- Platform
- Coursera
- Provider
- University of Michigan
- Effort
- 4-6 hours a week
- Length
- 4 weeks
- Language
- English
- Credentials
- Paid Certificate Available
- Part of
- Course Link
Overview
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.
WHAT YOU WILL LEARN
Taught by
Brenda Gunderson, Brady T. West and Kerby Shedden
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.
WHAT YOU WILL LEARN
- Properly identify various data types and understand the different uses for each
- Create data visualizations and numerical summaries with Python
- Communicate statistical ideas clearly and concisely to a broad audience
- Identify appropriate analytic techniques for probability and non-probability samples
Syllabus
WEEK 1 - INTRODUCTION TO DATA
WEEK 2 - UNIVARIATE DATA
WEEK 3 - MULTIVARIATE DATA
WEEK 4 - POPULATIONS AND SAMPLES
WEEK 1 - INTRODUCTION TO DATA
WEEK 2 - UNIVARIATE DATA
WEEK 3 - MULTIVARIATE DATA
WEEK 4 - POPULATIONS AND SAMPLES
Taught by
Brenda Gunderson, Brady T. West and Kerby Shedden