Об этом курсе
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Курс 1 из 3 в программе

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Начальный уровень

High school algebra

Прибл. 21 часа на выполнение

Предполагаемая нагрузка: 4 weeks of study, 4-6 hours/week...

Английский

Субтитры: Английский, Корейский

Чему вы научитесь

  • Check

    Properly identify various data types and understand the different uses for each

  • Check

    Create data visualizations and numerical summaries with Python

  • Check

    Communicate statistical ideas clearly and concisely to a broad audience

  • Check

    Identify appropriate analytic techniques for probability and non-probability samples

Приобретаемые навыки

StatisticsData AnalysisPython ProgrammingData Visualization (DataViz)

Курс 1 из 3 в программе

100% онлайн

Начните сейчас и учитесь по собственному графику.

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.

Начальный уровень

High school algebra

Прибл. 21 часа на выполнение

Предполагаемая нагрузка: 4 weeks of study, 4-6 hours/week...

Английский

Субтитры: Английский, Корейский

Программа курса: что вы изучите

Неделя
1
4 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.

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10 видео ((всего 110 мин.)), 8 материалов для самостоятельного изучения, 2 тестов
10 видео
(Cool Stuff in) Data8мин
Where Do Data Come From?12мин
Variable Types5мин
Study Design6мин
Introduction to Jupyter Notebooks9мин
Data Types in Python12мин
Introduction to Libraries and Data Management13мин
8 материала для самостоятельного изучения
Course Syllabus5мин
Meet the Course Team!10мин
About Our Datasets2мин
Help Us Learn More About You!10мин
Resource: This is Statistics10мин
Course Syllabus5мин
Let's Play with Data!10мин
Data management and manipulation10мин
2 практического упражнения
Practice Quiz - Variable Types10мин
Assessment: Different Data Types10мин
Неделя
2
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.

...
8 видео ((всего 92 мин.)), 2 материалов для самостоятельного изучения, 3 тестов
8 видео
Standard Score (Empirical Rule)7мин
Quantitative Data: Boxplots6мин
Demo: Interactive Histogram & Boxplot4мин
Important Python Libraries21мин
Tables, Histograms, Boxplots in Python25мин
2 материала для самостоятельного изучения
What's Going on in This Graph?10мин
Modern Infographics10мин
3 практического упражнения
Practice Quiz: Summarizing Graphs in Words15мин
Assessment: Numerical Summaries10мин
Python Assessment: Univariate Analysis10мин
Неделя
3
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.

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7 видео ((всего 56 мин.)), 3 материалов для самостоятельного изучения, 3 тестов
7 видео
Introduction to Pizza Assignment2мин
Multivariate Data Selection19мин
Multivariate Distributions8мин
Unit Testing5мин
3 материала для самостоятельного изучения
Pitfall: Simpson's Paradox10мин
Modern Ways to Visualize Data10мин
Pizza Study Design Assignment Instructions10мин
2 практического упражнения
Practice Quiz: Multivariate Data10мин
Python Assessment: Multivariate Analysis15мин
Неделя
4
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.

...
15 видео ((всего 223 мин.)), 6 материалов для самостоятельного изучения, 2 тестов
15 видео
Non-Probability Sampling: Part I10мин
Non-Probability Sampling: Part II9мин
Sampling Variance & Sampling Distributions: Part I15мин
Sampling Variance & Sampling Distributions: Part II7мин
Demo: Interactive Sampling Distribution21мин
Beyond Means: Sampling Distributions of Other Common Statistics10мин
Making Population Inference Based on Only One Sample14мин
Inference for Non-Probability Samples17мин
Complex Samples23мин
Sampling from a Biased Population15мин
Randomness and Reproducibility14мин
The Empirical Rule of Distribution18мин
6 материала для самостоятельного изучения
Building on Visualization Concepts5мин
Potential Pitfalls of Non-Probability Sampling: A Case Study10мин
Resource: Seeing Theory10мин
Article: Jerzy Neyman on Population Inference10мин
Preventing Bad/Biased Samples10мин
Course Feedback10мин
2 практического упражнения
Assessment: Distinguishing Between Probability & Non-Probability Samples10мин
Generating Random Data and Samples20мин
4.6
Рецензии: 48Chevron Right

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получил значимые преимущества в карьере благодаря этому курсу

Лучшие отзывы о курсе Understanding and Visualizing Data with Python

автор: FGApr 4th 2019

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.

автор: JSJan 24th 2019

I strongly recommend this course to those who want to begin python programming applied to statistics. It launches a very sound foundation for statistical inference theory.

Преподаватели

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Brenda Gunderson

Lecturer IV and Research Fellow
Department of Statistics
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Brady T. West

Research Associate Professor
Institute for Social Research
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Kerby Shedden

Professor
Department of Statistics

О 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....

О специализации ''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....
Statistics with Python

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