The Elements of Economic Analysis (Sequence):
Economic modeling, consumer theory, welfare analysis, taxation impact evaluation,
and macroeconomic theory including aggregate demand/supply and growth models.
Econometrics: Regression analysis, heteroscedasticity corrections,
limited dependent variable models, and hands-on implementation using R programming.
Financial Econometrics: Time-varying expected returns, volatility prediction,
factor models, high-frequency data analysis, and statistical modeling of financial markets.
Big Data Tools in Economics: Applied microeconomic modeling with large datasets,
identification and inference with high-dimensional data, neural networks with TensorFlow,
event studies and tratment evaluation using pandas.
Computer Science
Mathematical Foundations of Machine Learning: Matrix methods,
statistical models, optimization algorithms, classification, clustering, regression,
regularization, SVMs, neural networks, and deep learning applications.
Systems Programming: C programming, bit-level operations,
memory management, machine language, cache systems, exceptions, and operating system interfaces
through complex low-level programming projects.
Theory of Algorithms: Algorithm design and analysis with provable correctness
and runtime guarantees, including FFT, shortest paths, greedy algorithms, divide and conquer,
dynamic programming, network flow, linear programming, and NP-completeness.
Introduction to Database Systems: Database design, SQL programming,
core DBMS components (transactions, recovery, query processing), distributed databases,
and data analytics systems.