Data Science

Extend single-variable calculus into 3D with partial derivatives, gradients, and multiple integrals.

Learn how to organize data and write step-by-step procedures that make programs run fast.

Master derivatives, integrals, and infinite series — the math behind rates of change and accumulation.

Use Python libraries like pandas, NumPy, and scikit-learn to clean and analyze real datasets.

Work with matrices, vectors, and systems of equations used in graphics, AI, and engineering.

Study randomness, distributions, and statistical tests used to make data-driven decisions.

Learn the full data pipeline — collecting, cleaning, analyzing, and presenting data to answer questions.

Design databases, write queries, and learn how large-scale data storage actually works.

Draw conclusions about populations from sample data using confidence intervals and hypothesis tests.

Create clear charts, dashboards, and interactive graphics that reveal patterns in complex datasets.

Train algorithms to recognize patterns and make predictions from data without explicit programming.

Process massive datasets using distributed computing tools like Spark, Hadoop, and cloud platforms.

Teach computers to understand, generate, and analyze human language and text.

Examine bias, privacy, and fairness issues that arise when algorithms affect people's lives.