60 years
JUNE 2015 – JULY 2015Indian Statistical Institute, KolkataSummer Research Intern
• Prediction of Cancellations of Taxi ReservationsDeveloped a predictive model for classifying new book-ings as to whether they will eventually get canceleddue to unavailability of cabs; Used Naive Bayes’, SVM,Neural Net, Random Forest in R, Weka
EDUCATION
2016 B. Tech (Computer Science & Engg.)Heritage Institute of Technology, 8.22/10
2012 Senior Secondary Education (CBSE)Sarvodaya Sr Secondary School, 80.80 %
2010 Secondary Education (ICSE)St. Xavier’s School, 91.57 %
PERSONAL DETAILS
ADDRESS: K-36, opp. Presidium School, Sec-51,Gurgoan - 122018, Haryana, India
HOBBIES: Learning from MOOCs, Solving DataScience Challenges, PlayingMouthorgan & Tabla
