À propos de ce cours
4.5
40 notes
13 avis
Spécialisation
100 % en ligne

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Niveau débutant

Niveau débutant

High school algebra

Heures pour terminer

Approx. 19 heures pour terminer

Recommandé : 4 weeks of study, 4-6 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais

Ce que vous allez apprendre

  • 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

Compétences que vous acquerrez

StatisticsData AnalysisPython ProgrammingData Visualization (DataViz)
Spécialisation
100 % en ligne

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Niveau débutant

Niveau débutant

High school algebra

Heures pour terminer

Approx. 19 heures pour terminer

Recommandé : 4 weeks of study, 4-6 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
Heures pour terminer
4 heures pour terminer

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....
Reading
10 vidéos (Total 110 min), 7 lectures, 2 quiz
Video10 vidéos
What is Statistics?9 min
Interview: Perspectives on Statistics in Real Life28 min
(Cool Stuff in) Data8 min
Where Do Data Come From?12 min
Variable Types5 min
Study Design6 min
Introduction to Jupyter Notebooks9 min
Data Types in Python12 min
Introduction to Libraries and Data Management13 min
Reading7 lectures
Course Syllabus5 min
Meet the Course Team!10 min
About Our Datasets2 min
Help Us Learn More About You!10 min
Resource: This is Statistics10 min
Let's Play with Data!10 min
Data management and manipulation10 min
Quiz2 exercices pour s'entraîner
Practice Quiz - Variable Types10 min
Assessment: Different Data Types10 min
Semaine
2
Heures pour terminer
5 heures pour terminer

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....
Reading
8 vidéos (Total 92 min), 2 lectures, 3 quiz
Video8 vidéos
Quantitative Data: Histograms12 min
Quantitative Data: Numerical Summaries9 min
Standard Score (Empirical Rule)7 min
Quantitative Data: Boxplots6 min
Demo: Interactive Histogram & Boxplot4 min
Important Python Libraries21 min
Tables, Histograms, Boxplots in Python25 min
Reading2 lectures
What's Going on in This Graph?10 min
Modern Infographics10 min
Quiz3 exercices pour s'entraîner
Practice Quiz: Summarizing Graphs in Words15 min
Assessment: Numerical Summaries10 min
Python Assessment: Univariate Analysis10 min
Semaine
3
Heures pour terminer
5 heures pour terminer

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....
Reading
7 vidéos (Total 56 min), 3 lectures, 3 quiz
Video7 vidéos
Looking at Associations with Multivariate Quantitative Data7 min
Demo: Interactive Scatterplot2 min
Introduction to Pizza Assignment2 min
Multivariate Data Selection19 min
Multivariate Distributions8 min
Unit Testing5 min
Reading3 lectures
Pitfall: Simpson's Paradox10 min
Modern Ways to Visualize Data10 min
Pizza Study Design Assignment Instructions10 min
Quiz2 exercices pour s'entraîner
Practice Quiz: Multivariate Data10 min
Python Assessment: Multivariate Analysis15 min
Semaine
4
Heures pour terminer
6 heures pour terminer

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. ...
Reading
15 vidéos (Total 223 min), 6 lectures, 2 quiz
Video15 vidéos
Probability Sampling: Part I10 min
Probability Sampling: Part II15 min
Non-Probability Sampling: Part I10 min
Non-Probability Sampling: Part II9 min
Sampling Variance & Sampling Distributions: Part I15 min
Sampling Variance & Sampling Distributions: Part II7 min
Demo: Interactive Sampling Distribution21 min
Beyond Means: Sampling Distributions of Other Common Statistics10 min
Making Population Inference Based on Only One Sample14 min
Inference for Non-Probability Samples17 min
Complex Samples23 min
Sampling from a Biased Population15 min
Randomness and Reproducibility14 min
The Empirical Rule of Distribution18 min
Reading6 lectures
Building on Visualization Concepts5 min
Potential Pitfalls of Non-Probability Sampling: A Case Study10 min
Resource: Seeing Theory10 min
Article: Jerzy Neyman on Population Inference10 min
Preventing Bad/Biased Samples10 min
Course Feedback10 min
Quiz2 exercices pour s'entraîner
Assessment: Distinguishing Between Probability & Non-Probability Samples10 min
Generating Random Data and Samples20 min
4.5
13 avisChevron Right

Meilleurs avis

par 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.

par JSFeb 6th 2019

Really enjoyed the different (yet all wonderful) teaching styles of the large instructor team!

Enseignants

Avatar

Brenda Gunderson

Lecturer IV and Research Fellow
Department of Statistics
Avatar

Brady T. West

Research Associate Professor
Institute for Social Research
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Kerby Shedden

Professor
Department of Statistics

À propos de Université du 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....

À propos de la Spécialisation 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

Foire Aux Questions

  • Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.

  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

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