Machine Learning & Data Science Foundations

Practice the necessary mathematical background for further understanding in machine learning. You will study topics like probability (random variables, modeling with continuous and discrete distributions), linear algebra (inner product spaces, linear operators), and multivariate differential calculus (partial derivatives, matrix differentials). Some coding will be required; ultimately, you will learn how to translate these foundational math skills into concrete coding programs.

Practice the necessary computational background for further understanding in machine learning. You will study topics like computational complexity, analysis of algorithms, proof techniques, optimization, dynamic programming, recursion, and data structures. Some coding will be required; ultimately, you will learn how to translate these computational concepts into concrete coding programs.

Master the concepts, techniques, skills, and tools needed for developing programs in Python. You will study topics like types, variables, functions, iteration, conditionals, data structures, classes, objects, modules, and I/O operations while also receiving hands-on experience with development environments like Jupyter Notebook and software development practices like test-driven development, debugging, and style. Course projects include real-life applications on enterprise data and document manipulation, web scraping, and data analysis. These courses can be waived for computer science professionals already fluent in Python.

Learn foundational concepts related to the three core areas of data science: computing systems, analytics, and human-centered data science. In this course, you will acquire skills in solution design (e.g. architecture, framework APIs, cloud computing), analytic algorithms (e.g., classification, clustering, ranking, prediction), interactive analysis (Jupyter Notebook), applications to data science domains (e.g. natural language processing, computer vision), and visualization techniques for data analysis, solution optimization, and performance measurement on real-world tasks.

Students are required to complete 36 units of coursework to earn the Machine Learning & Data Science Certificate. However, they may waive up to 12 units of coursework upon successful completion of exemption exam(s): 

Math Waiver Exams (one exam for two courses) 

• Math Fundamentals of Machine Learning (6 units) 
• Computational Fundamentals of Machine Learning (6 units) 

Python Waiver Exams (one exam for two courses) 

• Python for Data Science I (6 units) 
• Python for Data Science II (6 units) 

If a student passes waiver exams for more than 12 units of coursework, an alternative course is available to replace the waived units. Should this happen, an advisor will be in touch to outline a new curriculum plan. Waived courses do not carry academic credit, and no tuition will be charged for them. Any scholarships awarded to the waived courses will be forfeited.  

Exemption Exam Process 

Students indicate their interest in taking the exam(s) within their program application. Exemption exams are available only to students who have submitted their enrollment deposit. After the enrollment deposit is received, additional details about scheduling and completing the exam(s) will be provided. 

Note - not all courses are available every semester. If you waive a course, it may require you to defer your enrollment to a future term when the next course is available.