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.
Этот курс входит в специализацию ''Специализация Statistics with Python'
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Специализация Statistics with PythonМичиганский университетОб этом курсе
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Сертификат, ссылками на который можно делиться с другими людьми
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100% онлайн
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Курс 1 из 3 в программе
Начальный уровень
High school algebra
Прибл. 21 час на выполнение
Английский
Субтитры: Арабский, Французский, Португальский (Европа), Итальянский, Вьетнамский, Корейский, Немецкий, Русский, Английский, Испанский
Чему вы научитесь
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
Приобретаемые навыки
- Statistics
- Data Analysis
- Python Programming
- Data Visualization (DataViz)
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 1 из 3 в программе
Начальный уровень
High school algebra
Прибл. 21 час на выполнение
Английский
Субтитры: Арабский, Французский, Португальский (Европа), Итальянский, Вьетнамский, Корейский, Немецкий, Русский, Английский, Испанский
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Мичиганский университет
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.
Программа курса: что вы изучите
5 ч. на завершение
WEEK 1 - INTRODUCTION TO DATA
In the first week of the course, we will review a course outline and discover the various concepts and objectives to be mastered in the weeks to come. You will get an introduction to the field of statistics and explore a variety of perspectives the field has to offer. We will identify numerous types of data that exist and observe where they can be found in everyday life. You will delve into basic Python functionality, along with an introduction to Jupyter Notebook. All of the course information on grading, prerequisites, and expectations are on the course syllabus and you can find more information on our Course Resources page.
5 ч. на завершение
11 видео ((всего 114 мин.)), 8 материалов для самостоятельного изучения, 2 тестов
5 ч. на завершение
WEEK 2 - UNIVARIATE DATA
In the second week of this course, we will be looking at graphical and numerical interpretations for one variable (univariate data). In particular, we will be creating and analyzing histograms, box plots, and numerical summaries of our data in order to give a basis of analysis for quantitative data and bar charts and pie charts for categorical data. A few key interpretations will be made about our numerical summaries such as mean, IQR, and standard deviation. An assessment is included at the end of the week concerning numerical summaries and interpretations of these summaries.
5 ч. на завершение
8 видео ((всего 92 мин.)), 2 материалов для самостоятельного изучения, 3 тестов
5 ч. на завершение
WEEK 3 - MULTIVARIATE DATA
In the third week of this course on looking at data, we’ll introduce key ideas for examining research questions that require looking at more than one variable. In particular, we will consider both numerically and visually how different variables interact, how summaries can appear deceiving if you don’t properly account for interactions, and differences between quantitative and categorical variables. This week’s assignment will consist of a writing assignment along with reviewing those of your peers.
5 ч. на завершение
7 видео ((всего 56 мин.)), 3 материалов для самостоятельного изучения, 3 тестов
6 ч. на завершение
WEEK 4 - POPULATIONS AND SAMPLES
In this week, you’ll spend more time thinking about where data come from. The highest-quality statistical analyses of data will always incorporate information about the process used to generate the data, or features of the data collection design. You’ll be exposed to important concepts related to sampling from larger populations, including probability and non-probability sampling, and how we can make inferences about larger populations based on well-designed samples. You’ll also learn about the concept of a sampling distribution, and how estimation of the variance of that distribution plays a critical role in making statements about populations. Finally, you’ll learn about the importance of reading the documentation for a given data set; a key step in looking at data is also looking at the available documentation for that data set, which describes how the data were generated.
6 ч. на завершение
15 видео ((всего 223 мин.)), 8 материалов для самостоятельного изучения, 2 тестов
Рецензии
Лучшие отзывы о курсе UNDERSTANDING AND VISUALIZING DATA WITH PYTHON
от партнера AT21 мая 2020 г.
Excellent course materials, especially the videos, with content that is thoughtfully composed and carefully edited. Very good python training, great instructors, and overall great learning experience.
от партнера PR3 сент. 2020 г.
Very helpful course for newcomer in data science studies. Great in clearing fundamentals for descriptive statistics, use of python to get these insights,plotting. Overall provide good learning curve.
от партнера VV2 авг. 2020 г.
Great course to learn the basics! The supplementary material in Jupyter notebooks is extremely valuable. Really appreciate the PhD students who took the time to explain even the simplest of codes :)
от партнера MC19 апр. 2020 г.
Excellent high level introduction, would have like the assessment to be more challenging. The additional materials are just amazing for most of them. The notebooks to practice are also excellent.
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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