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Mohammad Ali Alamdar Yazdi, Ph.D.

Associate Professor of Practice
Operation Management & Business Analytics 
Johns Hopkins Carey Business School

  • A consultant, Associate Professor, and data scientist with over 10 years of experience in AI, machine learning, data analytics, and data visualization.

  • Strong background in programming and analytics, with expertise in Python, R, Tableau, MySQL, and Excel.

  • 7 years of teaching experience, having educated more than 3,000 graduate-level students in data science topics.

  • JHU_logo_Johns_Hopkins_University_edited
  • LinkedIn

Current Research Project 

The Johns Hopkins Drug Supply Chain Data Dashboard (2024 – present)

7 YEARS OF TEACHING

Current Teaching:

BU.920.623
Big Data Consulting Project

The course includes a partnering organization that provides a large dataset and key questions.
Students work in teams to address these using descriptive and predictive analytics, often with Tableau.

BU.667.310 
Business Analytics

This course equips students with practical skills in descriptive, predictive, and prescriptive analytics Students preprocess data, build predictive models, and apply optimization techniques for informed business decision-making.

BU.520.650
Data Visualization

This project-based course teaches students to use Tableau for data analysis and visualization, empowering them to build compelling and interactive dashboards and make data-driven decisions.

BU.510.650
Data Analytics

This course teaches students to analyze data using machine learning models using R to support decision-making. Topics include EDA, Linear & Logistic Regression, Model Selection Methods, KNN, Decision Trees, and Clustering.

BU.510.615
Python for Data Analysis

This course teaches topics such as Data Structures, Functions, Classes, Numpy, Pandas, Matplotlib, and Seaborn to build practical programming skills for data analysis.

Orientations/Foundation Week Workshops

Developed and led multiple programming workshops to enhance students' skillsets, including R, Python, and Tableau sessions during MS Orientation and MBA Foundation Week events.
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Previous Teaching:

BU.510.601
Statistical Analysis

Probability & Random Variables
Sampling Distributions
Confidence Intervals
Hypothesis Testing
Statistical Inferences
Chi Square
Anova

BU.610.625 Simulation & Strategic Options

Monte Carlo Simulation
Decision Theory
Risk Profile
Real Options
Scenario Planning
Game Theory
Analysis of Contingent Claims.

About Big Data Consulting Project Course

Course Overview

Big Data Consulting Project is an experiential capstone course where full-time MBA students partner with a corporate client to solve real-world business challenges using data analytics. Working in teams, students explore a new dataset each year, apply descriptive and predictive techniques, and deliver actionable insights. The course emphasizes collaboration, critical thinking, and communication, culminating in formal presentations to the client. All work is conducted under a confidentiality agreement to protect proprietary data and business information.

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My Role

As the instructor for this course, I lead and manage the entire consulting engagement, guiding ~60 full-time MBA students in teams each year through the full lifecycle of a real-world data project. I teach key analytical tools such as Tableau, provide strategic direction during weekly team meetings, and ensure that all teams stay aligned with the client’s objectives. Acting as both faculty advisor and project lead, I oversee the quality and impact of each team’s analysis and serve as the main point of contact between the students and the client.

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Project Focus Areas

While details are confidential, project themes over the years have included:

  • Spring 2025: Procurement Optimization & Vendor Analysis

  • Spring 2024: Supply Chain Efficiency & Order Fulfillment Patterns

  • Spring 2023: SKU Rationalization & Supplier Base Risk Assessment

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Note: All project descriptions are general summaries. Specific business problems, data, and analyses are confidential and not publicly disclosed.​​​​

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What Students Are Saying About This Course in the Media

"What was your favorite course as an MBA? My favorite MBA course was the Big Data Consulting Project. It was one of several experiential learning courses in our MBA program. The course was a very analytical and technical experience involving group work like actual business environments. For the course, we were provided a large data set from McCormick Spice Company. Each team analyzed the data to find trends and make recommendations based on those findings. It was very practical knowledge that I definitely needed coming out of the military." 

— Bryan Munoz, 2024 MBA To Watch, Johns Hopkins Carey

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Learn More

Read more about the course on the official course website.

Students' Projects for the Data Visualization Course

Man Hands On Keyboard

Computer Skills

 Python    R    Tableau    MySQL    Excel   

 PHP    HTML/CSS/JS    SPSS    CPLEX 

Skills

Applications Developed

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  • Situation: In early 2021, as a Farsi-speaking community on Clubhouse grew, there were limited tools for users to find Farsi-speaking rooms, as Clubhouse's language recognition and room suggestion features were not fully optimized.

  • Task: I set out to create a web application that would help Farsi-speaking users easily find and navigate rooms where Persian was the primary language, while also providing additional insights and features for enhanced user experience.

  • Action: I single-handedly developed FarsiClubs, using Python and MySQL for backend algorithms and database management, along with PHP for server-side development, and JavaScript, HTML, and CSS for the front-end interface. The application utilized an innovative algorithm I designed to detect Farsi-speaking rooms without relying on language text cues, providing features such as live room tracking, club statistics, and a user-friendly search interface.

  • Result: The application successfully served the Farsi-speaking Clubhouse community by offering real-time access to up to 500 live rooms, detailed club insights, and room activity trends. While the app was discontinued after Clubhouse improved its algorithms in late 2021, FarsiClubs demonstrated my ability to independently design, develop, and deploy a data-driven web application, contributing positively to the community and enhancing my skills in full-stack development, data analysis, and user interface design.

FarsiClubs Application

DRIIVE Web Application

  • Situation: Fuel-efficient driving and routing were growing concerns for both individual drivers and transportation companies, but real-time, accessible tools to optimize eco-driving and eco-routing based on road and weather conditions were lacking.

  • Task: I developed DRIIVE, a real-time web application to reduce fuel consumption by optimizing driving behavior and route selection using dynamic programming and real-time data from Google Maps, OpenStreetMap, and OpenWeatherMap.

  • Action: I built the application using Python for the backend, implementing a dynamic programming model to optimize speed, gear shifts, and fuel consumption based on real-time data from Google Maps, OpenStreetMap, and OpenWeatherMap APIs. The app featured real-time navigation, elevation data, weather conditions, and vehicle-specific parameters. I used MySQL to store and manage data and JavaScript, HTML, and CSS for the front-end interface. The application also incorporated a cruise control simulation as a benchmark for comparison.

  • Result: DRIIVE reduced fuel consumption by up to 15%, helping drivers and transportation companies minimize costs and environmental impact. The application demonstrated the effectiveness of real-time data-driven solutions in optimizing fuel efficiency.

Research Journal Publications

Transportation Analytics:

Hierarchical point process models for recurring safety critical events involving commercial truck drivers: A reliability framework for human performance modeling 

Published in Journal of Quality Technology in 2022

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The association between crashes and safety-critical events: Synthesized evidence from crash reports and naturalistic driving data among commercial truck drivers 
Published in Transportation Research Part C: Emerging Technologies in 2021

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Predicting unsafe driving risk among commercial truck drivers using machine learning: lessons learned from the surveillance of 20 million driving miles 

Published in Accident Analysis & Prevention in 2021

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A Review of Data Analytic Applications in Road Traffic Safety. Part 2: Prescriptive Modeling

Published in Sensors in 2020

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A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling 

Published in Sensors in 2020

Healthcare Analytics:

Managing prior approval for site-of-service referrals: an algorithmic approach

Published in BMC health services research in 2022​​

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Streamlining science with structured data archives: insights from stroke rehabilitation

Published in Scientometrics in 2017

Human Computer Interaction:

Optimization of split keyboard design for touchscreen devices

Published in International Journal of Human–Computer Interaction in 2019

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A data-driven approach to modeling physical fatigue in the workplace using wearable sensors

Published in Applied ergonomics in 2017​​

 

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