"Visualizations act as a campfire around which we gather to tell stories" - Al Shalloway.
Here are the boxplots depicting different skills and their expertise levels.
“In God we trust; all others bring data.” ― W. Edwards Deming.
With the abundance of data in today's world, I aspire to be an empowered professional with vision and grace of building insight-driven solutions for enterprises to use data for strategic advantage.
Working as a Data Analyst for approximately 4 years, laid the foundation of my problem-solving, strong analytical, leadership, facilitation, communication and client/customer relationship management skills. As a part of innovation projects, I have involved in various Proof Of Concepts on tools and technologies in Hadoop, Big Data, Spark and Big Data Analytics on cloud infrastructure provided by Amazon Web Services (AWS).
As a part of my graduate curriculum here at the University of Texas Dallas, I am focused on learning Data Science tools and techniques, Machine Learning, Neural Nets & Deep Learning techniques on the infrastructure configured on the cloud provided by IAAS (Infrastructure as a Service) providers like AWS & Azure.
I would especially like to mention here that I am also an AWS - certified Solution Architect Associate, EMC certified DataScience Associate and have various other certifications that I have earned from digital platforms like Udemy, Coursera, DataCamp, etc.
On a personal level, I am detail- oriented, organized, and precise in my work; the only thing cleaner than my room are my spreadsheets. I have strong communication & I am comfortable on my own facing the numbers, but I really enjoy being part of a motivated team of smart people.
Arpit Chaukiyal
7780 McCullam Blvd
Dallas, TX 75252 US
(469)929-4740
chaukiyal.arpit@gmail.com
Master in Business Analytics • Aug 2017 - May 2019
This program is a perfect blend of business and technical courses. The core courses of Machine Learning, Marketing, Statistics, Big Data helped me in getting an understanding of both worlds. The program conducts many workshops and boot camps to help improve analytical skills. They organize alumni talks and project demonstration competitions so that students can develop industry contacts.
B.Tech. Degree in Information Technology • Aug 2008 - June 2012
UPTU is one of the most reputed and largest universities in India. In my undergraduate course completed basic technical courses like Database foundations, Object Oriented Programming etc. These courses are helped me in forging very strong technical and analytical base.
Data Analyst • June 2016 - July 2017
• Developed data solution based on predictive and behavioral models via statistical analysis like regression and classification techniques like decision trees and K-nearest neighbor for life insurance client which optimized their new business inflow by 17%.
• Developed a model that predicts the insurer’s fraudulent behavior in paying the premium as well as estimating the premium amount depending on undertaking data resulting in 11% reduction in defaulter volume.
• Created an interactive Tableau dashboard that helped the stakeholders and VPs to understand data-driven actionable insights and take business decisions
Data Analyst • Oct 2013 - Jun 2016
• Liaised with the client team to develop a model that predict the work-hours required to complete the task depending upon resources involved and the complexity of the task to align the project with CMMI-5 standards resulting in a 15% increase in team's efficiency.
• Incorporated linear regression, logistic regression and random forest techniques to develop statistical models for demand forecasting and procurement analytics which had in decreased the procurement cost by 7% annually.
• Trained 30 plus employees over the period of 8 months in technical and process-oriented modules which reduced on-boarding time by up to 20%
"Visualizations act as a campfire around which we gather to tell stories" - Al Shalloway.
Here are the boxplots depicting different skills and their expertise levels.
Implemented SVM, Decision Trees and ADA-boosting models and experimented with different kernels of SVM & performed hyperparameter tuning to maximize the accuracy of 97%
SVM, Decision Trees, Boosting.
Implemented supervised linear & logistic regression model using gradient descent algorithm. Compared train/test error metrics and plotted the results against learning rate for gradient descent & convergence threshold
Linear Regression, Logistic Regression, gradient descent
Shiny app created to study social network of emails shared within European Union. Analysed and visualized the network data of email exchanges from a European research institution having more than 1000 nodes and 25000 vertices.
SNA, R, Shiny, iGraph
Scraped the reviews of the users from the website (www.cars.com) by R code along with the star rating. Performed normalization measure like stemming & lemmatization along with document tagging. Constructed logit model that predicts user rating using sentiment scores of user’s reviews with an accuracy of 80%.
NPL, Web Scraping, Sentiment Analysis
Configured Flume for stream the twitter data into HDFS. Flatten the twitter JSON data into Hive table using serde-jars and calculated the AFINN score for analysing the sentiments of all tweets and re-tweets.
HIVE, Affin Score, Flume, SERDE-JARS
Focused on the effect of shall-issue in the 51 states (50 states + District of Columbia) in USA for a period of 23 years. Analysed the pannel data and compared various time and entity fixed effects models.
Pannel Data, Time and Entity fixed effects models, R
For this project, I have implemented Logic Gates using the basics of Neural Network. I’ve created a perceptron that uses different activation function like Sigmoid Activation function, Step Activation function etc. in our neuron.
Neural Network, Logical Gates, R
This is the ptoject I am currently working on. The inspiration for this project is been taken from Kaggle's Competition. In this I am trying analyze a Google Merchandise Store (also known as GStore, where Google swag is sold) customer dataset to predict revenue per customer.
Kaggle, Python, PredictionEither have work opportunities, wanted to collaborate on some project or just to say "Hi", fill the form and hit the 'SUBMIT' button. I will get back to you in no time.
You can also shoot me an email at chaukiyal.arpit@gmail.com or call me on my contact number.