Data Science
From data analysis to machine learning, these are the classes I've taken in the Data Science program at UCSD.
DSC 80: Practice of Data Science
In this course, students master the data science life-cycle and learn many of the fundamental principles and techniques of data science spanning algorithms, statistics, machine learning, visualization, and data systems
Key Topics
- Data cleaning and analysis
- Web scraping and APIs
- Statistical inference, advanced modeling, and NLP
DSC 40B: Theoretical Foundations of Data Science II
This course covers graph theory, probability, and continuous and discrete algorithms with applications to data science and machine learning.
Key Topics
- Time Complexity Analysis
- Data Structures and Algorithms
- Graph Theory
DSC 40A: Theoretical Foundations of Data Science I
DSC 40A covers the mathematical foundations of data science, including machine learning, statistics, and linear algebra with applications to data science.
Key Topics
- Multivariable Calculus & Linear Algebra for Machine Learning
- Linear Regression
- Probability, Combinatorics, and Classification
DSC 30: Data Structures and Algorithms
A comprehensive course on data structures and algorithms with applications to data science and software engineering.
Key Topics
- Object-Oriented Programming
- Data Structures (Lists, Trees, Graphs, Heaps)
- Algorithm Analysis and Design (Greedy, Divide and Conquer, Dynamic Programming, BFS, DFS)
DSC 20: Programming and Data Structures
Introduction to object-oriented programming and data structures using Python, with emphasis on data science applications.
Key Topics
- Object-Oriented Programming
- Basic Data Structures (Lists, Dictionaries, Sets)
- File I/O and Data Processing
DSC 10: Principles of Data Science
Introduction to data science concepts and Python programming with focus on data analysis and visualization.
Key Topics
- Python Programming Fundamentals and Pandas
- Data Visualization with Matplotlib
- Statistical Analysis and Hypothesis Testing