À propos de ce cours

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100 % en ligne
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Niveau débutant

High school algebra

Approx. 20 heures pour terminer
Anglais
Sous-titres : Anglais, Coréen

Ce que vous allez apprendre

  • 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

Compétences que vous acquerrez

StatisticsData AnalysisPython ProgrammingData Visualization (DataViz)

Résultats de carrière des étudiants

25%

ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau débutant

High school algebra

Approx. 20 heures pour terminer
Anglais
Sous-titres : Anglais, Coréen

Offert par

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Université du Michigan

Programme du cours : ce que vous apprendrez dans ce cours

Évaluation du contenuThumbs Up94%(6,508 notes)Info
Semaine
1

Semaine 1

5 heures pour terminer

WEEK 1 - INTRODUCTION TO DATA

5 heures pour terminer
11 vidéos (Total 114 min), 7 lectures, 2 quiz
11 vidéos
Understanding and Visualizing Data Guidelines3 min
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
7 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
2 exercices pour s'entraîner
Practice Quiz - Variable Types30 min
Assessment: Different Data Types10 min
Semaine
2

Semaine 2

5 heures pour terminer

WEEK 2 - UNIVARIATE DATA

5 heures pour terminer
8 vidéos (Total 92 min), 2 lectures, 3 quiz
8 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
2 lectures
What's Going on in This Graph?10 min
Modern Infographics10 min
3 exercices pour s'entraîner
Practice Quiz: Summarizing Graphs in Words15 min
Assessment: Numerical Summaries10 min
Python Assessment: Univariate Analysis10 min
Semaine
3

Semaine 3

5 heures pour terminer

WEEK 3 - MULTIVARIATE DATA

5 heures pour terminer
7 vidéos (Total 56 min), 3 lectures, 3 quiz
7 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
3 lectures
Pitfall: Simpson's Paradox10 min
Modern Ways to Visualize Data10 min
Pizza Study Design Assignment Instructions10 min
2 exercices pour s'entraîner
Practice Quiz: Multivariate Data10 min
Python Assessment: Multivariate Analysis15 min
Semaine
4

Semaine 4

6 heures pour terminer

WEEK 4 - POPULATIONS AND SAMPLES

6 heures pour terminer
15 vidéos (Total 223 min), 7 lectures, 2 quiz
15 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
7 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
Optional: Deeper Dive Reference10 min
Course Feedback10 min
2 exercices pour s'entraîner
Assessment: Distinguishing Between Probability & Non-Probability Samples10 min
Generating Random Data and Samples20 min

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À propos du 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

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