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.
提供：
Statistics with Python の専門講座ミシガン大学（University of Michigan）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）
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.
シラバス - 本コースの学習内容
5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.
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
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.
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
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.
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
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.
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
レビュー
UNDERSTANDING AND VISUALIZING DATA WITH PYTHON からの人気レビュー
ATにより2020年5月21日
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.
MCにより2020年4月19日
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.
DTにより2020年5月27日
This course is very good for the people who are not from programming background as everything related to the concepts is very well explained (with programming support) throughout the course
MRにより2020年6月2日
Never have I come across a course half as interactive as this and it was a much needed confidence booster for a beginner like me. I look forward to completing the specialization : )
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.
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When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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