Acquired new machine learning skills, from data preprocessing and standardization to feature engineering, and Git command line languages, enabling data scientists to work on branches and remote repos.
Delve deeper into data science in Python, mastering data manipulation, exploration, and visualization skills. This track also featured sampling techniques, hypothesis testing, and an introduction to machine learning, providing skills needed to succeed as a data scientist!
Developed data analytics skills in Python, including how to manipulate, analyze, and visualize data.
Explored data analytics in SQL. Learned how to write PostgreSQL queries, create subqueries, join, clean, and extract information from database tables.
Networks (Distinction)
Machine Learning (Distinction)
Category Theory (Distinction)
Entropy Decay in Markov Chain (Distinction)
Topics in Computational Geometry (Distinction)
Complex Analysis with Applications (Distinction)
Graphs and Matroids (Very Good Pass)
ALGEBRA (A)
TRIGONOMETRY (A)
COORDINATE GEOMETRY I (A)
CALCULUS (A)
INTRODUCTORY STATISTICS (A)
ABSTRACT ALGEBRA (A)
REAL ANALYSIS I (A)
VECTOR ANALYSIS (A)
MATHEMATICAL METHODS I (A)
PROBABILITY THEORY AND DISTRIBUTIONS (A)
LINEAR ALGEBRA (A)
DYNAMICS OF PARTICLES (A)
COMPLEX ANALYSIS I (A)
MATHEMATICAL METHODS II (A)
THEORY OF MODULES (A)
DYNAMICS OF A RIGID BODY (A)
HYDRODYNAMICS (A)
PROBABILITY DISTRIBUTION THEORY (A)
VECTOR AND TENSOR ANALYSIS (A)
OPERATIONS RESEARCH (A)
ADVANCED ALGEBRA I (A)
COMPLEX ANALYSIS II (A)
OPTIMIZATION THEORY (A)
VECTOR AND DYNAMICS (A)
DIFFERENTIAL EQUATIONS (A)
COORDINATE GEOMETRY II (B)
REAL ANALYSIS II (B)
NUMERICAL ANALYSIS I (B)
REAL ANALYSIS IV (B)
ADVANCED ALGEBRA II (B)
GENERAL TOPOLOGY (B)
PARTIAL DIFFERENTIAL EQUATIONS (B)