Education

Purdue University

B.S. in Mathematics, B.S. in Statistics, Certificate in Applied Data Science (Expected Graduation: Dec 2025)

Relevant Coursework

  • Statistical Theory
    • Topics: Point Estimation, Interval Estimation, Hypothesis Testing, Likelihood Ratio Tests, Linear Regression, AutoRegression, Bayesian Estimation.
    • What I Learned: This course provided a comprehensive understanding of statistical methods used to estimate parameters, test hypotheses, and analyze data through both frequentist and Bayesian approaches.
  • Operations Research
    • Topics: Mathematical Modeling, Linear Programming, Duality, Sensitivity Analysis, Network Flows, Mixed Integer Programming, Game Theory.
    • What I Learned: I learned how to model complex systems and optimize processes through techniques like linear programming, sensitivity analysis, and game theory to solve real-world problems in business and engineering.
  • Signals and Systems
    • Topics: Convolutions, Hilbert Spaces, Fourier Series, Discrete Fourier Transform, Fast Fourier Transform, Filters, Sampling, Image Processing.
    • What I Learned: This course introduced signal processing techniques, including the analysis and transformation of signals using Fourier analysis, and practical applications like image processing and filtering.
  • Analysis
    • Topics: Mathematical Induction, Sets, Topology, Limits, Sequences, Series, Derivatives, Taylor Series, Integration.
    • What I Learned: I gained a rigorous foundation in calculus and analysis, focusing on mathematical proofs, limits, series expansions, and the deep understanding of continuous functions.
  • Epidemiology
    • Topics: Disease Measurement, Screening, Survival Analysis, Study Designs, Measures of Association, Statistical Inference, Bias, Confounding, Interaction, Regression, Causal Inference, Policy Design.
    • What I Learned: This course provided tools for analyzing public health data, with a focus on causal inference, study design, and understanding how different factors interact to influence health outcomes.
  • Regression Analysis
    • Topics: Simple Linear Regression, T-Tests and ANOVA, Variable Transformations, Normality and Homoscedasticity Violations, Simultaneous Inference, Multiple Linear Regression, MLR ANOVA, Weighted Least Squares, Ridge Regression, Bootstrapping, Robust Regression, Interaction Terms, Categorical Data Analysis.
    • What I Learned: I learned various regression techniques and diagnostics, including simple and multiple linear regression, and how to handle violations in assumptions and assess model robustness.
  • Statistical Learning
    • Topics: Linear Regression, Linear Discriminant Analysis, Logistic Regression, Decision Trees, Random Forest Algorithms, Boosting.
    • What I Learned: This course covered foundational machine learning algorithms, including supervised learning methods, decision trees, and ensemble methods like random forests and boosting.
  • Probability
    • Topics: Combinatorics, Discrete Probability Distributions, Poisson Distribution, Exponential Distributions, Gamma Distributions, Normal Distributions, Joint PDFs, Random Variable Transformations, Limit Theorems.
    • What I Learned: I developed a solid understanding of probability theory, including discrete and continuous probability distributions, random variables, and the application of the central limit theorem.
  • Introductory Statistics
    • Topics: Discrete Probability Distributions, Continuous Probability Distributions, Confidence Intervals, Hypothesis Testing, ANOVA, Linear Regression.
    • What I Learned: I learned the fundamentals of statistical inference, including how to interpret confidence intervals, conduct hypothesis tests, and perform basic regression analysis.
  • Linear Algebra
    • Topics: Gaussian Elimination, Linear Combinations and Span, Vector Spaces, Linear Independence, Subspace, Basis and Dimensions, Determinants, Eigenvalues, Least Squares, Principal Component Analysis, Singular Value Decomposition.
    • What I Learned: This course provided a thorough understanding of linear algebra concepts, from solving systems of equations to eigenvalues and eigenvectors, which are fundamental to many machine learning algorithms.
  • Programming in C
    • Topics: Arrays, Pointers, Linked Lists, Stacks, Queues, Heaps, Binary Search Trees, Memory Allocation, Error Handling, Optimization, Rust, File Management.
    • What I Learned: I learned the principles of low-level programming in C, including data structures, memory management, and algorithms, which formed the foundation for my understanding of more complex programming languages.
  • Discrete Mathematics
    • Topics: Logic, Sets, Number Systems, Counting, Algorithm Analysis, Time Complexity, Graphs, Trees, Proofs, Recursion, Number Theory, Proofs, Finite State Machines, Automata, Computability.
    • What I Learned: This course introduced me to the mathematical foundations of computer science, covering logic, algorithms, and discrete structures essential for understanding computation and problem-solving.

Certifications

  • Python for Data Science and Machine Learning Bootcamp
    • What I Learned: I learned the basics of exploratory data analysis using Pandas and Numpy, data visualization using Matplotlib, Seaborn, and Plotly, and created geographic visualizations. I implemented statistical learning algorithms like Regression, KNN, SVM, Random Forests, Gradient Boosting, and Naive Bayes. I also gained an understanding of recommendation systems and deep neural networks. Finally, I learned how to use Apache Spark to handle Big Data.

Outside Coursework

  • Intermediate Microeconomics (MIT OCW)
    • What I Learned: This course covered topics in Intermediate Microeconomics such as consumer choice, income and substitution effects, optimization, dynamic programming, risk management, welfare theorems, and equilibrium, providing a strong foundation in microeconomic theory and its applications.