Coursework
Spring 2024
Scope:
Course notes can be accessed here.
Pandas [Series, DataFrame; slicing, grouping, aggregating];
data cleaning and EDA [granularity, scope, and temporality];
RegEx;
visualization [Matplotlib, Seaborn], kernel density estimate;
sampling [convenience/probability/random sample, chance error; selection/response/non-response bias];
modeling [bias-variance tradeoff, complexity], loss functions [MAE, MSE], OLS;
feature engineering [one-hot encoding, polynomial features];
sklearn, gradient descent [learning rate, dimensionality, batch size], PyTorch [Stochastic GD];
cross-validation, regularization;
random variables [expectation, variance], distribution [Bernoulli, Binomial, Uniform, Gaussian];
SQL;
logistic regression, cross-entropy loss [decision rule, threshold];
linear separability, confusion matrix [accuracy, precision, recall], ROC curve [AUC];
PCA [singular value decomposition, data variance and centering];
clustering [K-Means; hierarchical with single/average/complete link], inertia, silhouette score.
Course notes can be accessed here.
Scope:
list, array, SLL, doubly linked list,
inheritance, casting, HOF, comparators;
B-tree, red-black tree;
hashing;
priority queue, stack, min-max heap;
graph, tree traversals [DFS, BFS];
Dijkstra's algorithm for shortest path, Kruskal's and Prim's algorithm for MST, A * search;
DAG; refix operations, tries;
selesction sort, heapsort, mergesort, insertion sort, quicksort;
radix sort;
asymptotics, time and space complexity, compression.
Largely based on projects on Java. Worked in IntelliJ.
Largely based on projects on Java. Worked in IntelliJ.
Scope:
proofs [induction], propositional logic;
stable matching;
modular arithmetic, Chinese Remainder Theorem;
RSA, Fermat's Little Theorem;
polynomials, Lagrange interpolation, error-correcting code;
graphs, Eulerian, Hamiltonian tour/walk;
counting, countability;
random variables [Uniform, Bernoulli, Binomial, Gaussian, Poisson, Geometric];
expectation, variance, PDF, CDF;
sampling, confidence interval;
continuous, joint probability;
continous probability, Markov's, Chebyshev's inequality;
Markov chains.
Scope:
matrix operations, determinants;
vector spaces, inner product spaces;
eigenvalues and eigenvectors;
linear transformations, symmetric matrices;
linear ordinary differential equations (ODE), systems of linear ODE;
Fourier series.
Fall 2023
Scope:
visualization [histograms];
sampling, distributions [normal, skewed], Chebyshev's Inequality, Central Limit Theorem;
inference, causality, confounding factors;
hypothesis testing [null/alternative, test statistic, p-value cutoff], A/B testing;
bootstrapping, confidence intervals;
linear regression, Least Squares;
Nearest Neighbors.
Worked with
The course textbook can be accessed here.
Worked with
datascience library based on Pandas.
The course textbook can be accessed here.
Scope:
data abstraction using interfaces, objects, classes, and generic operators;
recursion, higher-order functions, generators, streams, currying;
interpreters, macros.
Wrote a Scheme interpreter based on Python as part of the course.
Wrote a Scheme interpreter based on Python as part of the course.
Scope:
• Statistical methods: Introduction to probabilities, weighted averages, correlations, data visualization, cross-tabulation, t-hypothesis tests, ANOVA tests, confidence intervals, A/B causality tests, chi-squared tests, and linear regression with non-linear variables, discontinuity, and seasonality.
• Business functions: Explore sales, market research, fundraising, customer service, corporate training, process improvement, online marketing, travel operations, procurement, financial planning & analysis, staffing, and other business functions.
Worked with Python and SQL. Largely based on labs with case studies.
• Statistical methods: Introduction to probabilities, weighted averages, correlations, data visualization, cross-tabulation, t-hypothesis tests, ANOVA tests, confidence intervals, A/B causality tests, chi-squared tests, and linear regression with non-linear variables, discontinuity, and seasonality.
• Business functions: Explore sales, market research, fundraising, customer service, corporate training, process improvement, online marketing, travel operations, procurement, financial planning & analysis, staffing, and other business functions.
Worked with Python and SQL. Largely based on labs with case studies.