A Curriculum Designed for the Real World
Our 30-credit curriculum is designed to provide you with the technical and practical skills necessary to succeed in data science. Through a personalized learning experience, you will work with faculty one-on-one as well as participate in various team projects.
Courses include:
- Python Programming for Data Science: Learn the foundations of Python and data wrangling to become proficient in analyzing and manipulating data.
- Probability and Statistics for Data Science: Become proficient in foundational statistical methods and approaches crucial for analyzing data in real-world contexts.
- Database Management: Develop expertise in querying databases using SQL, a must-have skill in data science.
- Data Analytics: Concepts and Techniques: Gain insight into data analysis methodologies and techniques.
- Big Data Analytics: Learn how to handle and analyze vast amounts of data using cutting-edge tools and platforms such as Hadoop and Spark.
- Applications for Data Science: Work on real-world interdisciplinary projects to apply data science skills and gain hands-on experience.
- Ethics and Bias in AI: Understand the ethical considerations of AI development and learn how to create transparent and interpretable AI systems.
- Advanced Techniques:
- Ensemble Modeling: Develop robust models by combining multiple algorithms to enhance prediction accuracy.
- Deep Learning: Explore neural networks and deep learning architectures to solve complex problems such as image recognition and natural language understanding.
- Natural Language Processing (NLP): Learn the techniques behind modern NLP applications, from sentiment analysis to text generation.
- Computer Vision: Explore computer vision techniques, focusing on object detection, segmentation, and real-world applications in fields like autonomous vehicles and manufacturing.
- Generative AI & Large Language Models (LLM) Using Python: Explore the rapidly growing field of generative AI and learn how to build and implement LLMs using Python.
- Low-Code/No-Code Machine Learning: Gain hands-on experience with tools for creating ML models without extensive coding.
