Data Science and Data Analytics

Students may still enroll if they missed the 1st class session. However, they need to communicate with the instructor via Canvas and catch up on all missed work prior to the 2nd class meeting.

This class is offered in an online synchronous format. Students are expected to log into this course via Canvas at the start time of scheduled meetings and participate via Zoom, for the duration of each scheduled class meeting.

To see all meeting dates, click "Full Schedule" below.

You will be granted access in Canvas to your course site and course materials approximately 24 hours prior to the published start date of the course.

Required Tools & Materials: 

Students are expected to have computers with Python 3.x, Jupyter Notebooks, and libraries: Pandas, Matplotlib and Numpy installed. Installing the Anaconda distribution of Python, gives access to Jupyter Notebooks and all the required libraries. Free Individual Edition can be obtained from: https://www.anaconda.com/products/individual

Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems; Aurélien Géron; O'Reilly Media Inc.; 2025. ISBN: 979-8341607989

Recommended Textbooks: 

Python Data Science Handbook; Jake VanderPlas; O'Reilly Media Inc.; 2023. ISBN: 9781098121228. Available at: https://jakevdp.github.io/PythonDataScienceHandbook/ 

Machine Learning with Python Cookbook; Gallatin and Albon; O'Reilly Media Inc.; 2023. ISBN: 9781098135690. Available at: https://learning.oreilly.com/library/view/machine-learning-with/9781098…;

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, O'Reilly Media, Inc., 2022-10-04, ISBN: 9781098122478.
Students can use this textbook as an alternative to the required textbook. Available at O'Reilly for Public Libraries.

This class meets simultaneously in a classroom and remotely via Zoom. Students are expected to attend and participate in the course, either in-person or remotely, during the days and times that are specified on the course schedule. Students attending remotely are also strongly encouraged to have their cameras on to get the most out of the remote learning experience. Students attending the class in-person are expected to bring a laptop to each class meeting.

No meeting on October 2, 2026 and November 27, 2026.  To see all meeting dates, click "Full Schedule" below.

You will be granted access in Canvas to your course site and course materials approximately 24 hours prior to the published start date of the course.

Required Tools & Materials: 

Students are expected to have computers with Python 3.x, Jupyter Notebooks, and libraries: Pandas, Matplotlib and Numpy installed. Installing the Anaconda distribution of Python, gives access to Jupyter Notebooks and all the required libraries. Free Individual Edition can be obtained from: https://www.anaconda.com/products/individual

Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems; Aurélien Géron; O'Reilly Media Inc.; 2025. ISBN: 979-8341607989

Recommended Textbooks: 

Python Data Science Handbook; Jake VanderPlas; O'Reilly Media Inc.; 2023. ISBN: 9781098121228. Available at: https://jakevdp.github.io/PythonDataScienceHandbook/ 

Machine Learning with Python Cookbook; Gallatin and Albon; O'Reilly Media Inc.; 2023. ISBN: 9781098135690. Available at: https://learning.oreilly.com/library/view/machine-learning-with/9781098…;

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, Aurélien Géron, O'Reilly Media, Inc., 2022-10-04, ISBN: 9781098122478.  
Students can use this textbook as an alternative to the required textbook. Available at O'Reilly for Public Libraries.