About

Author’s Bio

Hi! I’m Rahul Raoniar, a doctoral student pursuing research in Transportation System Engineering (Department of Civil Engineering) at the Indian Institute of Technology Guwahati. I love learning new things every day. Currently, I am working with Prof. Akhilesh Kumar Maurya on pedestrian risk-taking behaviour at intersection crosswalks. Before arriving at IIT Guwahati, I earned a master’s degree in Transportation Engineering from Central Road Research Institute (CSIR-CRRI Lab, Delhi, India) and worked as a Trainee Scientist at CSIR-CRRI for consecutive 3 years; where I studied the performance of Delhi’s transport systems.

Interests

Transportation Planning and Traffic Engineering, Pedestrian and Cyclist Safety Analysis, R & Python Programming, Statistical Analysis, Data Science, Machine Learning, Big Data Analytics, and Data Structures and Algorithms.

Technical Skills & Expertise

  • Programming Languages: Python, R, MySQL and MongoDB.
  • Core Transportation: Road Users’ Safety Evaluation, Vulnerable Road Users, Injury Epidemiology, and Injury, Prevention, Transportation Planning and Traffic Engineering.
  • Applied Data Science: Data Science Pipeline (Data collection, Exploratory analysis, Statistical Analysis, Modelling, Deployment, and Report making), Statistics (Experimental design, Exploratory and Confirmatory data analysis, Hypothesis testing, and Bayesian A/B testing), Geospatial data analysis and Visualisation, Time series forecasting, Big data analytics, OOP, Git and GitHub, Google sheet, and Excel.
  • Applied ML: Classification (Binary, multi-nominal and ordinal logit models) and Regression (Multiple linear regression, lasso and ridge regression), Count models (Poisson, negative binomial, and zero-inflated regression), Clustering (k-means, hierarchical), Survival analysis (KM estimate, COX-PH and AFT models), Mixed Effects Models (random intercepts and slopes), Time Series Forecasting, Association rule mining, Decision trees, Ensemble models (bagging and boosting) and Deep Learning.
  • Core/Analytics Tools: AWS Elastic Beanstalk, Linux CLI, Docker, GitHub Actions (CI/CD), Stata, Tableau, QGIS, LaTeX, MS Office Suite, Notebooks (Jupyter, Google collab etc.) and IDEs (VS Code and PyCharm).
  • Searching: Googling and Searching Stack Overflow.
  • Libraries/Packages: Data Manipulation (pandas, dplyr and dfply), Visualization (ggplot2, matplotlib, seaborn, plotnine, altair and plotly), Feature Engineering and Selection (sklearn and feature engine), Geospatial Analysis and Visualisation (folium and geopandas), Dashboard (plotly Dash and tableau), Text Analysis (regex), Time Series Forecasting (prophet and statsmodels), Machine Learning (scikit learn, statsmodels, tidymodels, pycaret, keras, tensorflow and H2o), ML Lifecycle Management (MLflow), Big Data Analytics (PySpark) and Web Application (streamlit and flask).

Leadership

Developer of “pysustrans” Python library | May 2022 – Present

  • Developer of an Open-Source data analytics library for performing regular analytics tasks specific to transportation data.

Taught “Python for Scientific Computing and Data Science” | April 2022, IIT Guwahati, India

  • Taught “Python for Scientific Computing and Data Science” in a three-week-long workshop with 300+ registered participants.

Guided Master’s Students [MTP Project] | 2018-Present, IIT Guwahati, India

  • Collaborated with Ph.D. guide and four Master’s students to formulate a hypothesis, perform data analysis, interpret results and prepare dissertation report.

Teaching Assistant in Transportation Planning and Traffic Engineering Lab. | 2016-2022, IIT Guwahati, India

  • Collaborated with instructors and 6 other TAs to lead recitations, grade labwork, and answer 60+ students’ queries.

My Book Reads

Data Science & ML Book Reads

  • A Whirlwind Tour of Python (Jake VanderPlas)
  • Learning R (Richard Cotton)
  • R for Data Science Cookbook (Yu-Wei, Chiu)
  • Machine Learning with R Cookbook (Yu-Wei, Chiu)
  • R for Data Science (Garrett Grolemund and Hadley Wickham)
  • R Graphics Cookbook (Winston Chang)
  • Python for Data Analysis (Wes McKInney)
  • Deep Learning with Python (François Chollet)
  • The Hitchhiker’s Guide to Plotnine (Jodie Burchell and Mauricio Vargas)
  • The Hitchhiker’s Guide to GGPlot2 (Jodie Burchell and Mauricio Vargas)
  • A Gentle Introduction to Stata (Alan C. Acock)
  • A Visual Guide to Stata Graphics (Michael N. Mitchell)
  • Data Management Using Stata – A Practical Handbook (Michael N. Mitchell)
  • Build a Career in Data Science (Emily Robinson and Jacqueline Nolis)
  • Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks (Will Kurt)
  • Pandas 1.x Cookbook (Matt Harrison and Theodore Petrou)
  • Practical Statistics for Data Scientists (Peter C. Bruce, Andrew Bruce, and Peter Gedeck)
  • Forecasting Time Series Data with Facebook Prophet (Greg Rafferty)

Story and Fiction Reads

  • Hitchhiker’s Guide to the Galaxy (Douglas Adams)
  • Cosmos (Carl Sagan)
  • The Subtle Art of Not Giving a F*ck (Mark Manson)
  • Rich Dad Poor Dad (Robert T. Kiyosaki)
  • The Psychology of Money (Morgan Housel)
  • How to Avoid a Climate Disaster (Bill Gates)
  • The Diary of a Young Girl (Anne Frank)
  • Elon Musk (Ashlee Vance)
  • I, Steve – Steve Jobs in His Own Words (George Beahm)
  • Talk Like TED (Carmine Gallo)
  • The Presentation Secrets of Steve Job (Carmine Gallo)
  • Think Like a Monk (Jay Shetty)
  • The Alchemist (Paulo Coelho)
  • Think and Grow Rich (Napoleon Hill)
  • The Power of Positive Thinking (Dr. Norman Vincent Peale)
  • The Leader Who Had No Title (Robin Sharma)
  • The Monk Who Sold His Ferrari (Robin Sharma)
  • Who Will Cry When You Die (Robin Sharma)
  • Tuesdays with Morrie (Mitch Albom)
  • How to Win Friends and Influence People (Dale Carnegie)
  • Ignited Minds (A. P. J. Abdul Kalam)
  • Wings of Fire (A. P. J. Abdul Kalam)
  • Turning Point (A. P. J. Abdul Kalam)
  • Dairy of a Wimpy Kid – Book Series (Jeff Kinney)
  • Into That Heaven of Freedom (Rashmi Bansal)
  • Finnish Lessons – What can the world learn from educational change in finland? (Pasi Sahlberg)
  • Project Hail Mary (Andy Weir)

Logo Design Credits

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