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.
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課程信息
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學生職業成果
25%
通過此課程獲得實實在在的工作福利
您將學到的內容有
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
您將獲得的技能
StatisticsData AnalysisPython ProgrammingData Visualization (DataViz)
學生職業成果
25%
通過此課程獲得實實在在的工作福利
100% 在線
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第 1 門課程（共 3 門）
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初級
High school algebra
完成時間大約為15 小時
建議：4 weeks of study, 4-6 hours/week
英語（English）
字幕：英語（English）, 韓語
教學大綱 - 您將從這門課程中學到什麼
完成時間為 5 小時
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.
完成時間為 5 小時
11 個視頻 （總計 114 分鐘）, 8 個閱讀材料, 2 個測驗
11 個視頻
Understanding and Visualizing Data Guidelines3分鐘
What is Statistics?9分鐘
Interview: Perspectives on Statistics in Real Life28分鐘
(Cool Stuff in) Data8分鐘
Where Do Data Come From?12分鐘
Variable Types5分鐘
Study Design6分鐘
Introduction to Jupyter Notebooks9分鐘
Data Types in Python12分鐘
Introduction to Libraries and Data Management13分鐘
8 個閱讀材料
Course Syllabus5分鐘
Meet the Course Team!10分鐘
About Our Datasets2分鐘
Help Us Learn More About You!10分鐘
Resource: This is Statistics10分鐘
Course Syllabus5分鐘
Let's Play with Data!10分鐘
Data management and manipulation10分鐘
2 個練習
Practice Quiz - Variable Types30分鐘
Assessment: Different Data Types10分鐘
完成時間為 5 小時
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.
完成時間為 5 小時
8 個視頻 （總計 92 分鐘）, 2 個閱讀材料, 3 個測驗
8 個視頻
Quantitative Data: Histograms12分鐘
Quantitative Data: Numerical Summaries9分鐘
Standard Score (Empirical Rule)7分鐘
Quantitative Data: Boxplots6分鐘
Demo: Interactive Histogram & Boxplot4分鐘
Important Python Libraries21分鐘
Tables, Histograms, Boxplots in Python25分鐘
2 個閱讀材料
What's Going on in This Graph?10分鐘
Modern Infographics10分鐘
3 個練習
Practice Quiz: Summarizing Graphs in Words15分鐘
Assessment: Numerical Summaries10分鐘
Python Assessment: Univariate Analysis10分鐘
完成時間為 5 小時
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.
完成時間為 5 小時
7 個視頻 （總計 56 分鐘）, 3 個閱讀材料, 3 個測驗
7 個視頻
Looking at Associations with Multivariate Quantitative Data7分鐘
Demo: Interactive Scatterplot2分鐘
Introduction to Pizza Assignment2分鐘
Multivariate Data Selection19分鐘
Multivariate Distributions8分鐘
Unit Testing5分鐘
3 個閱讀材料
Pitfall: Simpson's Paradox10分鐘
Modern Ways to Visualize Data10分鐘
Pizza Study Design Assignment Instructions10分鐘
2 個練習
Practice Quiz: Multivariate Data10分鐘
Python Assessment: Multivariate Analysis15分鐘
完成時間為 6 小時
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.
完成時間為 6 小時
15 個視頻 （總計 223 分鐘）, 6 個閱讀材料, 2 個測驗
15 個視頻
Probability Sampling: Part I10分鐘
Probability Sampling: Part II15分鐘
Non-Probability Sampling: Part I10分鐘
Non-Probability Sampling: Part II9分鐘
Sampling Variance & Sampling Distributions: Part I15分鐘
Sampling Variance & Sampling Distributions: Part II7分鐘
Demo: Interactive Sampling Distribution21分鐘
Beyond Means: Sampling Distributions of Other Common Statistics10分鐘
Making Population Inference Based on Only One Sample14分鐘
Inference for Non-Probability Samples17分鐘
Complex Samples23分鐘
Sampling from a Biased Population15分鐘
Randomness and Reproducibility14分鐘
The Empirical Rule of Distribution18分鐘
6 個閱讀材料
Building on Visualization Concepts5分鐘
Potential Pitfalls of Non-Probability Sampling: A Case Study10分鐘
Resource: Seeing Theory10分鐘
Article: Jerzy Neyman on Population Inference10分鐘
Preventing Bad/Biased Samples10分鐘
Course Feedback10分鐘
2 個練習
Assessment: Distinguishing Between Probability & Non-Probability Samples10分鐘
Generating Random Data and Samples20分鐘
審閱
4.7
來自UNDERSTANDING AND VISUALIZING DATA WITH PYTHON的熱門評論
由 SB 提供Oct 10, 2019
Really enjoyed this course. Looking forward to the next part of the specialization. I thought the quality of the lectures was excellent and made the topic interesting and digestible
由 FG 提供Apr 3, 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.
由 JS 提供Jan 23, 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.
由 NM 提供Apr 18, 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
由 AM 提供Feb 29, 2020
The course is very good & it gives a confidence to people like me who are new to Python .\n\nIt would be great If you can explain the hypothesis section in more detail .
由 RR 提供Feb 20, 2019
Very good course instructors ! Excellent balance of basics of statistics and python programming oriented towards data analysis. Thoroughly enjoyed the course material.
由 SP 提供Apr 10, 2019
Really helpful to build a foundation for the basic Python and improve the understanding on basic but key concepts on statistics and visualizing techniques. Awesome!
由 DW 提供Apr 13, 2019
I love the U of M courses! I get so much out of them. Thank you again for helping me to advance my knowledge of Python and deepen my understanding of statistics.
由 CC 提供Jan 30, 2020
This course is really a general overview. The focus seems to be laying a foundation which I hope we build on in a more technical way in upcoming stats courses.
由 SM 提供Feb 12, 2020
It's a very good course to learn statistics for data analysis and data science. The instructors were great and the information they gave was just precious.
由 MM 提供Nov 10, 2019
Pretty good introduction for using Python for statistics. Some of the lectures were a little dry, and the test material could have been more challenging.
由 NS 提供Dec 26, 2019
Great course to review basic statistical concepts. The added component of python was a practical way to learn python and apply it to real situations.
由 SK 提供Feb 12, 2020
Python exercises can be more interactive and the examples for sampling could be explained by taking a small data set for getting a realistic idea
由 M 提供Jan 6, 2020
some of the materials in this course was not very clear but I think there will be more explanation in the upcoming courses in this specialization
由 BS 提供May 28, 2019
Great class for beginners. Would have loved to learn the material in smaller python notebook snippets throughout. Instruction was excellent!
由 BC 提供Dec 30, 2018
This course is definitely a beginner level course in both python and stats, but it is very well done, and there is plenty of content.
由 NB 提供Dec 16, 2019
Now feeling a bit more confident about statistics. My anxiety in statistics is fading away. Thanks a ton for this amazing course.
由 MG 提供Aug 24, 2019
It was an excellent course, the explanations were very clear and the examples given really helped to ilustrate the concepts.
由 RK 提供Apr 15, 2019
This is a must. If you want to be a data scientist then this course is like a stepping stone. Trust me its totally worth it
由 FP 提供Nov 14, 2019
Very high level introduction to pandas and visualization. It is a good resource for finding different pandas functions.
關於 密歇根大学
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.
關於 Statistics with Python 專項課程
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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