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Data Preparation, Modeling and Visualization with Python
An elective course in the Python for Data Science, Web and Core Programming Specialized Studies Program.
Course Description
Learn how to create business value by effectively importing, preparing, modeling and visualizing data using the Python programming language. Students will learn how to implement various models like linear regression, logistic regression, and decision trees; both supervised and unsupervised modeling techniques will be covered. Pandas and scikit-learn will be the primary Python packages covered in this course. Both of these packages provide power tools for those in machine learning, data science, data mining, and web data professions.
Prerequisite(s): I&C SCI X426.64 Introduction to Python Programming AND I&C SCI X426.59 Intermediate Python.
NOTE: This course may have live sessions via Zoom. A device with audio and visual will be needed to participate. The following student guide provides additional resources/information on how to use and access your courses Zoom sessions.
Instructor
Mr. Thompson is an experienced statistician and data science professional with expertise in Statistical Modeling and Inference, SAS, SQL, and Python. He has experience in government consulting, the financial services industry, and the hospitality industry. ,He was a Senior Manager with Price Waterhouse where he was a firm expert in Statistical Sampling. He holds a B.S. in Mathematics, an M.S. in Applied Statistics, and an M.B.A.
Textbook Information
Textbooks for your course may be purchased from any vendor or bookseller of your choice.
No textbooks are required for this course.
Meeting Schedule
Event Date Day Start Time End Time Location Room
START 04/17/2023 Monday --- --- Online (Access Begins) ---
OL-LEC 04/24/2023 Monday 6:00 PM 7:00 PM Zoom ---
OL-LEC 05/01/2023 Monday 6:00 PM 7:00 PM Zoom ---
OL-LEC 05/08/2023 Monday 6:00 PM 7:00 PM Zoom ---
OL-LEC 05/15/2023 Monday 6:00 PM 7:00 PM Zoom ---
OL-LEC 05/22/2023 Monday 6:00 PM 7:00 PM Zoom ---
END 06/04/2023 Sunday --- --- Online (Access Ends) ---