Understanding and Visualizing Data with Python

О курсе

In this course, learners will be introduced to the field of statistics, including where data come from, study design, data management, and exploring and visualizing data. Learners will identify different types of data, and learn how to visualize, analyze, and interpret summaries for both univariate and multivariate data. Learners will also be introduced to the differences between probability and non-probability sampling from larger populations, the idea of how sample estimates vary, and how inferences can be made about larger populations based on probability sampling. At the end of each week, learners will apply the statistical concepts they’ve learned using Python within the course environment. During these lab-based sessions, learners will discover the different uses of Python as a tool, including the Numpy, Pandas, Statsmodels, Matplotlib, and Seaborn libraries. Tutorial videos are provided to walk learners through the creation of visualizations and data management, all within Python. This course utilizes the Jupyter Notebook environment within Coursera.

Навыки, которые вы освоите

Учитесь у экспертов

Brenda Gunderson

Lecturer IV Emerita in Statistics

Brady T. West

Collegiate Research Professor, Faculty Associate, Population Studies Center, Research Professor, Survey Research Center, Institute for Social Research, Adjunct Lecturer in Quantitative Methods and Social Sciences Program, College of Literature, Science, and the Arts and Research Professor, Biostatistics, School of Public Health

Kerby Shedden

Associate Chair and Professor of Statistics