About

Author’s Bio

Hi! I’m Rahul Raoniar, a doctoral student, pursuing research in Transportation System Engineering (Department of Civil Engineering) at 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. Prior to arriving at IIT Guwahati, I have earned a master’s degree in Transportation Engineering from Central Road Research Institute (CSIR-CRRI Lab, Delhi, India) and worked as a Trainee Scientist in CSIR-CRRI for consecutive 3 years; where I have studied the performance of Delhi’s transport systems.

Interests

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

Technical Skills & Expertise

  • Programming: Python, R, MySQL [Data Structures, and Algorithms].
  • Core Transportation: Human Behaviour and Psychology, Vulnerable Road Users, Road Users’ Safety, Injury Epidemiology, and Injury Prevention, Transportation Planning and Traffic Engineering.
  • Applied Data Science: Data Science Pipeline (Data collection, Manipulation, Exploratory Analysis, Statistical Analysis, 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, Object-Oriented Programming (OOP), Google Sheet, Excel, Git and GitHub.
  • Applied ML: Classification (Binary, multi-nominal and ordinal logit models) and Regression (Multiple Linear Regression, lasso and ridge regression), Count models (Poisson, negative binomial, truncated and censored, and hurdle and zero inflated regression), Survival analysis (Kaplan Meier estimate, COX-proportional hazard and Accelerated Failure Time 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: Stata, Tableau, Tableau Prep, QGIS, SPSS, SPSS AMOS, LaTeX (Overleaf), Microsoft Office, Notebooks (Jupyter, Google Collab etc.) and IDEs (Visual Studio 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), 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), Big Data Analytics (PySpark) and Web Application (streamlit).

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

font name: DeliusSwashCaps-Regular
font link: https://fonts.google.com/specimen/Delius+Swash+Caps
font author: Natalia Raices
font author site: https://plus.google.com/101900678527911193079
icon designer: P Thanga Vignesh
icon designer link: /amoghdesign