Über diesen Kurs
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Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

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Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Stufe „Anfänger“

High school algebra

Ca. 21 Stunden zum Abschließen

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

Englisch

Untertitel: Englisch, Koreanisch

Was Sie lernen werden

  • 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

Kompetenzen, die Sie erwerben

StatisticsData AnalysisPython ProgrammingData Visualization (DataViz)

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Stufe „Anfänger“

High school algebra

Ca. 21 Stunden zum Abschließen

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

Englisch

Untertitel: Englisch, Koreanisch

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1
4 Stunden zum Abschließen

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 Videos (Gesamt 110 min), 7 Lektüren, 2 Quiz
10 Videos
What is Statistics?9m
Interview: Perspectives on Statistics in Real Life28m
(Cool Stuff in) Data8m
Where Do Data Come From?12m
Variable Types5m
Study Design6m
Introduction to Jupyter Notebooks9m
Data Types in Python12m
Introduction to Libraries and Data Management13m
7 Lektüren
Course Syllabus5m
Meet the Course Team!10m
About Our Datasets2m
Help Us Learn More About You!10m
Resource: This is Statistics10m
Let's Play with Data!10m
Data management and manipulation10m
2 praktische Übungen
Practice Quiz - Variable Types10m
Assessment: Different Data Types10m
Woche
2
5 Stunden zum Abschließen

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 Videos (Gesamt 92 min), 2 Lektüren, 3 Quiz
8 Videos
Quantitative Data: Histograms12m
Quantitative Data: Numerical Summaries9m
Standard Score (Empirical Rule)7m
Quantitative Data: Boxplots6m
Demo: Interactive Histogram & Boxplot4m
Important Python Libraries21m
Tables, Histograms, Boxplots in Python25m
2 Lektüren
What's Going on in This Graph?10m
Modern Infographics10m
3 praktische Übungen
Practice Quiz: Summarizing Graphs in Words15m
Assessment: Numerical Summaries10m
Python Assessment: Univariate Analysis10m
Woche
3
5 Stunden zum Abschließen

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 Videos (Gesamt 56 min), 3 Lektüren, 3 Quiz
7 Videos
Looking at Associations with Multivariate Quantitative Data7m
Demo: Interactive Scatterplot2m
Introduction to Pizza Assignment2m
Multivariate Data Selection19m
Multivariate Distributions8m
Unit Testing5m
3 Lektüren
Pitfall: Simpson's Paradox10m
Modern Ways to Visualize Data10m
Pizza Study Design Assignment Instructions10m
2 praktische Übungen
Practice Quiz: Multivariate Data10m
Python Assessment: Multivariate Analysis15m
Woche
4
6 Stunden zum Abschließen

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 Videos (Gesamt 223 min), 6 Lektüren, 2 Quiz
15 Videos
Probability Sampling: Part I10m
Probability Sampling: Part II15m
Non-Probability Sampling: Part I10m
Non-Probability Sampling: Part II9m
Sampling Variance & Sampling Distributions: Part I15m
Sampling Variance & Sampling Distributions: Part II7m
Demo: Interactive Sampling Distribution21m
Beyond Means: Sampling Distributions of Other Common Statistics10m
Making Population Inference Based on Only One Sample14m
Inference for Non-Probability Samples17m
Complex Samples23m
Sampling from a Biased Population15m
Randomness and Reproducibility14m
The Empirical Rule of Distribution18m
6 Lektüren
Building on Visualization Concepts5m
Potential Pitfalls of Non-Probability Sampling: A Case Study10m
Resource: Seeing Theory10m
Article: Jerzy Neyman on Population Inference10m
Preventing Bad/Biased Samples10m
Course Feedback10m
2 praktische Übungen
Assessment: Distinguishing Between Probability & Non-Probability Samples10m
Generating Random Data and Samples20m
4.6
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ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs

Top-Bewertungen

von 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.

von 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.

Dozenten

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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

Über 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....

Über die Spezialisierung Statistics with Python

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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