このコースについて

391,529 最近の表示

受講生の就業成果

25%

コースが具体的なキャリアアップにつながった
共有できる証明書
修了時に証明書を取得
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。
次における3の1コース
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20時間で修了
英語
字幕:英語, 韓国語

学習内容

  • 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

習得するスキル

StatisticsData AnalysisPython ProgrammingData Visualization (DataViz)

受講生の就業成果

25%

コースが具体的なキャリアアップにつながった
共有できる証明書
修了時に証明書を取得
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。
次における3の1コース
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20時間で修了
英語
字幕:英語, 韓国語

提供:

ミシガン大学(University of Michigan) ロゴ

ミシガン大学(University of Michigan)

シラバス - 本コースの学習内容

コンテンツの評価Thumbs Up94%(6,534 件の評価)Info
1

1

5時間で修了

WEEK 1 - INTRODUCTION TO DATA

5時間で修了
11件のビデオ (合計114分), 7 readings, 2 quizzes
11件のビデオ
Understanding and Visualizing Data Guidelines3 分
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
7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
Let's Play with Data!10 分
Data management and manipulation10 分
2の練習問題
Practice Quiz - Variable Types30 分
Assessment: Different Data Types10 分
2

2

5時間で修了

WEEK 2 - UNIVARIATE DATA

5時間で修了
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

3

5時間で修了

WEEK 3 - MULTIVARIATE DATA

5時間で修了
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

4

6時間で修了

WEEK 4 - POPULATIONS AND SAMPLES

6時間で修了
15件のビデオ (合計223分), 7 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
7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
Optional: Deeper Dive Reference10 分
Course Feedback10 分
2の練習問題
Assessment: Distinguishing Between Probability & Non-Probability Samples10 分
Generating Random Data and Samples20 分

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Statistics with Python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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