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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Карьерные результаты учащихся
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получил значимые преимущества в карьере благодаря этому курсу
33%
стал больше зарабатывать или получил повышение
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Курс 1 из 3 в программе
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Начальный уровень
High school algebra
Прибл. 20 часов на выполнение
Английский
Субтитры: Арабский, Французский, Португальский (Европа), Китайский (упрощенное письмо), Итальянский, Вьетнамский, Корейский, Немецкий, Русский, Турецкий, 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
StatisticsData AnalysisPython ProgrammingData Visualization (DataViz)
Карьерные результаты учащихся
31%
получил значимые преимущества в карьере благодаря этому курсу
33%
стал больше зарабатывать или получил повышение
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
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Начальный уровень
High school algebra
Прибл. 20 часов на выполнение
Английский
Субтитры: Арабский, Французский, Португальский (Европа), Китайский (упрощенное письмо), Итальянский, Вьетнамский, Корейский, Немецкий, Русский, Турецкий, 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.)), 7 материалов для самостоятельного изучения, 2 тестов
11 видео
Understanding and Visualizing Data Guidelines3мин
What is Statistics?9мин
Interview: Perspectives on Statistics in Real Life28мин
(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
7 материалов для самостоятельного изучения
Course Syllabus5мин
Meet the Course Team!10мин
About Our Datasets2мин
Help Us Learn More About You!10мин
Resource: This is Statistics10мин
Let's Play with Data!10мин
Data management and manipulation10мин
2 практических упражнения
Practice Quiz - Variable Types30мин
Assessment: Different Data Types10мин
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
8 видео
Quantitative Data: Histograms12мин
Quantitative Data: Numerical Summaries9мин
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 практических упражнения
Practice Quiz: Summarizing Graphs in Words15мин
Assessment: Numerical Summaries10мин
Python Assessment: Univariate Analysis10мин
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
7 видео
Looking at Associations with Multivariate Quantitative Data7мин
Demo: Interactive Scatterplot2мин
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 практических упражнения
Practice Quiz: Multivariate Data10мин
Python Assessment: Multivariate Analysis15мин
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
15 видео
Probability Sampling: Part I10мин
Probability Sampling: Part II15мин
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
8 материалов 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
Optional: Deeper Dive Reference10мин
Course Feedback10мин
Keep Learning with Michigan Online10мин
2 практических упражнения
Assessment: Distinguishing Between Probability & Non-Probability Samples10мин
Generating Random Data and Samples20мин
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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