In this course, we will explore basic principles behind using data for estimation and for assessing theories. We will analyze both categorical data and quantitative data, starting with one population techniques and expanding to handle comparisons of two populations. We will learn how to construct confidence intervals. We will also use sample data to assess whether or not a theory about the value of a parameter is consistent with the data. A major focus will be on interpreting inferential results appropriately.
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课程信息
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学生职业成果
14%
通过此课程获得实实在在的工作福利
20%
加薪或升职
可灵活调整截止日期
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第 2 门课程（共 3 门）
中级
High school algebra, successful completion of Course 1 in this specialization or equivalent background
完成时间大约为19 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 中文（简体）, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 土耳其语（Turkish）, 英语（English）, 西班牙语（Spanish）
您将学到的内容有
Determine assumptions needed to calculate confidence intervals for their respective population parameters.
Create confidence intervals in Python and interpret the results.
Review how inferential procedures are applied and interpreted step by step when analyzing real data.
Run hypothesis tests in Python and interpret the results.
您将获得的技能
Confidence IntervalPython ProgrammingStatistical InferenceStatistical Hypothesis Testing
学生职业成果
14%
通过此课程获得实实在在的工作福利
20%
加薪或升职
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 3 门）
中级
High school algebra, successful completion of Course 1 in this specialization or equivalent background
完成时间大约为19 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 中文（简体）, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 土耳其语（Turkish）, 英语（English）, 西班牙语（Spanish）
提供方
密歇根大学
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.
教学大纲 - 您将从这门课程中学到什么
完成时间为 3 小时
WEEK 1 - OVERVIEW & INFERENCE PROCEDURES
In this first week, we’ll review the course syllabus and discover the various concepts and objectives to be mastered in weeks to come. You’ll be introduced to inference methods and some of the research questions we’ll discuss in the course, as well as an overall framework for making decisions using data, considerations for how you make those decisions, and evaluating errors that you may have made.
完成时间为 3 小时
9 个视频 （总计 67 分钟）, 5 个阅读材料, 1 个测验
完成时间为 7 小时
WEEK 2 - CONFIDENCE INTERVALS
In this second week, we will learn about estimating population parameters via confidence intervals. You will be introduced to five different types of population parameters, assumptions needed to calculate a confidence interval for each of these five parameters, and how to calculate confidence intervals. Quizzes will appear throughout the week to test your understanding. In addition, you’ll learn how to create confidence intervals in Python.
完成时间为 7 小时
12 个视频 （总计 118 分钟）, 5 个阅读材料, 3 个测验
完成时间为 7 小时
WEEK 3 - HYPOTHESIS TESTING
In week three, we’ll learn how to test various hypotheses - using the five different analysis methods covered in the previous week. We’ll discuss the importance of various factors and assumptions with hypothesis testing and learn to interpret our results. We will also review how to distinguish which procedure is appropriate for the research question at hand. Quizzes and a peer assessment will appear throughout the week to test your understanding.
完成时间为 7 小时
12 个视频 （总计 138 分钟）, 4 个阅读材料, 3 个测验
完成时间为 2 小时
WEEK 4 - LEARNER APPLICATION
In the final week of this course, we will walk through several examples and case studies that illustrate applications of the inferential procedures discussed in prior weeks. Learners will see examples of well-formulated research questions related to the study designs and data sets that we have discussed thus far, and via both confidence interval estimation and formal hypothesis testing, we will formulate inferential responses to those questions.
完成时间为 2 小时
6 个视频 （总计 77 分钟）, 4 个阅读材料, 1 个测验
审阅
来自INFERENTIAL STATISTICAL ANALYSIS WITH PYTHON的热门评论
由 RZ 提供Apr 1, 2020
This is a very great course. Statistics by itself is a very powerful tool for solving real world problems. Combine it with the knowledge of Python, there no limit to what you can achieve.
由 RS 提供Jan 21, 2021
Very good course content and mentors & teachers. The course content was very structured. I learnt a lot from the course and gained skills which will definitely gonna help me in future.
由 AA 提供May 27, 2020
The best part of this that it is designed in a way that it encourages people to dig deeper and explore more. The instructors have done a great job in making the curriculam this good.
由 SS 提供Mar 19, 2020
Great Course. There are so many example to understand the topic. I really enjoyed every lesson of this specialization. I am going forward for the next one.
关于 Statistics with Python 专项课程
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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