Courses

DA100

Foundations of Business Intelligence Analytics

This course introduces the fundamental concepts and techniques of business analytics. Students will learn how to extract insights from data to support decision-making processes in various business domains. Topics include data exploration, descriptive analytics, and basic statistical analysis.

DA110

Introduction to Programming with Python

This course provides a comprehensive introduction to programming using Python. Students will learn the basics of Python syntax, data structures, control flow, functions, and object-oriented programming. Emphasis is placed on problem-solving skills and developing practical programming applications.

DA111

Introduction to Database Programming with SQL

This course covers the fundamentals of relational databases and SQL (Structured Query Language). Students will learn how to create, manipulate, and query databases using SQL. Topics include database design, normalization, and SQL statements for data manipulation and retrieval.

DA120

Discrete Mathematics for Programming

This course introduces the mathematical concepts and techniques essential for computer programming and algorithm analysis. Topics include logic, sets, combinatorics, graphs, trees, and discrete probability. Emphasis is placed on developing analytical and problem-solving skills.

DA190

Communication Skills for Data Analysts

This course focuses on developing effective communication skills for data analysts. Students will learn how to present data-driven insights clearly and persuasively through various mediums, such as reports, presentations, and visualizations. Emphasis is placed on storytelling with data and communicating technical information to non-technical audiences.

DA200

Advanced Business Intelligence Analytics

Building upon the foundations of business analytics, this course covers advanced techniques for extracting insights from data to support decisions for more complex business programs. Students will continue to explore the applications of these techniques across various business domains.

Prereq: DA100

DA210

Introduction to Data Analysis with Python

This course will introduce students to Python libraries such as NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization, enabling them to extract insights from complex datasets through a programmatic approach.

Prereq: DA110, DA111

DA220

Data Structures and Algorithms

This course introduces fundamental data structures and algorithms, including lists, trees, graphs, sorting, and searching algorithms, as well as their applications in data analysis and problem-solving.

Prereq: DA120

DS221

Probability and Statistics for Data Analytics

Students will study probability theory, descriptive and inferential statistics, hypothesis testing, and regression analysis, which are essential for understanding and interpreting data in various domains.

Prereq: DA120

DS290

Data Visualization and Storytelling

This course covers techniques for creating effective data visualizations and communicating data-driven insights through compelling narratives, enabling students to present complex information in a clear and impactful manner.

Prereq: DA190

DS310

Introduction to Machine Learning with Python

Students will learn the end-to-end process of developing machine learning models using Python and Scikit-Learn. Topics that will be covered include supervised and unsupervised learning algorithms, model training techniques, model evaluation, and deployment.

Prereq: DA210

Coreq: DA320

DS311

Introduction to Deep Learning with Python

This course introduces deep learning concepts, architectures, and libraries such as TensorFlow and PyTorch, enabling students to build and train neural networks for various applications.

Coreq: DA310, DA320

DS312

Prompt Engineering for LLMs

Students will learn techniques for iteratively designing and testing effective prompts for large language models (LLMs), enabling them to leverage the power of these models for various natural language processing tasks.

DS320

Mathematics for Machine Learning

This course covers essential mathematical concepts for machine learning, including linear algebra, calculus, optimization, and probability theory, providing a solid foundation for understanding and developing machine learning models.

Prereq: DA221

DS330

Ethics in Data Science

This course explores ethical considerations in data science, including privacy, bias, fairness, and transparency. Students will examine case studies and ethical frameworks, and develop critical thinking skills for ethical decision-making in data science.