About

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Welcome to a showcase of some of the projects from my Master’s in Data Science journey at Ferris State University, MI, USA, where I’ve refined my skills in R, Python, Machine Learning, Predictive Analytics, Data Mining, Data Visualization, and Statistical Analysis. Each project highlights my ability to tackle complex challenges through rigorous, data-driven solutions, combining technical expertise with statistical precision and creative problem-solving.

With a solid foundation in statistics, computer science, and business, as well as a passion for research, I have applied advanced statistical methodologies to uncover meaningful insights and drive data-informed decision-making. My hands-on experience as a Research Assistant has deepened my expertise in academic research, contributing to innovative projects at the intersection of data science and statistical analysis. Additionally, my diverse work experience across multiple sectors has strengthened my ability to adapt data science solutions to real-world business challenges. Now, I am eager to pursue a Ph.D., push the boundaries of research and analytical methodologies, and contribute to the evolving field of data science.

Projects

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My Presentation At Joint Statistical Meetings 2024

This project is about improving the diagnostic accuracy in multivariate settings. The Multivariate Youden Index is a key measure in evaluating the effectiveness of medical diagnostic tests that involve multiple biomarkers.

JSM 2024 | View
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Loops & Spuervised Machine Learning In R

This project demonstrates the use of loops and supervised machine learning in R, showcasing iterative techniques like for-loops and while-loops. It also includes the implementation of basic machine learning models for predictive analysis.

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Statistical Learning in Python

This project implements classification algorithms such as Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Naive Bayes, and K-Nearest Neighbors using python to analyze data.

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Data Analysis & Visualizations In R

This project demonstrates data manipulation, statistical analysis, and visualizations using R programming. It involves building linear models to explore relationships between variables. It also includes creating various visualizations.

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Olympics Medals Prediction Using Machine Learning

In this project I used Python to build and evaluate various machine learning models with differernt variations to predict the number of medals won by participating countries in international sporting events.

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Running Image Processing Examples Using TensorFlow

This project uses TensorFlow, leveraging convolutional neural networks (CNNs) for image classification. The project showcases how TensorFlow can be effectively utilized for real-world image recognition problems.

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Loops in Python

explores the fundamentals of using loops to perform repetitive tasks efficiently. The project covers different types of loops, including for-loops, while-loops, and nested loops, to automate tasks such as iterating over data, controlling program flow, and optimizing code performance.

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Churn Analysis Using R

This project focuses on analyzing customer churn data to identify patterns and trends that affect customer retention. Through visualizations like bar plots, bubble charts, and correlation heatmaps, insights are drawn from features such as credit scores, geography, and customer activity.

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Data Analysis & Visualization using SAS

In this project, SAS Help datasets like Stock, Class, Cars, Heart, and Wages were utilized to perform comprehensive data analysis and visualization. Key insights were drawn by analyzing trends, distributions, and relationships.

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Numpy & Pandas in Python

This project explores the use of Numpy for efficient numerical computations and Pandas for data manipulation tasks, such as data cleaning and aggregation. It demonstrates how these libraries simplify handling and analyzing large datasets in Python.

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Data Wrangling in Python

This project focuses on Data Wrangling in Python, using famous library like Pandas to clean, transform, and prepare data for analysis. It showcases techniques for handling missing values, filtering data, and reshaping datasets to derive meaningful insights.

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Data Mining Using IBM SPS Modeler

This data mining project analyzes three distinct datasets following the CRISP-DM methodology, involves clustering countries based on their IT usage, uncovering association rules for cross-selling products, and predicting house prices in U.S. towns.

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Salary Estimation Using Supervised Machine Learning

This project applied supervised machine learning models in IBM SPSS Modeler to estimate sales representatives' salaries in the tech industry. Various models including Multiple Linear Rrgression, KNN and Neural Networks were evaluated using Mean Squared Error.

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Predictive Analysis of College Admissions Data

This project aimed to develop an accurate predictive model for college admissions outcomes, focusing on whether applicants would be admitted based on various attributes. A dataset containing information on 17,339 applicants across three colleges within a university was analyzed.

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NoSQL:Creating Dashboard in MongoDB

This project involved creating an interactive dashboard to visualize Olympic gold medal data, leveraging MongoDB for data storage and management.MongoDB was used for its flexibility in handling complex, unstructured data, allowing easy aggregation and retrieval for dynamic chart generation.

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White Paper: Database Security & Ethics

This project examines the multifaceted aspects of database security and ethics, exploring critical issues such as responsible data usage, breach accountability, and legal frameworks. It delves into managing sensitive data, emphasizing the need for clarity in intentions when using or collecting data.

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Database Administration: Disaster Management Plan

This project presents a detailed disaster management plan for a large-scale retail organization that depends on outsourced systems for its operations. The plan addresses four critical disaster scenarios: wide-scale power outages, system failures, ransomware attacks, and unauthorized access.

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Evaluating Cloud vs. In-House Database Hosting

This white paper focuses on evaluating the critical factors involved in choosing between cloud-hosted databases and in-house database management. The white paper explores key considerations such as the costs associated with each option, potential for early termination, the impact on staff levels, and security concerns.

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Applied Statistics: Logistic Regression & SAS

This project focuses on applying logistic regression using SAS software to analyze data from the Behavioral Risk Factor Surveillance System (BRFSS). I used logistic regression to model relationships between various behavioral risk factors (such as smoking, alcohol use, and physical activity) and health outcomes like chronic diseases or conditions.

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Applied Statistics: Simple Linear Regression & SAS

This in-class project focused on applying simple linear regression using SAS to solve statistical problems and analyze provided data. The project involved answering various questions related to the concepts of linear regression, including understanding relationships between variables, calculating regression coefficients, and interpreting statistical outputs.

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Applied Statistics: Multiple Linear Regression

This in-class project focused on the theoretical aspects of multiple linear regression as part of an applied statistical methods course. The objective was to understand and explore how multiple independent variables can be used to predict the outcome of a dependent variable and how multiple linear regression can be applied to complex datasets and scenarios.

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Applied Statistics: Multiple Linear Regression using R

This project involved applying multiple linear regression using R to analyze the "Graduate Admission 2" dataset, downloaded from Kaggle.com.The goal of this project was to model and predict the likelihood of a student being admitted to a graduate program based on these features. R was used for statistical analysis and evaluating model fitness.

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Applied Statistical Methods: Statistical Solutions & SAS Analysis

This midterm exam for the Applied Statistical Methods course focused on solving various statistical questions using theoretical knowledge and practical data analysis with SAS software. The exam tested a range of concepts, particularly regression analysis, hypothesis testing, and estimation techniques.

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Data Visualization: Interactive Charts Using R-Integrated Javascript

This project explores interactive data visualization techniques using R and JavaScript libraries. The goal was to integrate various JavaScript-based libraries, such as threejs, leaflet, visNetwork, and highcharter, with R to create dynamic and interactive visualizations that enhance data exploration and analysis.

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Contact Me

itsnayyer@hotmail.com
+1 586 838 6833
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