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.
Этот курс входит в специализацию ''Специализация Statistics with Python'
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Специализация Statistics with PythonМичиганский университетОб этом курсе
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Курс 2 из 3 в программе
Средний уровень
High school algebra, successful completion of Course 1 in this specialization or equivalent background
Прибл. 19 часов на выполнение
Английский
Субтитры: Арабский, Французский, Португальский (Европа), Китайский (упрощенное письмо), Итальянский, Вьетнамский, Корейский, Немецкий, Русский, Турецкий, Английский, Испанский
Чему вы научитесь
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.
Приобретаемые навыки
- Confidence Interval
- Python Programming
- Statistical Inference
- Statistical Hypothesis Testing
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Сертификат, ссылками на который можно делиться с другими людьми
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100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 2 из 3 в программе
Средний уровень
High school algebra, successful completion of Course 1 in this specialization or equivalent background
Прибл. 19 часов на выполнение
Английский
Субтитры: Арабский, Французский, Португальский (Европа), Китайский (упрощенное письмо), Итальянский, Вьетнамский, Корейский, Немецкий, Русский, Турецкий, Английский, Испанский
от партнера
Мичиганский университет
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Программа курса: что вы изучите
3 ч. на завершение
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.
3 ч. на завершение
9 видео ((всего 67 мин.)), 6 материалов для самостоятельного изучения, 1 тест
7 ч. на завершение
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.
7 ч. на завершение
12 видео ((всего 118 мин.)), 5 материалов для самостоятельного изучения, 3 тестов
7 ч. на завершение
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.
7 ч. на завершение
12 видео ((всего 138 мин.)), 4 материалов для самостоятельного изучения, 3 тестов
2 ч. на завершение
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.
2 ч. на завершение
6 видео ((всего 77 мин.)), 4 материалов для самостоятельного изучения, 1 тест
Рецензии
- 5 stars73,98 %
- 4 stars17,42 %
- 3 stars5,52 %
- 2 stars1,59 %
- 1 star1,47 %
Лучшие отзывы о курсе INFERENTIAL STATISTICAL ANALYSIS WITH PYTHON
от партнера RR6 мар. 2019 г.
If you are interested in statistics and statistical analysis, this course gets you grounded in the essential aspects of statistics. Excellent instructors.
от партнера AB5 нояб. 2020 г.
Great course with practical experience with Python. There are many courses that teach statistics with R but this is the first one to do so in Python.
от партнера AA27 мая 2020 г.
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.
от партнера RS21 янв. 2021 г.
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.
Специализация Statistics with Python: общие сведения
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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