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初級レベル

High school algebra

約21時間で修了

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

英語

字幕:英語, 韓国語

学習内容

  • 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

習得するスキル

StatisticsData AnalysisPython ProgrammingData Visualization (DataViz)

次における3の1コース

100%オンライン

自分のスケジュールですぐに学習を始めてください。

柔軟性のある期限

スケジュールに従って期限をリセットします。

初級レベル

High school algebra

約21時間で修了

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

英語

字幕:英語, 韓国語

シラバス - 本コースの学習内容

1
4時間で修了

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.

...
10件のビデオ (合計110分), 8 readings, 2 quizzes
10件のビデオ
What is Statistics?9 分
Interview: Perspectives on Statistics in Real Life28 分
(Cool Stuff in) Data8 分
Where Do Data Come From?12 分
Variable Types5 分
Study Design6 分
Introduction to Jupyter Notebooks9 分
Data Types in Python12 分
Introduction to Libraries and Data Management13 分
8件の学習用教材
Course Syllabus5 分
Meet the Course Team!10 分
About Our Datasets2 分
Help Us Learn More About You!10 分
Resource: This is Statistics10 分
Course Syllabus5 分
Let's Play with Data!10 分
Data management and manipulation10 分
2の練習問題
Practice Quiz - Variable Types10 分
Assessment: Different Data Types10 分
2
5時間で修了

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件のビデオ (合計92分), 2 readings, 3 quizzes
8件のビデオ
Quantitative Data: Histograms12 分
Quantitative Data: Numerical Summaries9 分
Standard Score (Empirical Rule)7 分
Quantitative Data: Boxplots6 分
Demo: Interactive Histogram & Boxplot4 分
Important Python Libraries21 分
Tables, Histograms, Boxplots in Python25 分
2件の学習用教材
What's Going on in This Graph?10 分
Modern Infographics10 分
3の練習問題
Practice Quiz: Summarizing Graphs in Words15 分
Assessment: Numerical Summaries10 分
Python Assessment: Univariate Analysis10 分
3
5時間で修了

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.

...
7件のビデオ (合計56分), 3 readings, 3 quizzes
7件のビデオ
Looking at Associations with Multivariate Quantitative Data7 分
Demo: Interactive Scatterplot2 分
Introduction to Pizza Assignment2 分
Multivariate Data Selection19 分
Multivariate Distributions8 分
Unit Testing5 分
3件の学習用教材
Pitfall: Simpson's Paradox10 分
Modern Ways to Visualize Data10 分
Pizza Study Design Assignment Instructions10 分
2の練習問題
Practice Quiz: Multivariate Data10 分
Python Assessment: Multivariate Analysis15 分
4
6時間で修了

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件のビデオ (合計223分), 6 readings, 2 quizzes
15件のビデオ
Probability Sampling: Part I10 分
Probability Sampling: Part II15 分
Non-Probability Sampling: Part I10 分
Non-Probability Sampling: Part II9 分
Sampling Variance & Sampling Distributions: Part I15 分
Sampling Variance & Sampling Distributions: Part II7 分
Demo: Interactive Sampling Distribution21 分
Beyond Means: Sampling Distributions of Other Common Statistics10 分
Making Population Inference Based on Only One Sample14 分
Inference for Non-Probability Samples17 分
Complex Samples23 分
Sampling from a Biased Population15 分
Randomness and Reproducibility14 分
The Empirical Rule of Distribution18 分
6件の学習用教材
Building on Visualization Concepts5 分
Potential Pitfalls of Non-Probability Sampling: A Case Study10 分
Resource: Seeing Theory10 分
Article: Jerzy Neyman on Population Inference10 分
Preventing Bad/Biased Samples10 分
Course Feedback10 分
2の練習問題
Assessment: Distinguishing Between Probability & Non-Probability Samples10 分
Generating Random Data and Samples20 分
4.6
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Understanding and Visualizing Data with Python からの人気レビュー

by 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.

by 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.

講師

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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

ミシガン大学(University of Michigan)について

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....

Statistics with Pythonの専門講座について

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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