Machine Learning Data Science

Joshua Lizardi

*************@*****.***

Professional Experience

Assistant Professor of Mathematics (2024 – Present) Trine University, Angola, IN

• Teach Intermediate Algebra, College Algebra, Math for Elementary Teachers, and Business Analytics.

• Develop and deliver course content, maintain Moodle pages, and provide academic support.

• Write Open Educational Resources (OER) for multiple courses.

• Conduct research in data analytics and machine learning applications in education. Graduate Professor & Academic Advisor (2022 – 2025) Trine University, Remote / Detroit, MI

• Manage online graduate courses in Statistics, Data Science, and Business Analytics.

• Facilitate discussions, hold online office hours, and assess student work.

• Provide academic advising, student support, and visa guidance.

• Track student progress and implement retention strategies. Data Analyst (2019 – 2020)

Shindigz, South Whitley, IN

• Conducted sales forecasting using regression and time series analysis.

• Automated data cleaning and reporting processes in SQL and Python.

• Performed A/B testing on marketing strategies to increase conversion rates.

• Developed dashboards and reports in Tableau and Power BI to track e-commerce performance.

• Built predictive models to optimize inventory management and reduce excess stock.

• Analyzed customer behavior to drive targeted marketing campaigns.

• Developed an application that filtered and aggregated customer data and helped create an automated tool for customer segmentation in scheduled catalog mailings. (R, SQL, Power BI) Data Science & Business Analytics Projects

• Topic Modeling & Paper Recommendation – Used Latent Semantic Analysis & K-means clustering to classify journal articles and recommend related papers. (Python)

• Customer Churn Prediction – Applied Logistic Regression & K-Modes clustering to identify at-risk customers and predict churn with 81% accuracy. (Python)

• Black Bear Weight Estimation – Built a Multiple Linear Regression model to estimate live body weight with an R of 96.81%. (Minitab)

• Fuel Efficiency Prediction – Modeled Miles Per Gallon (MPG) using Regression Analysis, achieving an R of 87%.

(Minitab, R)

• Optimizing Wi-Fi Access Points – Applied K-means clustering to optimize coverage and reduce infrastructure costs. (Python, Minitab)

• ARIMA Time Series Forecasting – Modeled Winnebago RV Sales & Sunspot Numbers using ARIMA models to make accurate future predictions. (R)

Education & Certifications

M.S., Data Analytics (2018 – 2020)

Western Governors University, Fort Wayne, IN Data

Science, Advanced SQL, Machine Learning, Data Mining, and Analytics.

M.S., Applied Mathematics (2015 – 2017)

Purdue University, Fort Wayne, IN

Regression Analysis, Time Series Analysis, Optimization, Artificial Intelligence.

B.S., Mathematics (2010 – 2014)

Mercy College, New York, NY

Mathematical Modeling, Numerical Analysis, Probability & Statistics, Java.

Certifications

• Oracle: Database SQL Certified Associate

• SAS: Certified Statistical Business Analyst Using SAS 9

• SAS: Predictive Modeling Using Logistic Regression

• SAS: Programming Essentials & Data Manipulation

• Pi Mu Epsilon: Scholarly Achievement in Mathematics

• Udemy: Statistics for Data Science and Business Analysis 1

Full-Day Lecture: Modern Statistics, Data Analytics, Machine Learning, and AI As part of my role at Trine University, I conduct an intensive 8-hour lecture at the Detroit Education Center, designed to provide students with a comprehensive foundation in modern statistical methods, data analytics techniques, and advancements in machine learning and artificial intelligence. This session is structured to equip students with both theoretical knowledge and practical applications across various domains. Lecture Outline

1. Introduction to Modern Statistics

• Descriptive vs. Inferential Statistics

• Probability Theory and Distributions

• Hypothesis Testing and Confidence Intervals

• Regression Analysis (Linear, Logistic, and Multivariate) 2. Data Analytics and Visualization

• Data Cleaning and Preprocessing Techniques

• Exploratory Data Analysis (EDA)

• Data Visualization with Power BI, Tableau, and Python

• Feature Engineering and Selection for Predictive Modeling 3. Machine Learning Fundamentals

• Supervised vs. Unsupervised Learning

• Decision Trees, Random Forests, and Gradient Boosting

• Clustering Techniques: K-Means and Hierarchical Clustering

• Model Evaluation and Cross-Validation

4. Deep Learning and AI Applications

• Introduction to Neural Networks and Deep Learning

• Convolutional Neural Networks (CNNs) for Image Processing

• Natural Language Processing (NLP) with AI-driven Models

• Ethical AI and Bias in Machine Learning

5. Time Series Forecasting and Predictive Analytics

• ARIMA and Exponential Smoothing Methods

• Recurrent Neural Networks (RNNs) for Sequential Data

• Business and Financial Forecasting Applications

6. Hands-On Application and Case Studies

• Live Coding Session in Python (scikit-learn, TensorFlow, pandas)

• Real-World Business Problem: Customer Churn Prediction

• AI in Healthcare: Predicting Disease Progression

• Optimizing E-Commerce Marketing with Data Science Objectives and Learning Outcomes

By the end of this full-day lecture, students will:

• Gain a strong foundation in statistical analysis and data-driven decision-making.

• Understand key machine learning algorithms and their real-world applications.

• Develop practical experience with Python, Power BI, and Tableau for data analytics.

• Learn how AI and deep learning models are shaping industries today.

• Work on hands-on case studies to apply concepts to business, finance, and healthcare. Teaching Methodology

This session is designed to be interactive and application-driven, incorporating:

• Lecture-Based Learning – Theory and conceptual understanding.

• Hands-On Coding Exercises – Python-based practical demonstrations.

• Case Study Analysis – Real-world applications and problem-solving.

• Q&A and Discussion – Encouraging student engagement and critical thinking. This immersive learning experience prepares students for real-world data science, analytics, and AI roles, ensuring they are equipped with the necessary skills to excel in their careers. 2

Professional Experience

Data Analyst Shindigz Fort Wayne, IN 2019–2020

• Time Series Analysis (2020) – Developed advanced time series models to predict inventory demand at scale, improving stock management efficiency by 25

• Causal Impact Analysis (2020) – Assessed the effectiveness of promotional campaigns, leading to a 15% increase in ROI by identifying high-impact strategies.

• RFM (Recency, Frequency, Monetary Value) Analysis (2019) – Segmented customer data to optimize marketing efforts, enhancing customer retention and increasing sales by 18

• Market Basket Analysis (2019) – Identified key product bundling opportunities, driving cross-selling strategies that resulted in a 12

• Causal Impact Analysis (2019) – Evaluated the success of catalog drops, providing actionable insights that improved campaign targeting and reduced marketing costs by 10 3

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