Understanding and Visualizing Data with Python
Master data analysis fundamentals using Python: Learn statistics, visualization, and data interpretation.
Master data analysis fundamentals using Python: Learn statistics, visualization, and data interpretation.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Statistics with Python Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Create and interpret data visualizations using Python libraries
Analyze both univariate and multivariate data effectively
Apply statistical concepts to real-world data analysis
Understand different sampling methods and their implications
Communicate statistical findings to diverse audiences
Skills you'll gain
This course includes:
5.9 Hours PreRecorded video
9 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course introduces learners to fundamental statistical concepts and data visualization techniques using Python. Starting with basic data types and study design, students progress through univariate and multivariate analysis, learning to create and interpret various visualizations using Python libraries. The course covers probability and non-probability sampling, population inference, and practical applications of statistical concepts. Hands-on exercises in Jupyter Notebook environment help students master Python tools for data analysis and visualization.
Introduction to Data
Module 1 · 4 Hours to complete
Univariate Data
Module 2 · 4 Hours to complete
Multivariate Data
Module 3 · 4 Hours to complete
Populations and Samples
Module 4 · 5 Hours to complete
Fee Structure
Instructors
Research Leader in Survey Methodology
Brady T. West serves as a Research Associate Professor in the Survey Methodology Program at the University of Michigan’s Survey Research Center, part of the Institute for Social Research. He earned his PhD in Survey Methodology from Michigan in 2011, following an MA in Applied Statistics in 2002 and a BS in Statistics with Highest Honors in 2001, both from the same institution. His research focuses on the implications of measurement error in auxiliary variables and survey paradata for survey estimation, along with survey nonresponse, interviewer effects, and multilevel regression models for clustered and longitudinal data. West is the lead author of Linear Mixed Models: A Practical Guide using Statistical Software, Second Edition (2014, Chapman Hall/CRC Press) and co-author of Applied Survey Data Analysis (2017, Chapman Hill) with Steven Heeringa and Pat Berglund. Residing in Dexter, MI, he enjoys family life with his wife, Laura, their children, Carter and Everleigh, and their American Cocker Spaniel, Bailey.
Advocate for Statistical Education
Brenda Gunderson is a Senior Lecturer at the University of Michigan, where she received her PhD in Statistics in 1989. She coordinates and teaches the largest undergraduate statistics course, Statistics and Data Analysis, which accommodates approximately 1,800 students each term. In addition to her teaching role, Brenda serves as an undergraduate advisor for students pursuing a major or minor in Statistics. Her research focuses on enhancing statistical education, particularly through the integration of technology to improve teaching and learning outcomes.
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4.7 course rating
2,631 ratings
Frequently asked questions
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