Python Statistics for Business Analysis
Master fundamental statistical concepts and Python programming for data-driven business analysis in this comprehensive 8-week course.
Master fundamental statistical concepts and Python programming for data-driven business analysis in this comprehensive 8-week course.
This foundational course combines statistics essentials with practical Python programming skills for business analytics. Students learn key statistical concepts including descriptive statistics, probability, inference, correlation, and regression while gaining hands-on experience implementing these techniques using Python. The course provides a solid foundation for data-driven decision making in business contexts, teaching both theoretical understanding and practical application.
Instructors:
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What you'll learn
Calculate descriptive statistics and create visualizations using Python
Apply probability principles and derive probability function measures
Understand and communicate statistical uncertainty
Perform regression analysis effectively
Differentiate between correlation and causation
Implement statistical concepts using Python programming
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
This course provides a comprehensive introduction to statistical analysis using Python programming. The curriculum covers essential statistical concepts necessary for data-driven business analysis, including descriptive statistics, probability theory, statistical inference, correlation analysis, and regression modeling. Students learn to implement these concepts using Python, gaining practical experience in data analysis and visualization while understanding the fundamental principles of statistics.
Fee Structure
Instructor
2 Courses
Expert in Business Analytics and Cloud Computing Technologies
Katie Gaertner serves as a Business Analytics Lecturer in the Department of Risk and Insurance at the University of Wisconsin-Madison's School of Business, where she brings extensive real-world experience to the classroom. With a diverse academic background including degrees in Statistics from Brigham Young University, Public Administration from the University of Arizona, and Data Science from Northwestern University, she combines theoretical knowledge with practical expertise. Her professional journey spans both public and private sectors, having served as an analyst for three Utah governors and most recently as a Machine Learning Engineer leading cloud data strategy in the financial industry. At UW-Madison, she teaches Data Visualization and Cloud Technologies in the Master of Science-Business: Data, Insights, and Analytics program, where she prepares students for real-world analytics challenges using Amazon Web Services platforms. Her teaching philosophy emphasizes hands-on experience with cloud data tools, including data lakes, scalable data warehouses, and machine learning services, ensuring students develop competitive skills for today's data-driven job market
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