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.
Informações sobre o curso
High school algebra
Habilidades que você terá
High school algebra
Universidade de Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
- 5 stars75,95%
- 4 stars18,58%
- 3 stars3,70%
- 2 stars0,82%
- 1 star0,91%
Principais avaliações do UNDERSTANDING AND VISUALIZING DATA WITH PYTHON
The course itself was good. I think I learnt the specific concepts well. However, I would have to go through the general sense of python to actually implement what I learnt here
This was a quick way of understanding the basics. I liked how detailed and basic the learning instructions were. Anyone, even those without a statistics background can begin from here
20 studying hours that helps me getting back to speed on manipulating the quantitative data in Pandas with different query conditions, powerful statistics and Sampling Distributions.
Excellent introductory course to statistics. Great use of NHANES dataset to demonstrate techniques on real dataset. I would appreciate a more demanding project at the course end.
Sobre Programa de cursos integrados Statistics with Python
This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical modeling procedures. Finally, they will learn the importance of and be able to connect research questions to the statistical and data analysis methods taught to them.
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