Acerca de este Curso
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100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel principiante

High school algebra

Aprox. 23 horas para completar

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

Inglés (English)

Subtítulos: Inglés (English), Coreano

Qué aprenderás

  • 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

Habilidades que obtendrás

StatisticsData AnalysisPython ProgrammingData Visualization (DataViz)

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel principiante

High school algebra

Aprox. 23 horas para completar

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

Inglés (English)

Subtítulos: Inglés (English), Coreano

Programa - Qué aprenderás en este curso

Semana
1
5 horas para completar

WEEK 1 - INTRODUCTION TO DATA

10 videos (Total 110 minutos), 8 lecturas, 2 cuestionarios
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
8 lecturas
Course Syllabus5m
Meet the Course Team!10m
About Our Datasets2m
Help Us Learn More About You!10m
Resource: This is Statistics10m
Course Syllabus5m
Let's Play with Data!10m
Data management and manipulation10m
2 ejercicios de práctica
Practice Quiz - Variable Types30m
Assessment: Different Data Types10m
Semana
2
5 horas para completar

WEEK 2 - UNIVARIATE DATA

8 videos (Total 92 minutos), 2 lecturas, 3 cuestionarios
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 lecturas
What's Going on in This Graph?10m
Modern Infographics10m
3 ejercicios de práctica
Practice Quiz: Summarizing Graphs in Words15m
Assessment: Numerical Summaries10m
Python Assessment: Univariate Analysis10m
Semana
3
5 horas para completar

WEEK 3 - MULTIVARIATE DATA

7 videos (Total 56 minutos), 3 lecturas, 3 cuestionarios
7 videos
Looking at Associations with Multivariate Quantitative Data7m
Demo: Interactive Scatterplot2m
Introduction to Pizza Assignment2m
Multivariate Data Selection19m
Multivariate Distributions8m
Unit Testing5m
3 lecturas
Pitfall: Simpson's Paradox10m
Modern Ways to Visualize Data10m
Pizza Study Design Assignment Instructions10m
2 ejercicios de práctica
Practice Quiz: Multivariate Data10m
Python Assessment: Multivariate Analysis15m
Semana
4
6 horas para completar

WEEK 4 - POPULATIONS AND SAMPLES

15 videos (Total 223 minutos), 6 lecturas, 2 cuestionarios
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 lecturas
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 ejercicios de práctica
Assessment: Distinguishing Between Probability & Non-Probability Samples10m
Generating Random Data and Samples20m
4.6
79 revisionesChevron Right

25%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

Principales revisiones sobre Understanding and Visualizing Data with Python

por 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.

por 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.

Instructores

Avatar

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

Acerca de Universidad de Míchigan

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....

Acerca de Programa especializado 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

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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